1. Introduction

2. Data Input and Methods

2.2. Methods

2.2.1. Theory

The Kister and Haas method, along with the Fair's correlation method, are used to estimate the flood capacity of the distillation column. These methods take into account various factors such as column geometry, fluid properties, and operating conditions to predict the flood point. The net area of the column is considered in the flood calculation.

Kister and Haas gave a correlation which is said to reproduce a large data base of measured flood points to within + 15 percents. CSB, flood is based on the net area.

By using equation (2) and obtaining the necessary parameters, such as the net area and hydrostatic head equation (5), h_ct was obtained subsequently from equation (3) can be applied to calculate the percentage flood for each distillation column. This percentage indicates the extