Prerpints.org logo
Preprint
Article

Assessment of the Potential to use the Expelled Heat Energy from a Typical Data Centre in Ireland for Alternative Farming Methods

Altmetrics

Downloads

241

Views

182

Comments

0

A peer-reviewed article of this preprint also exists.

This version is not peer-reviewed

Submitted:

17 April 2023

Posted:

18 April 2023

You are already at the latest version

Alerts
Abstract
Data centres, though a necessary part of modern society, are being stigmatised for consuming vast amounts of electricity for their operational and cooling needs. Due to Ireland’s reliance on fossil fuels to meet the increased energy demand of data centres, the data centres are contributing significantly to Ireland’s total carbon emissions. As much of this energy is expelled from data centres as waste heat energy, the potential for recycling some of this wasted heat energy was explored using environmentally friendly systems from recent publications. The recovered waste heat energy was applied in a vertical farming system, and the benefits of this waste heat to the vertical farm were analysed and quantified in two scenarios. Using conservative estimates, it was predicted that each vertical farm could be between 5-23% the size of the data centre and produce enough food to feed between 14-61 adults their daily calorie needs, and between 13-58 people their daily fresh produce requirements, depending on the scenario applied. For a more accurate prediction, each vertical farm would have to be assessed on a case-by-case basis. However, there was not enough data available on Irish data centres to perform these calculations.
Keywords: 
Subject: Environmental and Earth Sciences  -   Sustainable Science and Technology

1. Introduction

Data centres have become an integral part of modern society enabling fast communications for e-mail and social media, the storage of public and networked data remotely, and robust networking to have almost instant access to any of this data from an internet/intranet connection [1]. A data centre can vary in size depending on the amount of data that it has to store or transfer, from micro data centres (1-100 kW) that can be portable and used for environmental or construction projects [2], to hyper-scale data centres (100+ MW) that could maintain telecommunications of entire countries [3]. The ever-increasing use of server-dependant technologies like smartphones [4], online gaming [5] and media streaming [6] is increasing the demand for data centres. This is also accelerated by increased use of data, the continuous need for faster download speeds [7], and higher resolution images that increase the volume of data that needs to be processed and transferred [8].
Data centres are estimated to consume 3% of the global electricity supply and are predicted to consume more than 20% by 2025 [9]. Compared to pre-COVID-19 lockdown levels, internet services have increased 40-80% [10], further increasing their energy demands. At least 40% of this energy is dedicated to cooling the servers [11,12], making the cooling systems of data centres accountable for 1.6% of global greenhouse gas emissions [9]. Many countries are falling short on their carbon emission targets [13]. If the world keeps increasing its dependency on data centres without further consideration of the energy use, and energy source, there could be significant ramifications for the total greenhouse gas emissions.
Water is often used in cooling data centres, and there is a general lack of transparency on water usage, with less than a third of data centres measuring water consumption [14]. Google has been steadily increasing their water usage by an average of ~19.7% year-on-year since 2018 to 6.3 billion gallons in 2021, though it has reduced its rate of increase to ~10.5% in 2020 & 2021 [15,16]. Microsoft has also had a steady rate of increase in water usage, increasing by an average of ~10.4% year-on-year since 2018 to 7.6 billion litres of water in 2021, though they have managed to reduce their rate of increased water usage significantly more to an average of 1% in 2020 and 2021 [17].
Ireland’s data centres currently draw more energy than the entire country’s rural dwellings combined [18], and concerns about energy security and their environmental impact are causing a growing stigma about the sustainability of their energy demand and the associated greenhouse gas emissions [19,20]. In 2021 data centres required 11% of Ireland’s total annual energy consumption (3,019 GWh); this is expected to increase to 29% by 2028 [21,22], resulting in over 300Mt of CO2eq annually from the cooling systems alone [23,24]. Even though Ireland has continuously failed to reach emission targets [25,26,27], there is an increase in the number of data centres being constructed with Dublin becoming the largest data centre hub in Europe and the operation of Dublin’s data centres currently contributing significantly (1.9%) to Ireland’s total carbon emissions [28].
The global electricity demand grew by 6% in 2021 with coal being used to meet more than half of this extra demand. As a result, the global CO2 emissions from electricity rose by 7% [29]. Due to this increased global demand for energy, and increased energy insecurity from the Russo-Ukrainian war [30], there has been a drastic increase in the price of electricity and non-renewable energy sources [31]. The increase in fuel prices is one of the factors driving global inflation, which in turn is affecting the cost of food and food security [32]. Prior to the Russian invasion, Ukraine was a key exporter producing: 16% of the maize, 10% of the barley, 9% of the wheat, and 42% of the sunflower oil for the global market [33]. With other factors like soil degradation reducing yields, climate change-induced weather events destroying crops, and the economic consequences of the COVID-19 pandemic, there are more than 193 million people in 53 countries at crisis levels of food insecurity [34,35].
Approximately 38% of the global land surface is dedicated to agriculture, and about one-third of this is used as cropland with the rest for grazing livestock [36]. Though meat and associated products (milk, eggs etc.) are calorie-dense, and a good source of High Biological Value (HBV) protein and minerals like calcium and iron [37], meat only provides 11% of the global food energy [38]. A study by Ritchie and Roser [39] investigated the environmental impact of food production and found that meat requires significantly more resources than vegetables or grains, resulting in a greater environmental impact of production. To produce 1000 kilocalories of food, beef requires 119.49 m2 land area, peas require 2.16 m2, and maize requires 0.65 m2; for 100 g of protein, beef requires 163.6 m2 land area, peas require 3.4 m2, and grains require 4.6 m2; resulting in large amounts of CO2eq being released as beef produces 99.48 kg CO2eq per kilogram of product compared with peas producing 0.98 kg and maize producing 1.7 kg.
A study by Abbade [40] concluded that the world’s food production is sufficient to meet the world population’s nutritional demands, but there is much waste in supply chain efficiency, and the end users waste up to 30% of food purchased [41]. The centralisation of food production is more cost-effective [42], but it increases dependency on logistics and requires a more efficient and comprehensive supply chain [43]. A breakdown in the supply chain can have repercussions for the global market, like the blockage of the Suez Canal that impacted 12% of global trade [44], the delays caused by the breakdown in logistics affecting China’s distribution [45], or the effects of political decisions and national emergencies like Brexit and COVID-19 [46].
Many innovations in farming and food production that have increased production yields and improved post-harvest quality, such as implementing new crop rotation methods that increase the sustainability, and profitability of soybean production [47]. Other innovations include the experimentation of breeding technologies to develop safe-to-eat genetic variations of lettuce with improved post-harvest quality [48]. However, further research is still needed into drought-tolerant varieties, pest and disease resistance, and reducing the environmental impact of production [49]. Droughts were found to statistically significantly and negatively impact household nutrition due to the effect on crop yields; their frequency and severity are expected to increase worldwide in the coming years [50,51]. Pests and pathogens were accountable for 4.84-16.29% of the wheat loss in China between 2000 and 2018 [52]. Increased global temperatures are facilitating the growth and reproduction of insects, and this increased pest density is causing additional crop damage [53]. Alternative innovative methods of food production are being developed to reduce its environmental impact; such as the development of meat alternatives [54], the use of microalgae as animal feed and water purification/waste management [55], or the use of biological agents like tadpoles, fish, ducks, geese and pigs as weed control instead of chemical herbicides [56].
Vertical farming is one method of food production growing in popularity for domestic and commercial food production [57]. The global vertical farming market is expected to increase by an average of 23.86% year on year to a value of $20 billion by 2026 [58]. The world’s largest vertical farm has recently opened in Dubai with more than 300,000 m2 of production space with the capacity to produce one million kgs of leafy greens annually for Emirates Flight Catering [59]. There are many advantages to this farming method: the plants are arranged to support high crop yield production per unit area, enabling annual crop cultivation with less space, which is less labour intensive [60]; the absence of soil and indoor controlled environments reduce water loss through drainage and evaporation resulting in hydroponic methods using as little as 8% of the water compared to conventional methods [61], this also drastically reduce the risk of diseases or pests damaging the crop eliminating the use of pesticides and herbicides in vertical farm facilities [62]; and the decentralisation of food production would reduce emissions from transportation of food while increasing access to fresh produce [63]. There is however a major disadvantage to vertical farms in that their increased energy consumption which use on average 38.8 kWh per kg (139.7 MJ/kg) of produce compared to unheated greenhouses which use on average 5.4 kWh per kg (19.4 MJ/kg) [64].
Depending on the crop being grown, the air conditioning system uses 18-23% of a vertical farm’s total energy in temperate climates [65]. However, fluctuations in the temperature (23-34˚ C) of the external environment can increase this energy demand by up to 50% [66]. The average annual temperature of the Dublin region over the past three years is 10.1˚ C [67]. This is one of the reasons why Dublin is home to 25% of all data centres in Europe, the relatively cool climate reduces the workload and cost of running the air conditioning units [68]. Most of the electricity consumed in Information Technology (IT) installations is converted into waste heat, forming a large and stable low-temperature heat source [69]. Much research has been conducted into novel methods of utilising the wasted heat energy from data centres for heating homes and office space [70], but not much research into using the waste heat energy in a vertical farm setup. It has been shown that an increase in ambient temperature increases the growth rate of plants by up to 100% [71,72], so some of the low-grade heat energy can be utilised by a vertical farm to increase yields. This paper attempts to determine if there could be a potential symbiotic relationship in energy usage between data centres and vertical farms and quantify any reduction in overall energy consumption and carbon emissions.

2. Materials and Methods

2.1. Research methodology

This research will be a data-driven feasibility analysis to determine if it is possible to reduce the environmental impact of a data centre by recycling the waste heat produced to supply energy to a vertical farm. This paper will consult available data on Irish data centres and supplement any gaps in the literature with data from data centres operated in similar climate regions to determine a range for how much waste heat energy is produced. There will be considerations to energy losses through waste heat transportation. These losses will be quantified to determine the preferred method of heat transfer to maintain the maximum amount of heat energy available to a vertical farm. The magnitude of heat energy available will influence the size of the vertical farm and the types of produce that can be grown in the facility, the geographical location of the data centre will also be a factor to consider in the calculations. Using public data on the environmental impact and cost of energy production in Ireland, the advantages, and disadvantages of combining a data centre and a vertical farm will be explored to determine if the proposed system is feasible.

2.2. Data collection and analysis

Data on the energy use of Irish data centres was compiled from public online data resources [21,73,74].This data was found to be insufficient, so it was supplemented with comparable data from London data centres [75]. The quantification of waste heat recovery rates could not be determined from current research available on Irish data centres, therefore figures provided by [9] were used. The variations in weather conditions used in calculations were provided by the Irish Meteorological Service [67,76]. There was very little research into the energy requirements of a vertical farm in Ireland as the first large-scale vertical farm only started producing in 2021 [77], so data from a manufacturer of vertical farms [65] was used. Electric Ireland and the Environmental Protection Agency provided the data used to calculate the financial and environmental costs of electricity production in Ireland [24,78].

2.3. Determining the energy usage of Irish data centres

The available data on energy use in Irish data centres [73,74] was compiled in a spreadsheet. However, the only figures available were the size of the data centres (ft2) and the power consumption (MW) of the whole building, as much of the data was incomplete from DataCenters and Baxtel with only 33.3% and 45.7% of the respective sources having data on both size and power consumption of data centres in Ireland. To obtain more data, London data centres were considered as the climate is relatively comparable to Ireland, though an average of ~5.65˚ C warmer [79].Therefore the cooling systems of data centres will likely require slightly more energy in London than in Ireland. There was a slightly higher level of reporting (52.83%) in London data centres [75], hence this data will be used to assist in research but will be kept separate for calculations. The unit used to compare the data centres will be power used per area of data centre (kW.m-2).

2.4. Quantification of usable wasted heat energy produced in data centres

Research by X. Wang et al. [80], stated that the average total energy distribution of a data centre is: IT equipment (44%), cooling (40%), transportation and distribution losses (7%), site power system losses (6%), and miscellaneous (lighting, security etc.) (3%). Up to 90% of a data centre’s electricity is converted to low grade waste heat [81]. To calculate the viable waste heat, the miscellaneous energy use and the transportation losses will not be considered as they are unlikely to expel their waste heat in the server room with the heat recovery system. Similarly, the cooling system is unlikely to add recoverable heat to the system. Therefore, the maximum potential waste heat will be calculated based on the IT equipment and the site power system loss, assuming 90% of this energy is converted into recoverable heat. The average rate of waste heat energy ( Q w ˙ ) produced by a data was calculated using these estimates.
Q w ˙ = 0.9 ( Q ˙ ) × 0.44 + 0.06 = 0.45 ( Q ˙ )
The amount of this energy that can be recovered varies from system to system with up to 68% of the waste heat being recoverable when the servers are submerged in a dielectric coolant [82]. The value of 55% recoverable energy using in a district heating water-side economiser system [9], was chosen to calculate the maximum usable waste heat energy per second ( Q w u ˙ ) as it was considered to be the most efficient viable system to adopt.
Q w u ˙ = 0.55 ( Q w ˙ ) = 0.2475 ( Q ˙ )

2.5. Energy requirements of a vertical farm

Data from producers of vertical farms [65] was tabulated to determine the energy breakdown for each piece of necessary equipment used in a vertical farm. All electric powered devices will produce some waste heat energy, though LED lights are much more efficient than other light sources, they still emit between 70-80% of their energy usage as waste heat [83]. This will be assumed to be a constant 75% for ease of calculations. The desired temperature of the vertical farm is 20˚ C [65], but due to the amount of waste heat available from the data centre and the LED lights, plants more suited to a warmer climate will also be considered [84].

2.6. Feasibility analysis of combining the two systems

There are many factors to consider when retrofitting a vertical farm to a data centre and each of these variables will likely be different for every data centre. Without available data, it is impossible to design an ‘off the shelf’ solution that will suit all data centre configurations. This paper will assume that there is space adjacent to the data centre in order to retrofit the vertical farm or that the vertical farm and data centre was being constructed simultaneously. This study will focus on the energy balance between a data centre and a vertical farm to determine the ideal size of a vertical farm based on the available amount of waste heat generated and crops produced. The potential system will be analysed in two separate scenarios:

2.7. Scenario 1 – Integrated system

The data centre and vertical farm are one combined system. The waste heat is pumped directly into the vertical farm, where it is circulated. The air expelled from the data centre is dry and warm [85]. It will be used to heat the vertical farm and assist in the transpiration of the plants [86], and this cooler, more humid air is passed back to the CRAH unit to complete the cycle.

2.8. Scenario 2 – Heat exchange system

The data centre and vertical farm are two separate buildings adjacent to each other. The waste heat energy is transferred from the data centre to the vertical farm through heat exchangers as described by C. Zhang et al. [87]. There is no air mixing between the data centre and the vertical farm in this system, but there is some heat energy lost through a heat exchanger. The volumetric flow rate of the air circulating in the data centre (Figure 1. [top]), will depend on the quantity of waste heat energy a data centre produces. This in turn, will affect the flow rate of the ambient Irish air (Figure 1. [bottom]), to provide the vertical farm with its desired operating conditions.
, to the ambient air ( θ a v ) , to supply it with the heat to cater for the vertical farm ( θ v f ) , the cooled exhaust energy is recycled into the data centre ( θ i ) .   V ˙ D C and V ˙ v f represent the volumetric flow rate of the air in the data centre and vertical farm respectively.

2.9. Quantification of energy savings

The waste energy from the data centre will offset some of the energy requirements of a vertical farm by providing a stable climate and eliminating the need for a dedicated a/c system, humidifiers, and dehumidifiers. The energy savings will be determined using the ideal size of the vertical farm based on the available waste heat energy in each scenario.

2.10. Potential effect on food security and healthy eating of locality

Using the ideal size of the vertical farm from each scenario and available data on the yields of different food products grown in vertical farms compared to a similar area of arable land [88], the calorific potential of the vertical farms will be determined. Using the healthy eating guidelines [37], it will then be determined how many people (2,000 calories or seven portions a day) can be nourished with the vertical farm versus the same area of land using traditional farming methods.

2.11. Potential environmental and economic benefits

The energy saving implications of each scenario will incur economic savings through a reduced energy bill and a reduction in greenhouse gas emissions through this energy saving and the fuel saving in the transportation of goods. These reductions will be calculated using the average values from the Irish grid [24,78,89]. Food miles can account for up to a fifth of total food system emissions [90]. Using recent data on emission factors for transporting food [91], the average reduction in carbon emissions were tabulated to estimate the carbon emissions.

3. Results

3.1. Determining the energy usage of reported Irish data centres

Many variations were observed (Table 1.) in the size (621-86,000 m2), power consumption (0.46-108 MW), and power consumption per unit area of the data centres in Ireland (0.2-3 kW.m-2). Microsoft Dub 07 (part of Microsoft Dublin Grange Castle data centres) was found to correlate the closest to the average values from reported data.
Although there was also a lot of variation (Table 2.) in the size (809-84,542 m2), power consumption (0.5-29 MW), and power consumption per unit area of the data centres in London (0.05-10 kW.m-2), the average size of London data centres tended to be smaller than Irish data centres (9,576 vs. 17,734 m2). On average they consumed less power (7.66 vs 23.71 MW) but have overall higher average levels of power consumption per unit area (1.49 vs. 1.32 kW.m-2).

3.2. Quantification of usable wasted heat energy produced in data centres

The amount of recoverable waste energy being produced was calculated (Equation 2.) based on the heat recovery system (Figure 2.) described by [9], and available data from Irish and London-based data centres.
On average, Irish data centres produce more waste heat energy (5.87 MJ) than London-based data centres (1.9 MJ). However, when their areas are considered, there is more waste energy produced per square meter on average in London data centres (369.68 J.m-2) than in Ireland (326.75 J.m-2). The potentially recoverable minimum & maximum usable waste heat energy from Irish data centres and usable waste heat energy per square meter for Irish data centres were 0.11-26.73 MJ and 50.64-742.51 J.m-2 respectively.

3.3. Energy requirements of a vertical farm

It was found (Figure 3.) that the energy demand for different plants fluctuates depending on the species and whether it produces a flower or fruit. Energy demand per annum: Romaine lettuce 783 kWh.m-2 (2.81 GJ.m-2); Rocket 630 kWh.m-2 (2.26 GJ.m-2); Strawberries 1,405 kWh.m-2 (5.06 GJ.m-2). The LED lights require the most energy (55.2-66%), with more light energy required by strawberries to produce the fruit. Between 18.2% & 22% of the total energy demand comes from the air conditioning (AC) unit, and between 10.2% and 22.5% of us used to power dehumidifiers. The operating temperature of the vertical farm was assumed to be 20˚C.

3.4. Effect of waste heat on plant growth

Data centres are advised to operate in the range of 18-27˚ C, but are permitted to be as high as 32˚ C [92]. Many foods (Table 3.) can grow comfortably above the temperatures of a data centre, and some plants have increased growth rates at these temperatures.
The vertical farm has the potential to utilise some, or all of the waste heat from a data centre to optimise the potential growth rates in the vertical farm, but without causing thermal harm to the plants. However, there are many variables to consider such as the amount of waste heat recoverable from a data centre, the load of the data centre, the size of the vertical farm facility, its ambient temperature, and the type of crops being grown.

3.5. Feasibility analysis of combining the two systems

Though some rural Irish data centres, like that in Clonee have ample space around the data centre to construct a vertical farm, many inner-city data centres, like in Finglas are surrounded by streets or buildings. This paper will not consider the size constraints in each individual scenario and assume that the vertical farm can be built directly adjacent to the data centres for the scenario analysis. For all scenarios, it was assumed that the exhaust air ( θ e ) of the data centre is between 25-35˚ C [101]; the inlet temperature ( θ i ) of the data centre is between 18-23˚ C [102]; and the ideal operating conditions of the data centre is 21.5˚ C (45.5% RH) [103]. The external climate conditions assessed will be the average Irish temperature ( θ a v ) of 10.1˚C and London average of 15.8˚ C [67,76,79].The ideal conditions of the vertical farm ( θ v f ) will vary depending on the variety of plant being grown, most plants are comfortable at 20˚ C, but different plants will be considered in each scenario.
According to Naranjani et al. [104], a horizontal air speed of 0.3-0.5 m.s-1 boosts photosynthesis, and 0.4 m.s-1 will be used as the desired air speed of the vertical farm ( v ˙ i d ) , in order to calculate its ideal area ( A i d ) . The psychrometric properties of all the conditions to be analysed are summarised in Table 4. The energy of the system will be analysed using psychometric charts and systems as recommended by Callahan et al. [105].

3.6. Scenario 1 – Integrated system

As the waste heat air is pumped directly into the vertical farm, there is no heat energy lost from using a heat exchanger. The waste heat energy and its flow rate can vary depending on the size and energy use of the data centre. The CRAH system was assumed to have an air intake temperature of 20.5˚ C and an exhaust temperature of 30˚ C. The mass flow rate of the exhaust air was calculated according to Equation 3.
m ˙ = Q w ˙ h e − h i
Though many plants can comfortably grow at the average data centre exhaust air temperature, it is too hot for the average vertical farm and must be mixed with some external air to have the temperature at 20˚ C. The mixing ratio is calculated using the mass-energy balance of the flow rate and the airflows’ respective entropies according to Equation 4.
m e ˙ h e + m ˙ a v h a v = m ˙ v f h v f b u t   m ˙ v f = m ˙ e + m ˙ a v
∴ m ˙ a v = m e ˙ h e − h v f h v f − h a v
It was found (Equation 5.) that the volumetric flow rate of air into the vertical farm ranged between 31.8 – 7,467.1 m3.s-1 (average 1,639.34 m3.s-1); for every square meter of data centre the volumetric flow rate range was between 0.014 – 0.207 m3.s-1.m-2data centre (average 0.091 m3.s-1.m-2data centre). For every m3 of data centre exhaust air, on average 0.7895 m3 of air from the external environment is required to achieve the ideal temperature for the vertical farm.
V ˙ v f = Q w . V i d h e − h i 1 + h e − h v f h v f − h a v
Therefore, the ideal size of a vertical farm (Equation 6.) ranged between 80-18,667 m2 (average 4,098.4 m2). Assuming that the data centre and the vertical farm have the same height, the area of a vertical farm should be on average 23% the size of a data centre (range of 3-52%) to utilise the waste heat effectively.
A i d = V ˙ v f v ˙ i d
The range of values for Irish and London based data centres are presented in Table 5 & Table 6, showing their calculated air flow rates and the resulting area of the vertical farm that could be built to utilise the waste heat energies in each scenario.
As the vertical farm also produces some waste heat from the lights and other electrical equipment, the air will be heated further before being recirculated into the data centre’s CRAH unit. The plants in the vertical farm will contribute a slight evaporative cooling effect from the transpiration of water through the leaves. According to Qingjuan et al. [106], the maximum cooling effect of transpiration is 2.4˚ C/1000m3, causing a humidity increase of 6.43%. The waste energy provided by the electrical equipment in the vertical farm (Table 7.) ranges from 124 kW/1000 m3 (rocket) to 218 kW/1000 m3 (strawberries), assuming that the air mixing eliminates the need for humidity and heating control, the average value of 94.418 kW/1000 m3 was used to calculate the heating effect of the vertical farm’s electronics.
The cooling effect of transpiration ranged from 101-23,668 kW (average 5,196.17 kW) (Equation 7.), and the heating effect from the waste heat produced by a vertical farm’s electronics ranged from 7.5-1,762.6 kW (average 387 kW). When combined, it was found (Equation 8.) that the net negative energy of the system produced a vertical farm exhaust temperature ( θ v f e ) , of 15.2˚ C. The range of values for the energy savings of vertical farms built adjacent to Irish and London-based data centres are presented in Table 8.
Q ˙ t = V ˙ v f h v v f − h v t Q ˙ w v f = A i d ( 94.418   W . m − 2 )  
θ v f e = θ v f + Q ˙ w v f − Q ˙ t V ˙ v f × C v a i r
The above calculations assumed average temperatures for the air exhausted from the data centre (30˚ C), keeping all other temperature parameters constant. If the exhaust was operating at its upper limit (35˚ C), the vertical farm would be, on average, 19.27% smaller for Irish data centres and 9.42% smaller for London’s data centres; if operating at its lower limit (25˚ C), it would increase the size of the vertical farm by 66.6% in Irish data centres and by 32.5% in London’s data centres.

3.7. Scenario 2 – Heat exchange system

The waste heat energy from the vertical farm is transferred through a heat exchanger and loses some energy in the process. The usable waste heat energy ( Q w u ) is diverted to the vertical farm, which is assumed to be built adjacent to the data centre to prevent further heat loss. The usable heat energy (Table 9.) will then be transferred to the air from the external environment to create the ideal vertical farm conditions. The volumetric flow rate of the air in the vertical farm (Equation 9.) varied between 7.29-1,712.68 m3.s-1 (average 376.01 m3.s-1); for every square meter of the data centre, the volumetric flow rate range was between 0.003 – 0.048 m3.s-1.m-2data centre (average 0.021 m3.s-1.m-2data centre).
V ˙ v f = V v f ( Q ˙ w u h v f − h a v )
In this scenario, the ideal size of a vertical farm ranged between 18.2-4,281.7 m2 (average 940 m2), and the ideal area of the vertical farm would be 5% of the size of the data centre (range 0.8-11.9%) – a full breakdown of Irish and London data centres in Table 9 & Table 10.

3.8. Quantification of energy savings

Using the energy requirements of the vertical farm (Table 7.), it was determined that every square meter of the vertical farm would require, on average, 58.7 W less energy if the energy demands of the vertical farm can be compensated by the waste heat of a data centre. Using the data from Irish data centres in Scenario 1, it was determined (Table 8.) that the calculated ideal-sized vertical farm would save between 4.7-1,095.8 kW (average 240.6 kW) by utilising the data centres’ waste heat. In Scenario 2, the energy savings (Table 9.) were between 1.1-251.3 kW (average 55.2 kW) for Irish data centres.

3.9. Potential effect on food security and healthy eating of locality

From the sample of foods analysed (Table 12.), the vertical farm produces on average 7.23 kilograms of produce per square meter of growing space, with tomatoes having the greatest mass of yield. However, as potatoes are very calorie dense, they would be able to feed more than twice the people with the same growing area (52,350 kcal.m-2).
Using the average values (Table 11.), the ideal vertical farm from Scenario 1 could provide enough calories to feed between 1-280 people (average 61) their recommended 2,000 kcal per day and provide between 1-264 people, (average 58) people with all seven portions of fruit and vegetables. Using the same area of vertical farm as arable land, the land area would feed an average of 19 people their daily calories and an average of 22 people their fruit and vegetables. A complete list of values for Ireland and London are presented in Table 12 & Table 13.
Table 11. Comparison of crop yields per annum of different food products grown in a vertical farm versus traditional farming methods [37,88].
Table 11. Comparison of crop yields per annum of different food products grown in a vertical farm versus traditional farming methods [37,88].
Crop Vertical farm Yield (tons/ha) Field Yield (tons/ha) Vertical farm yield (kg.m-2) Field yield (kg.m-2) Energy of food (kcal/kg) Energy produced in vertical farm (kcal.m-2) Energy produced in field (kcal.m-2) Portions per m2 of vertical farm Portions per m2 of field
Radish 23 15 2.3 1.5 490 1127 735 11.5 7.5
Spinach 22 12 2.2 1.2 690 1518 828 11 6
Lettuce 37 25 3.7 2.5 480 1776 1200 18.5 12.5
Peas 9 6 0.9 0.6 3440 3096 2064 4.5 3
Cabbage 67 50 6.7 5 1080 7236 5400 33.5 25
Carrots 58 30 5.8 3 1250 7250 3750 29 15
Strawberries 69 30 6.9 3 1260 8694 3780 34.5 15
Peppers 133 30 13.3 3 890 11837 2670 66.5 15
Tomatoes 155 45 15.5 4.5 940 14570 4230 77.5 22.5
Potatoes 150 28 15 2.8 3490 52350 9772 75 14
Average 72.3 27.1 7.23 2.71 1401 10945.4 3442.9 36.15 13.55
Table 12. The number of calories and portions of fruit or vegetables a vertical farm can provide compared to the same land area using traditional farming methods for Irish data centres in Scenario 1. Full data available in Table S6.
Table 12. The number of calories and portions of fruit or vegetables a vertical farm can provide compared to the same land area using traditional farming methods for Irish data centres in Scenario 1. Full data available in Table S6.
Code People who can obtain daily calories (Vertical Farm) People who can obtain daily calories (Field) People who can obtain 7 portions of fruit or veg (Vertical Farm) People who can obtain 7 portions of fruit or veg (Field)
Min 1 0 1 0
Max 280 88 264 99
IAV 61 19 58 22
Table 13. The number of calories and portions of fruit or vegetables a vertical farm can provide compared to the same land area using traditional farming methods for London data centres in Scenario 1. Full data available in Table S7.
Table 13. The number of calories and portions of fruit or vegetables a vertical farm can provide compared to the same land area using traditional farming methods for London data centres in Scenario 1. Full data available in Table S7.
Code People who can obtain daily calories (Vertical Farm) People who can obtain daily calories (Field) People who can obtain 7 portions of fruit or veg (Vertical Farm) People who can obtain 7 portions of fruit or veg (Field)
Min 3 1 3 1
Max 171 54 161 60
LAV 45 14 43 16
In Scenario 2, the ideal vertical farm could provide between 0-64 people (average 14) with their daily calorie intake and 0-61 people (average 13) with their recommended fruit and vegetables. The same area of arable land could produce on average enough food to provide the calories to sustain four people and provide the fruit and vegetables for five people per day. A complete list of values for Ireland and London are presented in Table 14 & Table 15.

3.10. Potential environmental and economic benefits

Current data [89] shows that the cost of electricity in Ireland during the day-time and night-time are €0.3344/kWh (€92.88/GJ) and €0.165/kWh (€45.83/GJ) respectively. Assuming a constant energy demand, the total annual energy cost is €2,374.44/kWannum. According to the Environmental Protection Agency [78], there was an increase in coal and oil as a fuel source for the Irish electricity grid in 2021, causing an increase in the emission intensity of power generation in 2021 (331 g CO2eq/kWh) compared to 2020 (296 g CO2eq/kWh). The average of these values (2,748 kg CO2eq/kWannum) will be used for calculations.
In Scenario 1 the energy savings of the ideal vertical farm ranged between €11,082-2,601,895/annum (average €571,227); the range of carbon emissions offset by the system was 12.8-3,011.3 tonnes of CO2eq per annum (average 661.1 tonnes of CO2eq per annum).
In Scenario 2 the energy savings of the ideal vertical farm ranged between €2,542-596,781/annum (average €131,019); the range of carbon emissions offset by the system was 2.9-660.7 tonnes of CO2eq per annum (average 151.6 tonnes of CO2eq per annum).
Depending on whether the food product requires ambient or cooled conditions there is a different energy demand and associated carbon emissions. Assuming that there is 100km of distance from farm to port and from port to shop, the average carbon impact of importing a tonne of product was assessed (Table 16). Using the average values, the carbon emission per tonne of product imported were calculated to be 190 kg CO2e/tonne. Using the expected yields of the vertical farms, the annual carbon emission reductions by the proposed system ranged from 109-25,655 kg CO2eq (average 5,632 kg CO2e) in Scenario 1 and 106 - 6,128 kg CO2eq (average 1,619 kg CO2e) in Scenario 2 for Irish data centres (Table 17). The data shows that London data centres (Table 18.) are better suited to Scenario 2 and Irish data centres are better suited to Scenario 1.

4. Discussion

4.1. Summary of key findings

This study has shown that there is a lack of data on Irish data centres. There is little transparency regarding the accuracy of the available data and the comparability of data noted by many researchers investigating the energy use of data centres [14,108,109]. Less than half of Irish data centres have readily available information; since writing this paper, some of the information is no longer freely available [73], further decreasing the accessibility to data. As a result other recent research into the energy use of Irish data centres [110], cooling techniques applied in Irish data centres [111], and the sustainability of cooling methods in Irish data centres [112], had to make many assumptions regarding the energy uses, operating temperatures and applied technologies of Irish data centres. The data centre market is ever-growing and changing with a further three data centres approved for construction in Ireland (August 2022), one in Ennis [113], and two in Dublin [114]. These newer data centres are likely going to be more energy efficient than older data centres in compliance with energy and emission targets set by the countries they are being built in [115], and the companies that operate them [116,117]. Therefore, direct comparisons between all data centres impossible without considerations to the specifications of the cooling systems implemented in each system and their respective efficiencies.
Many variables are involved in determining a data centre’s waste heat. This paper has shown that all data centres produce waste heat energy that can be recovered to some degree. However, the accurate quantification of this energy must be assessed on a case-by-case basis. Conservative estimates and average values from the literature were used when required, causing the values obtained for the ideal vertical farm size and the associated energy and environmental savings, to be likely underestimated. This paper has calculated the average ideal size of a vertical farm to incur maximum savings in energy based on available data in two separate scenarios. The proposed systems can be implemented to supplement the energy of a non-ideal sized vertical farm and still reduce the overall energy demand. There is also little research into the optimisation of operating a vertical farm in Ireland, as Ireland’s first commercial vertical farm only started producing in 2021. It was built by retrofitting an existing mushroom farm into a hydroponic farm [118], and, therefore, may not be optimally designed for maximum yields and resource usage. However, after less than a year of operation, they are expanding their capacity by 20% [77], implying that there is a market gap for fresh produce from a vertical farm in Ireland and that the business model is profitable in Ireland even with the relatively high set up costs and energy costs of operation. By building a vertical farm adjacent to a data centre this paper has shown that there are guaranteed energy savings and carbon emission reductions, when compared to a stand-alone vertical farming system.
The research and calculations of this paper focused on creating the optimum operating conditions (20˚ C) of a vertical farm [65]. To create these conditions, some of the heat energy was lost through heat exchangers or the temperature was decreased by mixing the exhaust air with air from the external environment to maintain these conditions. However, research has shown (Table 3.) that many food-producing plants can grow in a wide range of temperatures (18-35˚ C). Suppose a data centre has more or less space for a vertical farm than suggested by calculations in the scenario analysis, or the average waste energy values are different than reported figures from primary research. By being creative with the selection of produce, the vertical farm could support a range of plants that can utilise most, if not all, of the available waste heat energy from a data centre.
The temperature of the external environment is one of the key variables that drive the energy demand of the cooling system in a data centre [119]. By comparing the total energy consumption per unit area of Irish versus London based data centres (Tables 1 & 2), we can see that London data centres on average use 12.8% more energy per unit area. This is at least partially due to the increased temperature of the ambient environment, but the relatively small size and energy use of London data centres versus Irish data centres, 46% and 67% respectively, and the accuracy of reporting, could also play a role in the discrepancy. Though this extra heat could support larger vertical farms in London versus Ireland in both scenarios compared to the size of the data centre provided, due to the scale of Irish data centres, larger vertical farms could be built in Ireland.
The waste heat energy provided by the data centre has been shown to eliminate the need for a dedicated heating system in the vertical farm. There was an average energy saving of 36.6% versus a vertical farm relying entirely on its own climate control system. The energy costs associated with operating a vertical farm being a commonly referenced risk associated with the profitability of a vertical farm [120,121]. The direct impact on energy costs incurred by utilising the waste heat of a data centre would therefore help make vertical farms a more profitable enterprise. These energy savings will also directly impact the carbon emissions produced by the vertical farm. In 2021, the agriculture sector was directly responsible for 37.5% of Ireland’s total greenhouse gas emissions [122]. The Irish government has set a target of a 25% reduction in agricultural emissions by 2030 [115]. Vertical farming has already been shown to emit up to 70% less carbon emissions than traditional agricultural methods [123]. By reducing the energy needs of a vertical farm using the waste heat of a data centre, these carbon emissions can be further reduced, which would help Ireland reach its 2030 target.
Currently, the only produce grown in a vertical farm in Ireland are salad leaves and herbs such as basil [77]. By utilising the waste heat energy from a data centre in a vertical farm, it would be possible to grow some of the produce which cannot be grown or have low yields in the Irish climate, like rice [99], or cherry tomatoes [94]. Using the average temperatures provided by the data centre, each Irish vertical farm could produce enough food to feed an average of 61 people per day their daily calorie needs or an average of 58 people their recommended seven portions of fruit and vegetables.
The amount of waste energy an average data centre uses was quantified based on available data and recent research. The amount of this energy available to a vertical farm was calculated using two different scenarios to further investigate the proposed system’s feasibility. The energy, and subsequently the environmental and economic savings were quantified in both scenarios. Both systems had quantifiable benefits to the communities surrounding the data centre by providing fresh produce and reducing carbon emissions from transportation. Although both scenarios incurred savings, initial results indicate that Scenario 1 is the better system to implement in Ireland. However, both systems have advantages and disadvantages that must be considered before determining which system is better to implement in a specific data centre.

4.2. Scenario 1

The waste heat energy from the data centre was mixed with air from the external environment to achieve the ideal temperatures of the vertical farm. The flow rate of this air was used to determine the ideal size of the vertical farm. Based on the size of the vertical farm the energy savings that could be incurred were calculated assuming the absence of a dedicated air handling system. The effect of transpiration cooled the air low enough to be circulated back into the data centre, though some air must be vented to decrease the airflow speed to its original specifications. A summary of the main findings is presented in Table 19.
Due to the average temperature of London (15.8˚ C) being closer to the average temperature of a vertical farm (20˚ C) than Ireland (10.1˚ C), less energy was required to increase the ambient air to the ideal conditions of a vertical farm in London. As a result, the system applied to London data centres could support a vertical farm more than twice the size of one in Ireland. However, due to the scale of Irish data centres, there would be greater savings in energy and more people could be fed and nourished with more significant environmental benefits if the model was introduced in Ireland.

4.3. Scenario 2

The waste heat energy from the data centre was transferred to the vertical farm using a water-based heat exchanger. The heat energy from the data centre was cooled from the average exhaust (30˚ C) to the average inlet temperature (20.5˚ C) to be re-circulated back into the data centre. Using the heat exchanger, this energy was transferred to air from the external environment to produce the ideal conditions of a vertical farm. The ideal size of the vertical farm was determined by the amount of heat that could be transferred to supply external air with the conditions necessary for the vertical farm. The energy saving was calculated using the same means in Scenario 1. A summary of the main findings is presented in Table 20.
As in Scenario 1, the increased temperature of London caused the size of the vertical farms to be larger in London than in Ireland. However, the energy lost in the heat exchange system also caused the London vertical farms to be more efficient than Irish vertical farms in maintaining temperatures that are closer to the ambient conditions. A consequence of this is that the system would be able to incur greater energy savings, sustain more people, and have a greater benefit to the environment if constructed in London rather than Ireland.

4.4. Scenario Comparison

Both scenarios utilised the waste heat of the data centres to supplement the growth of plants in a vertical farm. However, each scenario needs to be compared further to distinguish which vertical farming system would better suit Irish data centres. The main difference between the two scenarios is that Scenario 1 mixes air from the external environment into the air of the data centre–vertical farm system while Scenario 2 contains all the air in the data centre and transfers its energy via a heat exchanger. Data centres are primarily designed to circulate air and transfer the waste heat through a heat exchanger in a closed system [80], similar to Scenario 2. However, the data has shown (Table 21.) that a heat exchange system can drastically decrease the energy available to the vertical farm, reducing the potential size of the vertical farm. This in turn would reduce the food production capacity and diminish the potential energy-savings that could be achieved, and hence, reduce the cost savings and the carbon emission reductions of the system when compared to the results from Scenario 1.
Though the energy savings in Scenario 1 are greater, the introduction of air from the external environment poses a risk to data centres [85], and would likely require additional filtration steps [124] before the air can be passed back into the data centre. In order to determine which scenario is best suited to a particular data centre there are many other factors to consider that could further influence the decision. If a data centre has no land area directly adjacent to the building, or the data centre is operating in strict sterile conditions, then the heat exchange system in Scenario 2 might be the better solution. If the data centre has ample space but its CRAH systems at separate sides of the building then the system in Scenario 1 might be the better solution as the vertical farm is more efficient it can be easily split to two separate growing sites to fully utilise the waste heat, while some of this energy would be lost the transportation of this energy in Scenario 2.

4.5. Implications

The research has shown that the data centre market is growing rapidly [125], with three further data centres proposed in Ireland since writing this paper [113,114]. Although the research topic is popular, 383,308 papers were published in 2021, and 324,639 papers were published between January and August of 2022 relating to data centres [126]. Many of these papers are forced to make assumptions surrounding the energy use of data centres due to the secrecy of data centre’s energy usage or lack of reporting [14,109,112], causing the values produced in many of the papers surrounding data centres to have unknown margins of error. The ever-expanding data centre market and the lack of transparency surrounding the energy usage have hindered research and innovation in the area [127]. Even though some research has estimated energy use and carbon emissions [128], it is the author’s opinion that the problem cannot truly be tackled unless data centres are forced to become publicly researchable.
Data centres currently consume more than 3% of the global electricity supply, and are predicted to consume more than 20% by 2025 [9]. The associated carbon emissions are currently responsible for 3.7% of global emissions [129]. Without energy-saving methods being implemented, this figure will also rise. Data centre’s high energy use and environmental impact continuously cause objections to their construction [113,130,131]. Many global companies, such as Amazon and Google, use carbon offsets to claim their companies are operating renewably [132]. However, the use of purchased carbon offsets is causing more environmental harm than good resulting in more emissions being released [133]. There is a lack of accountability for bought carbon offsets, as many of the forests the companies selling carbon offsets claim to protect or plant were never at risk, or do not get planted [134]. The proposed methods in this paper would utilise the waste energy of the data centres to reduce their energy demand and provide emission reductions instead of carbon offset to reduce the company’s environmental impact, while tangibly benefitting the communities of the surrounding areas through accessibility to fresh produce. Increasing the relationship with the communities surrounding the data centre would likely benefit the stigma associated with their construction and energy demand, provided there is transparency throughout the process [135]. Even if the relationship with the public didn’t improve, the public would still benefit from the nutritional value of the fresh produce [136], and the data centres would be truly tackling their environmental impact.
Research has shown that vertical farm is more beneficial to the environment than traditional farming methods by reducing the area required for production [137], increasing yields of produce through efficient layouts that protect the crop from harsh environmental conditions [138], by reducing the water consumption [139], and by optimising nutrient delivery [66]. Though the Irish climate is beneficial to cooling a data centre, it hinders heating a vertical farm due to the ever-increasing cost of energy [140,141]. The increased energy required to heat a vertical farm in Irish conditions could be a deterrent for the construction of commercial vertical farms in Ireland. Climate control is a vertical farm’s second highest energy demand after the lighting [65], Using the proposed models will require less electricity to control the facility’s climate and, hence, a further reduction in environmental impact of vertical farming versus traditional farming methods.
If all the data centres that had reported data in Ireland were to adopt the proposed method in Scenario 1 there would be enough food produced to feed 1,966 adults daily, diverting 180 tonnes of CO2eq from Ireland’s annual emissions by the transport of this food alone. There would be up to 0.021M tonnes of CO2eq from Ireland’s annual agricultural emissions, reducing the total annual agricultural emissions by 0.09%, contributing to 0.36% of Ireland’s 2030 agricultural emission reduction targets. Ireland’s agricultural emissions rose by 3% in 2021 versus 2020 [142]. If the Irish government, and the companies building data centres plan on taking their environmental commitments seriously [115,116,117], then resources must be allocated to further experiment in ways of increasing the sustainability of Irish agriculture and reducing the environmental impact of data centres. This paper has shown the theoretical energy and environmental savings of using a data centre’s waste heat in a vertical farm, but the model must be tested in a real-life application to quantify these savings better. It is the author’s opinion that Ireland has the resources to design a hybrid vertical farm-data centre system. Designing such a system it could help Ireland lead the way in creating a more environmentally friendly system that could be adopted in areas with energy and food insecurities.

4.6. Further applications

This study serves as a proof-of-concept analysis that there can be energy savings for the vertical farm if it is built adjacent to a data centre. By integrating a vertical farm into a data centre as part of the initial planning process, further optimisations and energy-saving techniques could be implemented [143]. The load of a data centre directly influences the power consumption and hence the waste heat generated in a data centre [144]. The heat energy from the servers with the highest energy demand could be diverted to a smaller vertical farm that produces crops that prefer a warmer climate, while the remaining waste heat energy is diverted to one or more vertical farms of different sizes operating at different temperatures. Similarly, the construction of a vertical farm could have a data centre built into the design to gain extra revenue and help with operating costs. According to Sajid et al. [145], by using block-chain decentralisation workload management for geographically distributed data centres to migrate the workload, there would be a minimum of 46% reduction in time. This would mean that smaller data centres could be located throughout the country to increase the data transfer speeds of the end user while supplementing the heating needs and profitability of a vertical farm.
The proposed systems could be applied to other industries that produce a constant or predictable supply of waste heat, such as the iron and steel industry, which produce waste heat from molten slag or exhaust gasses [146,147,148], or the petrochemical industry that produce waste heat through flue gasses [149,150]. Though the waste heat from these sources is far greater than from a data centre, the energy could be stored and transferred to a vertical farm using the model described in Scenario 2. Some of the energy will be lost as the heat energy is transferred from the industrial site to the vertical farm. However, as long as the energy source is predictable and quantifiable, the ideal vertical farm size should be computable.
There could also be applications closer to the end user. Cruise ships are at sea for 7-10 days at a time [151] and are constantly preparing meals. Baldi et al. [152] determined that a waste heat recovery system could save approximately 22% of the energy on a cruise ship. However, the energy demand varies seasonally (more energy is used in winter) and depending on the ship’s speed, some of the waste heat energy could be diverted to a vertical farm to provide fresh produce for passengers at sea. There has been more recent research into heat recovery methods of cargo ships than cruise ships [153,154], but many of the heat sources are very similar. For cargo ships, the cargo being carried is likely to be more economical than vertical farming; however, there could be a small vertical farm to provide some fresh produce for the workers as these ships are at sea for an average of 40-50 days at a time [155].
Many buildings produce waste heat, such as hotels that need to have a readily available supply of hot water and to have the rooms and public spaces at comfortable temperatures [156], or large kitchens and food production sites that would be producing waste heat from cooking [157,158]. These sources may not produce much waste heat when compared to the petrochemical industry or a constant and predictable heat supply like a data centre, however, these businesses focus on feeding fresh food to their patrons, and the novelty of producing their own ingredients on site could create a symbiotic relationship between the vertical farming business and the catering business supplying the waste heat and using the produce [159].

4.7. Limitations

One of the major limitations of this research is the lack of available and reliable data. Less than half of Irish data centres reported enough data to be included in the study. Therefore, the paper could not fully assess all data centres in Ireland. Though there was unknown reliability surrounding this data, the values were used in calculations due to a lack of alternatives. The energy demands of a data centre change due to weather variations [160], the load of a data centre [144], or the time of day [161]. The operating temperature, and hence the available waste energy would constantly fluctuate. However, there was insufficient data to perform the calculations required to factor in these energy changes in Ireland.
The paper only considered the environmental impacts of electric energy and transport for calculations. Other factors that could have environmental implications were not considered. There would be carbon emissions in both scenarios due to the construction of the vertical farms [162] and the food waste that may incur post-harvest [163]. In Scenario 1, the system uses air from the external environment, which would need a filtration step before being circulated back into the data centre [164]. The filters used cannot be recycled and must be disposed of in landfill [165]. In Scenario 2, the volume of water being pumped would change depending on the size of the data centre and vertical farm. The volume of water that the system needs to pump directly affects the energy demand of the heat exchanger [166], however, the energy of the pump was not considered in the calculations.
The energy loss of the heat exchanger in Scenario 2, was assumed to be a constant value of 45% based on literature [9]. The distance between the vertical farm and the data centre greatly influences this figure. If the data centre was built closer to the vertical farm the transferrable energy would likely be greater [167], increasing the possible size of the vertical farm to utilise this extra energy. All the estimates used were conservative in lieu of real-life data. If the research were conducted using more accurate data from an operational data centre, it would likely yield higher energy transfer values.
Though London’s data centres were considered and yielded positive results for utilising the waste heat of a data centre in a vertical farm in both scenarios, the increased energy consumption of the cooling system of London-based data centres and their environmental impact were not considered in this study. The primary focus of the work undertaken was to assess the viability of the systems in Irish data centres and countries with similar climates. Though the system may be applied in other regions, the application of this research will require an examination of each data centre on a case-by-case basis to maximise the size of the vertical farm that can be built and to maximise the subsequent energy savings and reduction in carbon emissions.
The vertical farm was assumed to operate at a constant temperature. However, the temperature requirements of plants can change throughout the day and night cycles, with a 5-15˚ C difference between the ideal day and night conditions [168]. Plants require more heat during the daytime, and data centres are at their highest load, creating the greatest amount of waste heat during the daytime, and in the summer [169]. However, to use these calculations with the information available from Irish data centres, the already sparce data on Irish data centres would have to be extrapolated to accommodate the temperature ranges, exaggerating any errors in accuracy. A full year of the hourly temperature variations of a data centre would have to be compared to the energy readings of that data centre, ideally monitored at multiple points, to confidently perform these calculations which was deemed beyond the scope of this study.

4.8. Recommendations

Further research is required to determine the daily variations in the temperature and energy use of a data centre in Ireland to design a heat transfer system that can more efficiently fulfil the varying climate needs of a vertical farm. Access to more data will enable more accurate calculations of the ideal size of a vertical farm that can be built onto a particular data centre. It will also help choose the most efficient and economical heat transfer methods to accommodate the current waste heat management system.
This paper has demonstrated proof of concept that a data centre currently operating in Ireland could retrofit one of its facilities to accommodate a vertical farming system and provide data to benefit further research and optimisation of the system. There are currently data centres in development in Ireland, and integrating a vertical farm into the waste heat management system could easily be tested on a small scale to provide staff lunches or as office decoration. Without testing building a hybrid data centre-vertical farm system, the research can only go so far as to determine the possible energy savings. Authors should discuss the results and how they can be interpreted from the perspective of previous studies and of the working hypotheses.
The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted.

5. Conclusions

The aim of this study was to assess the feasibility of incorporating a vertical farm into a data centre’s air conditioning system and to quantify any potential reduction in both energy consumption and CO2eq emissions. Through a thorough review of the literature and quantitative analysis of the available data on Irish data centres, this research concluded that all the data centres in Ireland produce waste heat that can be recovered for use in a vertical farm system. The use of this heat energy was found to decrease the energy demand of the air control systems in vertical farms. For each Irish data centre, a range of energy and carbon emission savings were quantified in both scenarios.
Scenario analysis concluded that the heat transfer method (one with heat loss using a heat exchanger and the other without any heat loss), and the ambient climate conditions, can impact the amount of waste heat that can be recovered, affecting the size of the vertical farm that is possible to build. By comparing the two systems, we can see the average sizes of vertical farms that an Irish data centre can support through waste heat recovery methods are between 940 – 4,100 m2, with larger data centres being able to accommodate larger vertical farms (4,282 – 18,668 m2). In both systems analysed, the size of vertical farms directly related to the energy savings that could be made (average 55-240 kW), with larger vertical farms saving up to 1.1 MW of energy when fully utilising the waste heat energy of a large data centre.
The potential carbon reduction of the system was analysed with respect to the electricity savings incurred versus a stand-alone vertical farm and the fuel saved in the transportation of the produce versus importation in both scenarios. By introducing food production closer to cities it was found that there were average savings of 1.3 - 5.6 tonnes CO2eq/annum per vertical farm from the transport of produce alone. This, however, was dwarfed by the average vertical farm’s potential reduction in carbon emissions based on the electricity savings in each scenario, 151 – 661 tonnes CO2eq/annum. This paper has concluded that there can be substantial carbon reductions by recovering the waste heat of a data centre for use in a vertical farm.
Though the Irish climate benefits the cooling of a data centre, the air is too cold to be used directly in a vertical farm and must be heated to accommodate the plant’s needs. The vertical farms of the proposed system could each feed an average of 14 – 61 people their daily calories and provide 13-58 people their daily portions of fruit and vegetables without any source of heating other than the data centre. The guaranteed energy supply from the data centre would relieve some of the financial burdens of operation, increasing their size, yield, profitability, and likelihood of introduction in Ireland. Vertical farms are not used much in Ireland, their introduction would increase Irish food security and benefit the health of the local communities. This paper has concluded that using waste heat from data centres to supplement the energy needs of a vertical farm is feasible and would be socially, economically, and environmentally beneficial to Ireland.

References

  1. Thornton, G. A Study of the Economic Benefits of Cata Centre Investment in Ireland. Available online: https://www.idaireland.com/newsroom/publications/ida-ireland-economic-benefits-of-data-centre-inves (accessed on 30 January 22).
  2. Bilal, K.; Khalid, O.; Erbad, A.; Khan, S.U. Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers. Computer Networks 2018, 130, 94–120. [Google Scholar] [CrossRef]
  3. Chen, X.; Jiang, S.; Chen, Y.; Zou, Z.; Shen, B.; Lei, Y.; Zhang, D.; Zhang, M.; Gou, H. Energy-saving superconducting power delivery from renewable energy source to a 100-MW-class data center. Applied Energy 2022, 310, 118602. [Google Scholar] [CrossRef]
  4. Kastanaki, E.; Giannis, A. Forecasting quantities of critical raw materials in obsolete feature and smart phones in Greece: A path to circular economy. Journal of Environmental Management 2022, 307, 114566. [Google Scholar] [CrossRef]
  5. Teng, C.-I.; Shiau, W.-L.; Cheng, T.C.E.; Huang, H.-Y. Drawing goals nearer: Using the goal-gradient perspective to increase online game usage. International Journal of Information Management 2022, 66, 102522. [Google Scholar] [CrossRef]
  6. Wongkitrungrueng, A.; Assarut, N. The role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research 2020, 117, 543–556. [Google Scholar] [CrossRef]
  7. Baig, E. Faster download speeds coming soon: New 5G promises to be like having a fiber- optic device 'in your pocket anywhere you go'. USA today (Arlington, Va.), 2017. [Google Scholar]
  8. Cao, J.; Yu, P.; Ma, M.; Gao, W. Fast Authentication and Data Transfer Scheme for Massive NB-IoT Devices in 3GPP 5G Network. IEEE Internet of Things Journal 2019, 6, 1561–1575. [Google Scholar] [CrossRef]
  9. Huang, P.; Copertaro, B.; Zhang, X.; Shen, J.; Löfgren, I.; Rönnelid, M.; Fahlen, J.; Andersson, D.; Svanfeldt, M. A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating. Applied Energy 2020, 258, 114109. [Google Scholar] [CrossRef]
  10. Karimi, L.; Yacuel, L.; Johnson, J.D.; Ashby, J.; Green, M.; Renner, M.; Bergman, A.; Norwood, R.; Hickenbottom, K.L. Water-energy tradeoffs in data centers: A case study in hot-arid climates. Resources, Conservation and Recycling 2022, 181, 106194. [Google Scholar] [CrossRef]
  11. Luo, Y.; Andresen, J.; Clarke, H.; Rajendra, M.; Maroto-Valer, M. A framework for waste heat energy recovery within data centre. Energy Procedia 2019, 158, 3788–3794. [Google Scholar] [CrossRef]
  12. Masanet, E.; Lei, N. How Much Energy Do Data Centres Really Use? Available online:. Available online: https://energyinnovation.org/2020/03/17/how-much-energy-do-data-centers-really-use/ (accessed on 30 January 22).
  13. Mulvaney, K. The world is still falling short of meeting its climate goals. Available online: https://www.nationalgeographic.com/environment/article/the-world-is-still-falling-short-of-meeting-its-climate-goals (accessed on 26 July 22).
  14. Mytton, D. Data centre water consumption. npj Clean Water 2021, 4, 11. [Google Scholar] [CrossRef]
  15. Google. Google Environmental Report 2019; Online, 2019.
  16. Google. Google Environmental Report 2022; Online, 2022.
  17. Microsoft. 2021 Environmental Sustainability Report; Online, 2022.
  18. Short, E. Data centre energy use in Ireland increased 32% last year. Available online: https://www.businesspost.ie/news/data-centre-energy-use-in-ireland-increased-32-last-year/ (accessed on 26/07/22).
  19. Healy, C. ‘We have to think hard about prioritisation’: The environmental impact of Ireland’s data centres. Available online: https://www.thejournal.ie/data-centres-2-5693974-Feb2022/?utm_source=email (accessed on 08/03/22).
  20. Boland, L. Data centres' electricity consumption has more than doubled since 2015. Available online: https://www.thejournal.ie/data-centres-electricity-use-5659789-Jan2022/?utm_source=email (accessed on 29 January 22).
  21. CSO. Data Centres Metered Electricity Consumption 2020. Available online: https://www.cso.ie/en/releasesandpublications/ep/p-dcmec/datacentresmeteredelectricityconsumption2020/keyfindings/ (accessed on 29 January 22).
  22. eirgrid. All-Island Generation Capacity Statement. Available online: https://www.eirgridgroup.com/site-files/library/EirGrid/EirGrid-Group-All-Island-Generation-Capacity-Statement-2019-2028.pdf (accessed on 8 March 22).
  23. SEAL. Renewable energy targets. Available online: https://www.seai.ie/data-and-insights/seai-statistics/key-statistics/renewables/ (accessed on 30 January 22).
  24. ElectricIreland. Electric Ireland fuel mix disclosure label. 2020.
  25. Oireachtas. Committee of Public Accounts (PAC) criticises State’s failure to meet greenhouse gas emissions and renewable energy targets at cost of €50m to taxpayer in 2020. 2022.
  26. EPA. Ireland will not meet its 2020 greenhouse gas emissions reduction targets. Action is needed now to meet 2030 EU targets. 2021.
  27. O’Sullivan, K. Q&A: Why is Ireland failing to meet its environmental targets? The Irish Times 2019.
  28. O’Sullivan, K. Number of operational data centres in Ireland up by quarter, report finds. The Irish Times 2021.
  29. IEA. Electricity Market Report - 22. 2022. 20 January.
  30. Lo, G.D.; Marcelin, I.; Bassène, T.; Sène, B. The Russo-Ukrainian war and financial markets: the role of dependence on Russian commodities. Finance Research Letters 2022, 103194. [Google Scholar] [CrossRef]
  31. Alsaleh, M.; Abdul-Rahim, A.S.; Abdulwakil, M.M. The importance of worldwide governance indicators for transitions toward sustainable bioenergy industry. Journal of Environmental Management 2021, 294, 112960. [Google Scholar] [CrossRef]
  32. Kpodar, K.; Liu, B. The distributional implications of the impact of fuel price increases on inflation. Energy Economics 2022, 108, 105909. [Google Scholar] [CrossRef]
  33. Hegarty, S. How can Ukraine export its harvest to the world? 2022.
  34. FAO. Global Report on Food Crises: acute food insecurity hits new highs. 2022.
  35. Mehrabi, Z.; Delzeit, R.; Ignaciuk, A.; Levers, C.; Braich, G.; Bajaj, K.; Amo-Aidoo, A.; Anderson, W.; Balgah, R.A.; Benton, T.G.; et al. Research priorities for global food security under extreme events. One Earth 2022, 5, 756–766. [Google Scholar] [CrossRef]
  36. FAO. Land use in agriculture by the numbers. Available online: https://www.fao.org/sustainability/news/detail/en/c/1274219/ (accessed on 26 July 22).
  37. FSAI. Healthy Eating Guidelines to Improve Nations Diet. Available online: https://www.fsai.ie/news_centre/press_releases/healthy_eating_guidelines_28012019.html (accessed on 26 July 22).
  38. Smith, N.W.; Fletcher, A.J.; Hill, J.P.; McCabb, W.C. Modeling the Contribution of Meat to Global Nutrient Availability. Frontiers in Nutrition 2022, 9, 766796. [Google Scholar] [CrossRef]
  39. Ritchie, H.; Roser, M. Environmental Impacts of Food Production. 2021.
  40. Abbade, E.B. Estimating the nutritional loss and the feeding potential derived from food losses worldwide. World Development 2020, 134, 105038. [Google Scholar] [CrossRef]
  41. Ananda, J.; Gayana Karunasena, G.; Pearson, D. Identifying interventions to reduce household food waste based on food categories. Food Policy 2022, 111, 102324. [Google Scholar] [CrossRef]
  42. Almena, A.; Fryer, P.J.; Bakalis, S.; Lopez-Quiroga, E. Centralized and distributed food manufacture: A modeling platform for technological, environmental and economic assessment at different production scales. Sustainable Production and Consumption 2019, 19, 181–193. [Google Scholar] [CrossRef]
  43. Trienekens, J.H.; van der Vorst, J.G.A.J.; Verdouw, C.N. Global Food Supply Chains. In Encyclopedia of Agriculture and Food Systems, Van Alfen, N.K., Ed.; Academic Press: Oxford, 2014; pp. 499–517. [Google Scholar]
  44. Russon, M.-A. The cost of the Suez Canal blockage. Available online: https://www.bbc.com/news/business-56559073 (accessed on 27 July 22).
  45. Murray, B.; Koh, A.; Varley, K. Global Supply Chain Crisis Flares Up Again Where It All Began. Available online: https://www.bloomberg.com/news/features/2022-04-25/china-s-covid-crisis-threatens-global-supply-chain-chaos-for-summer-2022 (accessed on 4 August 22).
  46. Garnett, P.; Doherty, B.; Heron, T. Vulnerability of the United Kingdom’s food supply chains exposed by COVID-19. Nature Food 2020, 1, 315–318. [Google Scholar] [CrossRef]
  47. Garbelini, L.G.; Debiasi, H.; Junior, A.A.B.; Franchini, J.C.; Coelho, A.E.; Telles, T.S. Diversified crop rotations increase the yield and economic efficiency of grain production systems. European Journal of Agronomy 2022, 137, 126528. [Google Scholar] [CrossRef]
  48. Damerum, A.; Chapman, M.A.; Taylor, G. Innovative breeding technologies in lettuce for improved post-harvest quality. Postharvest Biology and Technology 2020, 168, 111266. [Google Scholar] [CrossRef]
  49. Caira, S.; Ferranti, P. Innovation for Sustainable Agriculture and Food Production. In Reference Module in Food Science; Elsevier, 2023. [Google Scholar]
  50. Carpena, F. How do droughts impact household food consumption and nutritional intake? A study of rural India. World Development 2019, 122, 349–369. [Google Scholar] [CrossRef]
  51. Kogan, F.; Guo, W.; Yang, W. Drought and food security prediction from NOAA new generation of operational satellites. Geomatics, Natural Hazards and Risk 2019, 10, 651–666. [Google Scholar] [CrossRef]
  52. Zhang, Q.; Men, X.; Hui, C.; Ge, F.; Ouyang, F. Wheat yield losses from pests and pathogens in China. Agriculture, Ecosystems & Environment 2022, 326, 107821. [Google Scholar] [CrossRef]
  53. Tonnang, H.E.Z.; Sokame, B.M.; Abdel-Rahman, E.M.; Dubois, T. Measuring and modelling crop yield losses due to invasive insect pests under climate change. Current Opinion in Insect Science 2022, 50, 100873. [Google Scholar] [CrossRef]
  54. Coucke, N.; Vermeir, I.; Slabbinck, H.; Geuens, M.; Choueiki, Z. How to reduce agri-environmental impacts on ecosystem services: the role of nudging techniques to increase purchase of plant-based meat substitutes. Ecosystem Services 2022, 56, 101444. [Google Scholar] [CrossRef]
  55. Weinrich, R.; Busch, G. Consumer knowledge about protein sources and consumers’ openness to feeding micro-algae and insects to pigs and poultry. Future Foods 2021, 4, 100100. [Google Scholar] [CrossRef]
  56. Fahad, S.; Saud, S.; Akhter, A.; Bajwa, A.A.; Hassan, S.; Battaglia, M.; Adnan, M.; Wahid, F.; Datta, R.; Babur, E.; et al. Bio-based integrated pest management in rice: An agro-ecosystems friendly approach for agricultural sustainability. Journal of the Saudi Society of Agricultural Sciences 2021, 20, 94–102. [Google Scholar] [CrossRef]
  57. Vangorp, K. Vertical farming gaining popularity among traditional growers. Available online: https://www.hortidaily.com/article/9308753/vertical-farming-gaining-popularity-among-traditional-growers/ (accessed on 27 July 22).
  58. Shahbandeh, M. Global vertical farming market projection 2019 & 2025. 2022.
  59. Hall, C. Crop One, Emirate open ‘world’s largest vertical farm’ in Dubai. Available online: https://techcrunch.com/2022/07/19/crop-one-emirate-worlds-largest-vertical-farm-in-dubai/?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAI_R6DGpihYDG9N5-D_YPfUVfUhE1scF5vRjQq14IKKXK6Fb76bLJS1_thV32e2rA0bjDJRQ4WF5a_gFI9ovHn-2jfW3C9qeJ0DwD5LNBb2QiLNX_U553Rzt26drr92jy4hib1x-fI-jqakeZSnXvs6Myee3Ccxufyiu4KpxHwWC (accessed on 4 August 22).
  60. Gumisiriza, M.S.; Ndakidemi, P.; Nalunga, A.; Mbega, E.R. Building sustainable societies through vertical soilless farming: A cost-effectiveness analysis on a small-scale non-greenhouse hydroponic system. Sustainable Cities and Society 2022, 83, 103923. [Google Scholar] [CrossRef]
  61. Wang, L.; Iddio, E. Energy performance evaluation and modeling for an indoor farming facility. Sustainable Energy Technologies and Assessments 2022, 52, 102240. [Google Scholar] [CrossRef]
  62. Yap, L. Converting Urban Areas into Indoor Pesticide-Free Farms for Year-Round Food. Available online: https://www.azocleantech.com/article.aspx?ArticleID=1275 (accessed on 27 July 22).
  63. Delorme, M.; Santini, A. Energy-efficient automated vertical farms. Omega 2022, 109, 102611. [Google Scholar] [CrossRef]
  64. McDonald, J. Vertical farms have the vision, but do they have the energy? Available online:. Available online: https://www.emergingtechbrew.com/stories/2022/04/21/vertical-farms-have-the-vision-but-do-they-have-the-energy (accessed on 27 July 22).
  65. iFarm. How Much Electricity Does a Vertical Farm Use. Available online: https://ifarm.fi/blog/2020/12/how-much-electricity-does-a-vertical-farm-consume (accessed on 08/04/22).
  66. Haitsma Mulier, M.C.G.; Van de Ven, F.H.M.; Kirshen, P. Quantification of the local water energy nutrient food nexus for three urban farms in Amsterdam & Boston. Energy Nexus 2022, 6, 100078. [Google Scholar] [CrossRef]
  67. MET. Monthly Data - Dublin Airport; Phoenix Park; Casement Aerodrome. Available online: https://www.met.ie/climate/available-data/monthly-data (accessed on 28 July 22).
  68. Keena, C. Usage could outpace grid’s capacity to generate more electricity, report likely to say. The Irish Times 2021.
  69. Chen, X.; Pan, M.; Li, X.; Zhang, K. Multi-mode operation and thermo-economic analyses of combined cooling and power systems for recovering waste heat from data centers. Energy Conversion and Management 2022, 266, 115820. [Google Scholar] [CrossRef]
  70. Li, J.; Yang, Z.; Li, H.; Hu, S.; Duan, Y.; Yan, J. Optimal schemes and benefits of recovering waste heat from data center for district heating by CO2 transcritical heat pumps. Energy Conversion and Management 2021, 245, 114591. [Google Scholar] [CrossRef]
  71. Yamaura, H.; Kanno, K.; Takano, N.; Isozaki, M.; Iwasaki, Y. Supra-optimal daily mean temperature stimulates plant growth and carbohydrate use in tomato. Scientia Horticulturae 2021, 276, 109780. [Google Scholar] [CrossRef]
  72. Ras, M.; Steyer, J.-P.; Bernard, O. Temperature effect on microalgae: a crucial factor for outdoor production. Reviews in Environmental Science and Bio/Technology 2013, 12, 153–164. [Google Scholar] [CrossRef]
  73. Baxtel. Republic of Ireland Data Centre Market. Available online: https://baxtel.com/data-center/republic-of-ireland (accessed on 13 April 22).
  74. DataCenters. Ireland Data Centre Market. Available online: https://www.datacenters.com/locations/ireland (accessed on 31 July 22).
  75. DataCenters. London Data Centre Market. Available online: https://www.datacenters.com/locations?page=2&per_page=40&query=&withProducts=false&showHidden=false&nearby=false&cities%5B%5D%5Bcity%5D=London&cities%5B%5D%5Bstate%5D=England&cities%5B%5D%5Bcountry%5D=United%20Kingdom&radius=0&bounds=&circleBounds=&polygonPath= (accessed on 5 August 22).
  76. MET. Irish Meteorological Service: Dublin Airport 1981–2010 averages. Available online: https://www.met.ie/climate-ireland/1981-2010/dublin.html (accessed on 01/08/22)).
  77. Emeraldgreens. Emerald Greens To Increase Capacity By 20% In New Deal. 2021.
  78. EPA. EPA data shows Ireland’s 2021 Greenhouse Gas Emissions above pre-Covid levels. Annual 2022.
  79. METUK. Met Office UK:Greenwich Park Average graphs. Available online: https://www.metoffice.gov.uk/research/climate/maps-and-data/uk-climate-averages/u10hb54gm (accessed on 5 August 22).
  80. Wang, X.; Wen, Q.; Yang, J.; Xiang, J.; Wang, Z.; Weng, C.; Chen, F.; Zheng, S. A review on data centre cooling system using heat pipe technology. Sustainable Computing: Informatics and Systems 2022, 35, 100774. [Google Scholar] [CrossRef]
  81. Luo, Y.; Andresen, J.; Clarke, H.; Rajendra, M.; Maroto-Valer, M. A decision support system for waste heat recovery and energy efficiency improvement in data centres. Applied Energy 2019, 250, 1217–1224. [Google Scholar] [CrossRef]
  82. Pambudi, N.A.; Sarifudin, A.; Firdaus, R.A.; Ulfa, D.K.; Gandidi, I.M.; Romadhon, R. The immersion cooling technology: Current and future development in energy saving. Alexandria Engineering Journal 2022, 61, 9509–9527. [Google Scholar] [CrossRef]
  83. Cen, J.; Li, Z.; Wang, Y.; Jiang, F.; Liao, S.; Liang, F. Heat Pump-Based Novel Energy System for High-Power LED Lamp Cooling and Waste Heat Recovery; Intechopen: Online, 2018. [Google Scholar]
  84. Engler, N.; Krarti, M. Review of energy efficiency in controlled environment agriculture. Renewable and Sustainable Energy Reviews 2021, 141, 110786. [Google Scholar] [CrossRef]
  85. Chu, J.; Huang, X. Research status and development trends of evaporative cooling air-conditioning technology in data centers. Energy and Built Environment 2021. [Google Scholar] [CrossRef]
  86. Zhu, Y.; Cheng, Z.; Feng, K.; Chen, Z.; Cao, C.; Huang, J.; Ye, H.; Gao, Y. Influencing factors for transpiration rate: A numerical simulation of an individual leaf system. Thermal Science and Engineering Progress 2022, 27, 101110. [Google Scholar] [CrossRef]
  87. Zhang, C.; Luo, H.; Wang, Z. An economic analysis of waste heat recovery and utilization in data centers considering environmental benefits. Sustainable Production and Consumption 2022, 31, 127–138. [Google Scholar] [CrossRef]
  88. Adenaeuer, L. Up, Up and Away! The Economics of Vertical Farming. Journal of Agricultural Studies 2014, 2, 40–60. [Google Scholar] [CrossRef]
  89. ElectricIreland. Electricity Prices. Available online: https://www.electricireland.ie/switch/new-customer/price-plans?priceType=E (accessed on 16 August 22).
  90. Tandon, A. ‘Food miles’ have larger climate impact than thought, study suggests. Available online: https://www.carbonbrief.org/food-miles-have-larger-climate-impact-than-thought-study-suggests/ (accessed on 18 August 22).
  91. Statista. Emission factors for transporting food worldwide as of 2018, by selected modes of transport. Available online: https://www.statista.com/statistics/1253773/food-freight-transport-emission-factors-by-mode/ (accessed on 8 August 22).
  92. ASHRAE. 2021 Equipment Thermal Guidelines for Data Processing.
  93. Environments. 2021.
  94. Barickman, T.C.; Olorunwa, O.J.; Sehgal, A.; Walne, C.H.; Reddy, K.R.; Gao, W. Yield, Physiological Performance, and Phytochemistry of Basil (Ocimum basilicum L.) under Temperature Stress and Elevated CO2 Concentrations. Plants 2021, 10, 1072. [Google Scholar] [CrossRef]
  95. He, Z.; Su, C.; Cai, Z.; Wang, Z.; Li, R.; Liu, J.; He, J.; Zhang, Z. Multi-factor coupling regulation of greenhouse environment based on comprehensive growth of cherry tomato seedlings. Scientia Horticulturae 2022, 297, 110960. [Google Scholar] [CrossRef]
  96. Johnson, A.J.; Meyerson, E.; de la Parra, J.; Savas, T.L.; Miikkulainen, R.; Harper, C.B. Flavor-cyber-agriculture: Optimization of plant metabolites in an open-source control environment through surrogate modeling. PLoS One 2019, 14, e0213918. [Google Scholar] [CrossRef]
  97. Walters, K.J.; Lopez, R.G. Modeling growth and development of hydroponically grown dill, parsley, and watercress in response to photosynthetic daily light integral and mean daily temperature. PLOS ONE 2021, 16, e0248662. [Google Scholar] [CrossRef]
  98. Yamori, N.; Levine, C.P.; Mattson, N.S.; Yamori, W. Optimum root zone temperature of photosynthesis and plant growth depends on air temperature in lettuce plants. Plant Mol Biol 2022. [Google Scholar] [CrossRef]
  99. Xu, J.; Henry, A.; Sreenivasulu, N. Rice yield formation under high day and night temperatures—A prerequisite to ensure future food security. Plant, Cell & Environment 2020, 43, 1595–1608. [Google Scholar] [CrossRef]
  100. Rai, S.K.; Ghosh, P.K.; Kumar, S.; Singh, J.B. Research in Agrometeorolgy on Fodder Crops in Central India—An Overview. Atmospheric and Climate Sciences 2014, 4. [Google Scholar] [CrossRef]
  101. Khammayom, N.; Maruyama, N.; Chaichana, C.; Hirota, M. Impact of environmental factors on energy balance of greenhouse for strawberry cultivation. Case Studies in Thermal Engineering 2022, 33, 101945. [Google Scholar] [CrossRef]
  102. Kuzay, M.; Dogan, A.; Yilmaz, S.; Herkiloglu, O.; Atalay, A.S.; Cemberci, A.; Yilmaz, C.; Demirel, E. Retrofitting of an air-cooled data center for energy efficiency. Case Studies in Thermal Engineering 2022, 36, 102228. [Google Scholar] [CrossRef]
  103. Ham, S.-W.; Park, J.-S.; Jeong, J.-W. Optimum supply air temperature ranges of various air-side economizers in a modular data center. Applied Thermal Engineering 2015, 77, 163–179. [Google Scholar] [CrossRef]
  104. Cho, J.; Park, B.; Jeong, Y. Thermal Performance Evaluation of a Data Center Cooling System under Fault Conditions. Energies 2019, 12, 2996. [Google Scholar] [CrossRef]
  105. Naranjani, B.; Najafianashrafi, Z.; Pascual, C.; Agulto, I.; Chuang, P.-Y.A. Computational analysis of the environment in an indoor vertical farming system. International Journal of Heat and Mass Transfer 2022, 186, 122460. [Google Scholar] [CrossRef]
  106. Callahan, C.W.; Elansari, A.M.; Fenton, D.L. Chapter 8 - Psychrometrics. In Postharvest Technology of Perishable Horticultural Commodities, Yahia, E.M., Ed.; Woodhead Publishing: 2019; pp. 271-310.
  107. Qingjuan, Y.; Wanyi, S.; Ziqi, L. A microclimate model for plant transpiration effects. Urban Climate 2022, 45, 101240. [Google Scholar] [CrossRef]
  108. CSO. Ireland's Trade in Goods 2020. Available online: https://www.cso.ie/en/releasesandpublications/ep/p-ti/irelandstradeingoods2020/food/ (accessed on 8 August 22).
  109. Whitehead, B.; Andrews, D.; Shah, A. The life cycle assessment of a UK data centre. The International Journal of Life Cycle Assessment 2015, 20, 332–349. [Google Scholar] [CrossRef]
  110. Zhou, F.; Shen, C.; Ma, G.; Yan, X. Power usage effectiveness analysis of a liquid-pump-driven hybrid cooling system for data centers in subclimate zones. Sustainable Energy Technologies and Assessments 2022, 52, 102277. [Google Scholar] [CrossRef]
  111. Coyne, B.; Denny, E. An Economic Evaluation of Future Electricity Uses in Irish Data Centres. Available online: https://www.econstor.eu/bitstream/10419/226784/1/TRiSS-WPS-2018-02.pdf (accessed on 18 August 22).
  112. Gibbons, L.; Coyne, B.; Kennedy, D.; Alimohammadi, S. A Techno-Economic Analysis of Current Cooling Techniques in Irish Data Centres. In Proceedings of the 2019 25th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC), 2019, 25-27 Sept. 2019; pp. 1–6. [Google Scholar]
  113. Gibbons, L.; Persoons, T.; Alimohammadi, S. Techno-Economic and Sustainability Analysis of Potential Cooling Methods in Irish Data Centres. Journal of Electronics Cooling and Thermal Control 2021, 10. [Google Scholar] [CrossRef]
  114. Burke, C. €450 million Ennis data centre granted planning permission despite local opposition. The Journal 2022.
  115. McCárthaigh, S. Amazon gets planning permission for two new data centres in north Dublin. Independent 2022.
  116. Martin, M. Government announces sectoral emissions ceilings, setting Ireland on a pathway to turn the tide on climate change. 2022.
  117. Amazon. Sustainability in the Cloud. Available online: https://sustainability.aboutamazon.com/environment/the-cloud?energyType=true (accessed on 18 August 22).
  118. Microsoft. An update on Microsoft’s sustainability commitments: Building a foundation for 2030. Available online: https://blogs.microsoft.com/blog/2022/03/10/an-update-on-microsofts-sustainability-commitments-building-a-foundation-for-2030/ (accessed on 18 August 22).
  119. Harney, C. Dublin brothers become Ireland’s first commercial vertical farmers. Available online: https://www.farmersjournal.ie/dublin-brothers-become-ireland-s-first-commercial-vertical-farmers-653493 (accessed on 18 August 22).
  120. Lei, N.; Masanet, E. Climate- and technology-specific PUE and WUE estimations for U.S. data centers using a hybrid statistical and thermodynamics-based approach. Resources, Conservation and Recycling 2022, 182, 106323. [Google Scholar] [CrossRef]
  121. Davidson, O.; Lorimer, P.; Tomson, F. How can vertical farmers power through the energy crisis? Available online:. Available online: https://www.lettusgrow.com/blog/vertical-farming-energy-crisis (accessed on 18 August 22).
  122. Lane, P.; Boekhout, R. Will rising electricity prices kill off vertical farming? Available online:. Available online: https://www.hortidaily.com/article/9404736/will-rising-electricity-prices-kill-off-vertical-farming/ (accessed on 18 August 22).
  123. EPA. Agriculture sector emission share 2021. Available online: https://www.epa.ie/our-services/monitoring--assessment/climate-change/ghg/agriculture/ (accessed on 9 August 22).
  124. Magilo, N. How Different Types of Agriculture Impact CO2 Emissions. Available online: https://www.greenforges.com/blog/how-different-types-of-agriculture-impact-co2-emissions (accessed on 18 August 22).
  125. Shehabi, A.; Ganguly, S.; Gundel, L.A.; Horvath, A.; Kirchstetter, T.W.; Lunden, M.M.; Tschudi, W.; Gadgil, A.J.; Nazaroff, W.W. Can combining economizers with improved filtration save energy and protect equipment in data centers? Building and Environment 2010, 45, 718–726. [Google Scholar] [CrossRef]
  126. Kathoke, K.; Jangra, H.; Kumar, V. Data Center Market by Component. Available online: https://www.alliedmarketresearch.com/data-center-market-A13117 (accessed on 21 August 22).
  127. ScienceDirect. Search results:”Data Centre”. Available online: https://www.sciencedirect.com/search?qs=data%20centre&years=2023%2C2022%2C2021&lastSelectedFacet=years (accessed on 21 August 22).
  128. Bahari, H.; Mohamed Shariff, S. Review on data center issues and challenges: Towards the Green Data Center; 2016; pp. 129-134.
  129. Mytton, D.; Ashtine, M. Sources of data center energy estimates: A comprehensive review. Joule 2022. [Google Scholar] [CrossRef]
  130. Lavi, H. Measuring greenhouse gas emissions in data centres: the environmental impact of cloud computing. Available online: https://www.climatiq.io/blog/measure-greenhouse-gas-emissions-carbon-data-centres-cloud-computing (accessed on 18 August 22).
  131. Hamilton, T.B. In a small Dutch town, a fight with Meta over a massive data center. Available online: https://www.washingtonpost.com/climate-environment/2022/05/28/meta-data-center-zeewolde-netherlands/ (accessed on 21 August 22).
  132. Goodbody, W. Planning permission extension for Apple's Galway data centre quashed by High Court. Available online: https://www.rte.ie/news/business/2022/0607/1303435-planning-extension-for-apple-galway-data-centre-quashed/ (accessed on 21 August 22).
  133. Meredith, S. World’s biggest companies accused of exaggerating their climate actions. Available online: https://www.cnbc.com/2022/02/07/study-worlds-biggest-firms-seen-exaggerating-their-climate-actions.html (accessed on 12 August 22).
  134. Monbiot, G. Carbon offsetting is not warding off environmental collapse – it’s accelerating it. Available online: https://www.theguardian.com/commentisfree/2022/jan/26/carbon-offsetting-environmental-collapse-carbon-land-grab (accessed on 18 August 22).
  135. Oliver, J. Carbon Offsets: Last Week Tonight with John Oliver (HBO). 2022.
  136. Park, H.; Blenkinsopp, J. The roles of transparency and trust in the relationship between corruption and citizen satisfaction. International Review of Administrative Sciences 2011, 77, 254–274. [Google Scholar] [CrossRef]
  137. Dou, H.; Niu, G.; Gu, M.; Masabni, J.G. Responses of Sweet Basil to Different Daily Light Integrals in Photosynthesis, Morphology, Yield, and Nutritional Quality. HortScience horts 2018, 53, 496–503. [Google Scholar] [CrossRef]
  138. Puigdueta, I.; Aguilera, E.; Cruz, J.L.; Iglesias, A.; Sanz-Cobena, A. Urban agriculture may change food consumption towards low carbon diets. Global Food Security 2021, 28, 100507. [Google Scholar] [CrossRef]
  139. Van Gerrewey, T.; Boon, N.; Geelen, D. Vertical Farming: The Only Way Is Up? Agronomy 2022, 12, 2. [Google Scholar] [CrossRef]
  140. Engler, N.; Krarti, M. Optimal designs for net zero energy controlled environment agriculture facilities. Energy and Buildings 2022, 272, 112364. [Google Scholar] [CrossRef]
  141. ESB. Electric Ireland Announces Energy Price Increases Effective from August 1st, 2022. Available online: https://www.esb.ie/media-centre-news/press-releases/article/2022/07/01/electric-ireland-announces-energy-price-increases-effective-from-august-1st-2022 (accessed on 21 August 22).
  142. Hennessy, M. Taoiseach affirms growing energy demand as electricity cost rises 86%. Available online: https://www.thejournal.ie/electricity-prices-5846255-Aug2022/ (accessed on 24 August 22).
  143. EPA. Latest emissions data. Available online: https://www.epa.ie/our-services/monitoring--assessment/climate-change/ghg/latest-emissions-data/ (accessed on 18 August 22).
  144. Hinkle, L.E.; Wang, J.; Brown, N.C. Quantifying potential dynamic façade energy savings in early design using constrained optimization. Building and Environment 2022, 221, 109265. [Google Scholar] [CrossRef]
  145. Jin, C.; Bai, X. The study of servers’ arrangement and air distribution strategy under partial load in data centers. Sustainable Cities and Society 2019, 49, 101617. [Google Scholar] [CrossRef]
  146. Sajid, S.; Jawad, M.; Hamid, K.; Khan, M.U.S.; Ali, S.M.; Abbas, A.; Khan, S.U. Blockchain-based decentralized workload and energy management of geo-distributed data centers. Sustainable Computing: Informatics and Systems 2021, 29, 100461. [Google Scholar] [CrossRef]
  147. Ozcan, H.; Kayabasi, E. Thermodynamic and economic analysis of a synthetic fuel production plant via CO2 hydrogenation using waste heat from an iron-steel facility. Energy Conversion and Management 2021, 236, 114074. [Google Scholar] [CrossRef]
  148. Ma, G.-y.; Cai, J.-j.; Zeng, W.-w.; Dong, H. Analytical Research on Waste Heat Recovery and Utilization of China's Iron & Steel Industry. Energy Procedia 2012, 14, 1022–1028. [Google Scholar] [CrossRef]
  149. Chen, J.; Xing, Y.; Wang, Y.; Zhang, W.; Guo, Z.; Su, W. Application of iron and steel slags in mitigating greenhouse gas emissions: A review. Science of The Total Environment 2022, 844, 157041. [Google Scholar] [CrossRef] [PubMed]
  150. Rahimi, B.; Marvi, Z.; Alamolhoda, A.A.; Abbaspour, M.; Chua, H.T. An industrial application of low-grade sensible waste heat driven seawater desalination: A case study. Desalination 2019, 470, 114055. [Google Scholar] [CrossRef]
  151. Larrinaga, P.; Campos-Celador, Á.; Legarreta, J.; Diarce, G. Evaluation of the theoretical, technical and economic potential of industrial waste heat recovery in the Basque Country. Journal of Cleaner Production 2021, 312, 127494. [Google Scholar] [CrossRef]
  152. Teace, E.L. How Long Can a Cruise Ship Stay Out At Sea For? Available online:. Available online: https://emmacruises.com/cruise-ship-stay-at-sea/ (accessed on 18 August 22).
  153. Baldi, F.; Ahlgren, F.; Nguyen, T.-V.; Thern, M.; Andersson, K. Energy and Exergy Analysis of a Cruise Ship. Energies 2018, 11, 2508. [Google Scholar] [CrossRef]
  154. Konur, O.; Yuksel, O.; Korkmaz, S.A.; Colpan, C.O.; Saatcioglu, O.Y.; Muslu, I. Thermal design and analysis of an organic rankine cycle system utilizing the main engine and cargo oil pump turbine based waste heats in a large tanker ship. Journal of Cleaner Production 2022, 368, 133230. [Google Scholar] [CrossRef]
  155. Kosmadakis, G.; Neofytou, P. Reversible high-temperature heat pump/ORC for waste heat recovery in various ships: A techno-economic assessment. Energy 2022, 256, 124634. [Google Scholar] [CrossRef]
  156. Ahern, R.F. Travel by Cargo Ship: What You Should Know about Freighter Travel. Available online: https://www.gonomad.com/1560-freighter-travel-faqs (accessed on 18 August 22).
  157. Wang, S.; Liu, Z.; Liu, C.; Wang, X. Thermodynamic analysis of operating strategies for waste heat recovery of combined heating and power systems. Energy 2022, 258, 124803. [Google Scholar] [CrossRef]
  158. Sakdanuphab, R.; Sakulkalavek, A. Design, empirical modelling and analysis of a waste-heat recovery system coupled to a traditional cooking stove. Energy Conversion and Management 2017, 139, 182–193. [Google Scholar] [CrossRef]
  159. Abdoulla-Latiwish, K.O.A.; Mao, X.; Jaworski, A.J. Thermoacoustic micro-electricity generator for rural dwellings in developing countries driven by waste heat from cooking activities. Energy 2017, 134, 1107–1120. [Google Scholar] [CrossRef]
  160. Lamperti, G. Vertical farms inside restaurants? How hydroponics is creating a natural farming future — indoors. Available online: https://geneticliteracyproject.org/2021/09/07/vertical-farms-inside-restaurants-how-hydroponics-is-creating-a-natural-farming-future-indoors/ (accessed on 18 August 22).
  161. Fang, L.; Xu, Q.; Yin, T.; Fang, J.; Shi, Y. Numerical analysis of layout of air conditioning in data center considering seasonal factors. Energy Reports 2022, 8, 1365–1371. [Google Scholar] [CrossRef]
  162. Rahman, M.; Nguyen, V.T.V. A statistical approach to multisite downscaling of daily extreme temperature series: A case study using data in Bangladesh. Journal of Hydro-environment Research 2022, 44, 77–87. [Google Scholar] [CrossRef]
  163. Ustaoglu, A.; Yaras, A.; Sutcu, M.; Gencel, O. Investigation of the residential building having novel environment-friendly construction materials with enhanced energy performance in diverse climate regions: Cost-efficient, low-energy and low-carbon emission. Journal of Building Engineering 2021, 43, 102617. [Google Scholar] [CrossRef]
  164. Zhang, X.; Ma, D.; Lv, J.; Feng, Q.; Liang, Z.; Chen, H.; Feng, J. Food waste composting based on patented compost bins: Carbon dioxide and nitrous oxide emissions and the denitrifying community analysis. Bioresource Technology 2022, 346, 126643. [Google Scholar] [CrossRef]
  165. Jahangir, M.H.; Mokhtari, R.; Mousavi, S.A. Performance evaluation and financial analysis of applying hybrid renewable systems in cooling unit of data centers – A case study. Sustainable Energy Technologies and Assessments 2021, 46, 101220. [Google Scholar] [CrossRef]
  166. Bennert, A. Are Air Purifiers Good for the Environment? Available online:. Available online: https://www.airoasis.com/blogs/articles/are-air-purifiers-good-for-environment (accessed on 24 August 22).
  167. Jung, Y.; Kim, J.; Kim, H.; Nam, Y.; Cho, H.; Lee, H. Comprehensive multi-criteria evaluation of water source heat pump systems in terms of building type, water source, and water intake distance. Energy and Buildings 2021, 236, 110765. [Google Scholar] [CrossRef]
  168. Pattanaik, M.S.; Cheekati, S.K.; Varma, V.B.; Ramanujan, R.V. A novel magnetic cooling device for long distance heat transfer. Applied Thermal Engineering 2022, 201, 117777. [Google Scholar] [CrossRef]
  169. Ahmed, H.A.; Yu-Xin, T.; Qi-Chang, Y. Optimal control of environmental conditions affecting lettuce plant growth in a controlled environment with artificial lighting: A review. South African Journal of Botany 2020, 130, 75–89. [Google Scholar] [CrossRef]
  170. Khosravi, A.; Laukkanen, T.; Vuorinen, V.; Syri, S. Waste heat recovery from a data centre and 5G smart poles for low-temperature district heating network. Energy 2021, 218, 119468. [Google Scholar] [CrossRef]
Preprints 71211 g001
Figure 1. Heat exchange system that transfers heat from the exhaust of the data centre ( θ e )
Figure 1. Heat exchange system that transfers heat from the exhaust of the data centre ( θ e )
Preprints 71211 g001
Preprints 71211 g002
Figure 2. Schematic diagram of one-borefield model as described by [9].
Figure 2. Schematic diagram of one-borefield model as described by [9].
Preprints 71211 g002
Preprints 71211 g003
Figure 3. Energy distribution of vertical farm for romaine lettuce, rocket, and strawberries [65].
Figure 3. Energy distribution of vertical farm for romaine lettuce, rocket, and strawberries [65].
Preprints 71211 g003
Table 1. Available information on data centres in Ireland [73,74].
Table 1. Available information on data centres in Ireland [73,74].
Code Site Name Area (m2) Power (MW) kW.m-2
I1 Amazon Dublin Mulhuddart Campus 20,717 35.00 1.69
I2 Blanchardstown Dublin - Digital Realty 11,148 8.00 0.72
I3 BT Citywest Ireland Dublin 10,219 3.20 0.31
I4 CyrusOne Dublin Grange Castle 11,148 18.00 1.61
I5 Digital Profile Park Dublin 8,361 11.50 1.38
I6 DUB10 Blanchardstown Dublin Data Center 11,148 10.00 0.90
I7 DUB12 Clonshaugh Dublin Data Center 8,000 10.00 1.25
I8 Echelon Arklow 14,400 35.00 2.43
I9 EdgeConnex DUB04 12,797 7.00 0.55
I10 Edgeconnex Dublin 6,000 18.00 3.00
I11 Equinix Dublin DB1 11,136 4.72 0.42
I12 Equinix Dublin DB3 & DB4 10,552 10.08 0.96
I13 Equinix: DB3 Ballycoolin Data Center 10,552 10.08 0.96
I14 Facebook Clonee Ireland 86,000 108.00 1.26
I15 Google Dublin Grange Castle 28,800 80.00 2.78
I16 Interxion Dublin DUB3 2,320 4.60 1.98
I17 K2 Dublin 1 11,799 18.00 1.53
I18 Keppel DC Dublin1 6,328 8.00 1.26
I19 Microsoft DUB 06 21,553 20.00 0.93
I20 Microsoft DUB 07 16,258 24.00 1.48
I21 Microsoft DUB 08 15,979 24.00 1.50
I22 Microsoft DUB 09 15,979 24.00 1.50
I23 Microsoft DUB 10 15,979 24.00 1.50
I24 Microsoft DUB 12 15,979 24.00 1.50
I25 Microsoft DUB 13 15,979 24.00 1.50
I26 Microsoft DUB 14 28,066 32.00 1.14
I27 Microsoft DUB 15 28,168 32.00 1.14
I28 Microsoft Dublin DB3 Grange 51,097 47.00 0.92
I29 Microsoft Dublin Grange Castle 28,150 23.40 0.83
I30 Sungard: Dublin - Park West - DC2 Data Center 2,248 0.46 0.20
I31 Sungard: Dublin - Profile Park - DC3 Data Center 621 0.70 1.13
I32 T5 Data Centers: @Ireland Data Center 30,008 60.00 2.00
Iav Average 17,734 23.71 1.32
Table 2. Available information on data centres in London [75].
Table 2. Available information on data centres in London [75].
Code Site Name Area (m2) Power (MW) kW.m-2
L1 Cyxtera: LHR1 Slough Data Center Campus 5,574 13.50 2.42
L2 Cyxtera: LHR1 Slough Data Center Campus 5,574 13.50 2.42
L3 Cyxtera: LHR2 Docklands Data Center Campus 5,574 2.70 0.48
L4 DataBank: Heathrow Data Center 11,148 3.00 0.27
L5 Digital Realty: LHR18 Oliver's Yard London Data Center 2,450 4.00 1.63
L6 Digital Realty: LHR19 Cloud House West, 47 Millharbour 1,771 2.50 1.41
L7 Digital Realty: LHR20 Sovereign House 227 Marsh Wall 8,865 12.00 1.35
L8 Digital Realty: LON1 London Data Center 4,645 8.00 1.72
L9 Digital Realty: LON2 London Data Center 1,858 4.00 2.15
L10 e-shelter: London 1 Data Center 24,000 8.00 0.33
L11 Equinix: LD1 London Data Center 809 0.54 0.67
L12 Equinix: LD3 Park Royal Data Center 3,900 3.96 1.02
L13 Equinix: LD8 London Data Center 12,769 12.00 0.94
L14 Equinix: LD9 London Data Center 26,345 21.00 0.80
L15 Global Switch: London North Data Center 2,900 29.00 10.00
L16 INAP: London 2 Data Center 836 1.50 1.79
L17 IP House: IP House Data Center 1,486 2.00 1.35
L18 Netwise: Harbour Exchange Data Center 5,574 0.50 0.09
L19 Netwise: London Central Data Center 1,022 0.50 0.49
L20 Netwise: Telehouse North Data Center 9,290 0.50 0.05
L21 Server Farm: Lon-1 Data Center 11,148 10.50 0.94
L22 Sungard Availability Services: TC2 - Docklands UK 6,169 13.50 2.19
L23 Sungard Availability Services: Woking - TC3 UK Data Center 4,951 10.00 2.02
L24 Sungard Availability Services: Woking - TC3 UK Data Center 4,951 10.00 2.02
L25 Volta Data Centres: Great Sutton Street 8,454 9.60 1.14
L26 Volta Data Centres: Great Sutton Street 84,542 9.60 0.11
L27 Voxility: Digital Realty Memaco House in London 1,943 1.00 0.51
LAV Average 9,576 7.66 1.49
Table 3. Ideal growing temperatures of common produce.
Table 3. Ideal growing temperatures of common produce.
Product Ideal growing temperature Noted by
Basil 25-30˚ C [93]
Cherry tomato 27.6˚ C [94]
Cotton 18-35˚C depending on stage of growth [95]
Dill 22.5˚ C [96]
Lettuce 30˚C (Day); 25˚ C (Night) [97]
Maize 32-35˚ C [98]
Oat 25˚ C [99]
Parsley 28˚ C [96]
Rice 30-32˚ C [99]
Strawberries 23-28˚ C [100]
Table 4. Psychrometric properties of ideal data centre conditions, exhaust air of data centre, inlet air of data centre, and average Irish weather conditions.
Table 4. Psychrometric properties of ideal data centre conditions, exhaust air of data centre, inlet air of data centre, and average Irish weather conditions.
Parameter Symbol Unit Data centre ideal conditions Exhaust Average Inlet Average Irish Average London Average Vertical farm conditions Vertical farm transpiration effect per 1000 m2
Dry bulb temperature Tdb ˚ C 21.50 30.00 20.50 10.10 15.75 20.00 17.60
Wet bulb temperature Twb ˚ C 14.35 17.42 13.91 8.22 12.38 13.71 12.56
Dew point Td ˚ C 9.28 9.28 9.16 6.49 9.90 9.15 8.73
Relative Humidity RH % 45.50 27.50 48.00 78.15 68.00 49.50 55.93
Enthalpy h kJ/kgdry air 40.05 48.71 38.88 25.25 34.98 38.36 35.40
Specific Volume V m3/kgdry air 0.84 0.87 0.84 0.81 0.83 0.84 0.83
Partial Vapour Pressure Pp Pa 1,167 1,167 1,158 966 1,217 1,157 1,125
Saturated Vapor Pressure Ps Pa 2,565 4,246 2,412 1,236 1,790 2,339 2,013
Humidity Ratio HR kgwater/kgdry air 0.0072 0.0072 0.0072 0.0060 0.0076 0.0072 0.0070
Absolute Humidity W kgwater/m3dry air 0.0085 0.0083 0.0086 0.0074 0.0092 0.0086 0.0084
Specific Heat of air Cair kJ.kgdry air-1.K-1 1.0185 1.0185 1.0185 1.0163 1.0193 1.0185 1.0182
Volumetric Enthalpy hV kJ.m-3 47.45 56.05 46.23 31.17 42.25 45.67 42.50
Volumetric specific heat of air Cvair kJ.m-3.K-1 1.21 1.17 1.21 1.25 1.23 1.21 1.22
Table 5. List of Irish data centres calculated mass flow rate of circulating air, the resulting volumetric flow rate of air available to a vertical farm at ideal temperatures, and the ideal size of the vertical farm depending on this flow rate. Full data available in Table S1.
Table 5. List of Irish data centres calculated mass flow rate of circulating air, the resulting volumetric flow rate of air available to a vertical farm at ideal temperatures, and the ideal size of the vertical farm depending on this flow rate. Full data available in Table S1.
Code Waste heat energy (kJ) Waste heat energy per area (J.m-2) Mass flow rate (kg.s-1) Volumetric flow rate of air in data centre (m3.s-1) Ideal size of vertical farm (m2)
Min 207 92.07 21.39 17.77 79.51
Max 48600 1,350.01 5022.69 4,172.78 18,667.69
IAV 10670 594.10 1102.70 916.10 4,098.36
Table 6. List of London data centres calculated mass flow rate of circulating air, the resulting volumetric flow rate of air available to a vertical farm at ideal temperatures, and the ideal size of the vertical farm depending on this flow rate. Full data available in Table S2.
Table 6. List of London data centres calculated mass flow rate of circulating air, the resulting volumetric flow rate of air available to a vertical farm at ideal temperatures, and the ideal size of the vertical farm depending on this flow rate. Full data available in Table S2.
Code Waste heat energy (kJ) Waste heat energy per area (J.m-2) mass flow rate (kg.s-1) Volumetric flow rate of air in data centre (m3.s-1) Ideal size of vertical farm (m2)
Min 225 24.22 23.25 19.32 196.18
Max 13050 4,500.00 1348.68 1,120.47 11,378.72
LAV 3448.33 672.15 356.38 296.07 3,006.71
Table 7. Energy requirements of romaine lettuce, rocket and strawberries grown in a vertical farm setup, based on a 1000 m3 vertical farm as described by [65]. *Excluding use of AC systems, humidifier, and dehumidifier.
Table 7. Energy requirements of romaine lettuce, rocket and strawberries grown in a vertical farm setup, based on a 1000 m3 vertical farm as described by [65]. *Excluding use of AC systems, humidifier, and dehumidifier.
Romaine Lettuce Rocket Strawberries
Energy Source (W/m2) Waste energy produced (W/m2) 30 days (W/m2) (W/m2) Waste energy produced (W/m2) 30 days (W/m2) (W/m2) Waste energy produced (W/m2) 30 days (W/m2)
Led Lamps 90 67.5 38880 68 51 29376 180 135 77760
AC System 30 27 12960 22.5 20.25 9720 60 54 25920
Computer 0.2 0.18 144 0.2 0.18 144 0.2 0.18 144
Osmosis 1.5 1.35 270 1.5 1.35 270 1.5 1.35 270
Fertigation 1.2 1.08 216 1.2 1.08 216 1.2 1.08 216
Pump 7.4 6.66 444 7.4 6.66 444 7.4 6.66 444
Dehumidifier 20 18 12000 20 18 12000 20 18 12000
Humidifier 1.2 1.08 720 1.2 1.08 720 1.2 1.08 720
Automation 0.3 0.27 216 0.3 0.27 216 0.3 0.27 216
Work Lamps 0.4 0.36 120 0.4 0.36 120 0.4 0.36 120
Webcam 0.02 0.018 14.4 0.02 0.018 14.4 0.02 0.018 14.4
Total kW per 1000 m3 152.22 123.50 65,984 122.72 100.25 53,240 272.22 218.00 117,824
Total kW per 1000 m3 * 101.02 77.418 40304.4 79.02 60.918 30800.4 191.02 144.918 79184.4
Table 8. Irish [left] and London [right] based data centres’ potential cooling effect of transpiration, and heating effect of the electronics of vertical farm contributing a net negative amount of energy, cooling the vertical farm to 15.19˚ C. Full data available in Table S3.
Table 8. Irish [left] and London [right] based data centres’ potential cooling effect of transpiration, and heating effect of the electronics of vertical farm contributing a net negative amount of energy, cooling the vertical farm to 15.19˚ C. Full data available in Table S3.
Net energy savings of Irish data centres Net energy savings of London-based data centres
Code Cooling energy of transpiration (kW) Heating effect of vertical farm waste energy (kW) Energy savings (kW) Code Cooling energy of transpiration (kW) Heating effect of vertical farm waste energy (kW) Energy savings (kW)
Min 100.81 7.51 4.67 Min 248.74 18.52 11.52
Max 23,668.1 1,762.57 1,095.79 Max 14,426.7 1,074.36 667.93
IAV 5,196.17 386.96 240.57 LAV 3,812.11 283.89 176.49
Table 9. Scenario 2 data for Irish data centres to predict ideal size of vertical farm. Full data available in Table S4.
Table 9. Scenario 2 data for Irish data centres to predict ideal size of vertical farm. Full data available in Table S4.
Code Usable waste heat energy (kJ) Mass flow rate (kg.s-1) Mass flow rate of external air into vertical farm (kg.s-1) Volumetric flow rate of air into vertical farm (m3.s-1) Volumetric flow rate of air into vertical farm per square meter of data centre (m3.s-1.m-2) Ideal size of vertical farm (m2) Energy savings (kW)
Min 114 21.39 8.68 7.29 0.003 18.24 1.07
Max 26730 5022.69 2,038.90 1,712.68 0.048 4281.69 251.34
IAV 5868 1,102.70 447.626 376.01 0.021 940.015 55.179
Table 10. Scenario 2 data for London data centres to predict ideal size of vertical farm. Full data available in Table S5.
Table 10. Scenario 2 data for London data centres to predict ideal size of vertical farm. Full data available in Table S5.
Code Usable waste heat energy (kJ) Mass flow rate (kg.s-1) Mass flow rate of external air into vertical farm (kg.s-1) Volumetric flow rate of air into vertical farm (m3.s-1) Volumetric flow rate of air into vertical farm per square meter of data centre (m3.s-1.m-2) Ideal size of vertical farm (m2) Energy savings (kW)
Min 123.75 23.25 36.61 30.75 0.003 76.89 4.51
Max 7,177.50 1,348.68 2,123.52 1,783.76 0.615 4459.39 261.77
LAV 1,896.58 356.38 561.12 471.34 0.09 1178.35 69.17
Table 14. The number of calories and portions of fruit or vegetables a vertical farm can provide compared to the same land area using traditional farming methods for Irish data centres in Scenario 2. Full data available in Table S8.
Table 14. The number of calories and portions of fruit or vegetables a vertical farm can provide compared to the same land area using traditional farming methods for Irish data centres in Scenario 2. Full data available in Table S8.
Code People who can obtain daily calories (Vertical Farm) People who can obtain daily calories (Field) People who can obtain 7 portions of fruit or veg (Vertical Farm) People who can obtain 7 portions of fruit or veg (Field)
Min 0 0 0 0
Max 64 20 61 23
IAV 14 4 13 5
Table 15. The number of calories and portions of fruit or vegetables a vertical farm can provide compared to the same land area using traditional farming methods for London data centres in Scenario 2. Full data available in Table S9.
Table 15. The number of calories and portions of fruit or vegetables a vertical farm can provide compared to the same land area using traditional farming methods for London data centres in Scenario 2. Full data available in Table S9.
Code People who can obtain daily calories (Vertical Farm) People who can obtain daily calories (Field) People who can obtain 7 portions of fruit or veg (Vertical Farm) People who can obtain 7 portions of fruit or veg (Field)
Min 1 0 1 0
Max 67 21 63 24
LAV 18 6 17 6
Table 16. Carbon impact of importing goods from various countries in either chilled or ambient storage. Costa Rica 277.4 kg CO2e/tonne; South Africa 297.2 kg CO2e/tonne; Spain 100.1 kg CO2e/tonne; and UK 85.6 kg CO2e/tonne [91,107].
Table 16. Carbon impact of importing goods from various countries in either chilled or ambient storage. Costa Rica 277.4 kg CO2e/tonne; South Africa 297.2 kg CO2e/tonne; Spain 100.1 kg CO2e/tonne; and UK 85.6 kg CO2e/tonne [91,107].
kg of CO2e per tonne transported per km using different transportation methods and conditions Costa Rica to Dublin (kg of CO2e per tonne) Spain to Dublin (kg of CO2e per tonne) South Africa to Dublin (kg of CO2e per tonne) UK to Dublin (kg of CO2e per tonne)
Road ambient 0.2 40 40 40 40
Road cooled 0.6 120 120 120 120
Ship ambient 0.01 131.6031 13.3714 144.8264 3.75
Ship cooled 0.02 263.2062 26.7428 289.6528 7.5
Total ambient emissions 171.60 53.37 184.83 43.75
Total cooled emissions 383.21 146.74 409.65 127.5
Average 277.40465 100.0571 297.2396 85.625
Table 17. Comparison of the reduction in cost of operations and environmental impact in Scenario 1 & 2 for Irish data centres and vertical farm systems.
Table 17. Comparison of the reduction in cost of operations and environmental impact in Scenario 1 & 2 for Irish data centres and vertical farm systems.
Scenario 1 Scenario 2
Code Annual cost savings (€) Annual reduction in CO2 emissions from energy (kg) Annual reduction in CO2 emissions from transport (kg) Total Annual Reduction in CO2 emissions (kg) Annual cost savings (€) Annual reduction in CO2 emissions from energy (kg) Annual reduction in CO2 emissions from transport (kg) Total Annual Reduction in CO2 emissions (kg)
I1 843,207 975,893 8,314 984,207 193,401 223,835 1,907 225,742
I2 192,733 223,061 1,900 224,962 44,206 51,162 436 51,598
I3 77,093 89,225 760 89,985 17,682 20,465 174 20,639
I4 433,649 501,888 4,276 506,164 99,463 115,115 981 116,096
I5 277,054 320,651 2,732 323,382 63,546 73,546 627 74,172
I6 240,916 278,827 2,375 281,202 55,257 63,953 545 64,498
I7 240,916 278,827 2,375 281,202 55,257 63,953 545 64,498
I8 843,207 975,893 8,314 984,207 193,401 223,835 1,907 225,742
I9 168,641 195,179 1,663 196,841 38,680 44,767 381 45,148
I10 433,649 501,888 4,276 506,164 99,463 115,115 981 116,096
I11 113,712 131,606 1,121 132,727 26,082 30,186 257 30,443
I12 242,844 281,057 2,394 283,452 55,700 64,464 549 65,014
I13 242,844 281,057 2,394 283,452 55,700 64,464 549 65,014
I14 2,601,895 3,011,328 25,655 3,036,982 596,781 690,690 5,884 696,574
I15 1,927,330 2,230,613 19,004 2,249,617 442,060 511,622 4,359 515,981
I16 110,821 128,260 1,093 129,353 25,418 29,418 251 29,669
I17 433,649 501,888 4,276 506,164 99,463 115,115 981 116,096
I18 192,733 223,061 1,900 224,962 44,206 51,162 436 51,598
I19 481,832 557,653 4,751 562,404 110,515 127,906 1,090 128,995
I20-25 578,199 669,184 5,701 674,885 132,618 153,487 1,308 154,794
I26 770,932 892,245 7,601 899,847 176,824 204,649 1,743 206,392
I27 770,932 892,245 7,601 899,847 176,824 204,649 1,743 206,392
I28 1,132,306 1,310,485 11,165 1,321,650 259,710 300,578 2,561 303,139
I29 563,744 652,454 5,559 658,013 129,303 149,649 1,275 150,924
I30 11,082 12,826 109 12,935 2,542 2,942 25 2,967
I31 16,864 19,518 166 19,684 3,868 4,477 38 4,515
I32 1,445,497 1,672,960 14,253 1,687,212 331,545 383,717 3,269 386,986
Min 11,082 12,826 109 12,935 2,542 2,942 25 2,967
Max 2,601,895 3,011,328 25,655 3,036,982 596,781 690,690 5,884 696,574
IAV 571,227 661,115 5,632 666,748 131,019 151,636 1,292 152,928
Table 18. Comparison of the reduction in cost of operations and environmental impact in Scenario 1 & 2 for London data centres and vertical farm systems.
Table 18. Comparison of the reduction in cost of operations and environmental impact in Scenario 1 & 2 for London data centres and vertical farm systems.
Scenario 1 Scenario 2
Code Annual cost savings (€) Annual reduction in CO2 emissions from energy (kg) Annual reduction in CO2 emissions from transport (kg) Total Annual Reduction in CO2 emissions (kg) Annual cost savings (€) Annual reduction in CO2 emissions from energy (kg) Annual reduction in CO2 emissions from transport (kg) Total Annual Reduction in CO2 emissions (kg)
L1 738,292 854,469 7,280 861,749 289,342 334,872 2,853 337,725
L2 738,292 854,469 7,280 861,749 289,342 334,872 2,853 337,725
L3 147,658 170,894 1,456 172,350 57,868 66,974 571 67,545
L4 164,065 189,882 1,618 191,500 64,298 74,416 634 75,050
L5 218,753 253,176 2,157 255,333 85,731 99,221 845 100,067
L6 136,721 158,235 1,348 159,583 53,582 62,013 528 62,542
L7 656,260 759,528 6,471 765,999 257,193 297,664 2,536 300,200
L8 437,507 506,352 4,314 510,666 171,462 198,443 1,691 200,133
L9 218,753 253,176 2,157 255,333 85,731 99,221 845 100,067
L10 437,507 506,352 4,314 510,666 171,462 198,443 1,691 200,133
L11 29,532 34,179 291 34,470 11,574 13,395 114 13,509
L12 216,566 250,644 2,135 252,780 84,874 98,229 837 99,066
L13 656,260 759,528 6,471 765,999 257,193 297,664 2,536 300,200
L14 1,148,455 1,329,175 11,324 1,340,498 450,087 520,912 4,438 525,350
L15 1,585,961 1,835,527 15,638 1,851,165 621,549 719,355 6,128 725,484
L16 82,032 94,941 809 95,750 32,149 37,208 317 37,525
L17 109,377 126,588 1,078 127,667 42,865 49,611 423 50,033
L18 27,344 31,647 270 31,917 10,716 12,403 106 12,508
L19 27,344 31,647 270 31,917 10,716 12,403 106 12,508
L20 27,344 31,647 270 31,917 10,716 12,403 106 12,508
L21 574,227 664,587 5,662 670,249 225,043 260,456 2,219 262,675
L22 738,292 854,469 7,280 861,749 289,342 334,872 2,853 337,725
L23 546,883 632,940 5,392 638,333 214,327 248,053 2,113 250,167
L24 546,883 632,940 5,392 638,333 214,327 248,053 2,113 250,167
L25 525,008 607,623 5,177 612,799 205,754 238,131 2,029 240,160
L26 525,008 607,623 5,177 612,799 205,754 238,131 2,029 240,160
L27 54,688 63,294 539 63,833 21,433 24,805 211 25,017
Min 27,344 31,647 270 31,917 10,716 12,403 106 12,508
Max 1,585,961 1,835,527 15,638 1,851,165 621,549 719,355 6,128 725,484
LAV 419,075 485,020 4,132 489,152 164,238 190,082 1,619 191,702
Table 19. Summary of main findings of a vertical farm situated in Ireland versus London based on the proposed system of Scenario 1.
Table 19. Summary of main findings of a vertical farm situated in Ireland versus London based on the proposed system of Scenario 1.
Parameter Irish Min Irish Max Irish Average London Min London Max London Average
Ideal size of vertical farm (m2) 79.5 18,667.7 4,098.4 196.2 11,378.7 3,006.7
Area of vertical farm compared to data centre 0.04 0.52 0.23 0.02 3.92 0.59
Energy savings (kW) 4.67 1,095.79 240.57 11.52 667.93 176.49
People who can obtain daily calories 1 280 61 3 171 45
People who can obtain 7 portions of fruit or veg 1 264 58 3 161 43
Annual cost savings (€) 11,082 2,601,895 571,227 27,344 1,585,961 419,075
Total Annual Reduction in CO2 emissions (kg) 12,935 3,036,982 666,748 31,917 1,851,165 489,152
Table 20. Summary of main findings of a vertical farm situated in Ireland versus London based on the proposed system of Scenario 2.
Table 20. Summary of main findings of a vertical farm situated in Ireland versus London based on the proposed system of Scenario 2.
Parameter Irish Min Irish Max Irish Average London Min London Max London Average
Ideal size of vertical farm (m2) 621 86,000 17,734 809 84,542 9,576
Area of vertical farm compared to data centre 0.46 108.00 23.71 0.50 29.00 7.66
Energy savings (kW) 207 48600 10670 225.00 13050.00 3448.33
People who can obtain daily calories 114 26730 5868 123.75 7,177.50 1,896.58
People who can obtain 7 portions of fruit or veg 18.2 4281.7 940.0 76.9 4459.4 1178.4
Annual cost savings (€) 0.008 0.119 0.052 0.008 1.538 0.23
Total Annual Reduction in CO2 emissions (kg) 1.07 251.34 55.18 4.51 261.77 69.17
Table 21. Summary of all scenarios.
Table 21. Summary of all scenarios.
Parameter Irish Average Scenario 1 Irish Average Scenario 2 London Average Scenario 1 London Average Scenario 2
Ideal size of vertical farm (m2) 4,098.4 940.0 3,006.7 1178.4
Area of vertical farm compared to data centre 0.23 0.052 0.59 0.23
Energy savings (kW) 240.57 55.18 176.49 69.17
People who can obtain daily calories 61 14 45 18
People who can obtain 7 portions of fruit or veg 58 13 43 17
Annual cost savings (€) 571,227 131,019 419,075 164,238
Total Annual Reduction in CO2 emissions (kg) 666,748 152,928 489,152 191,702
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated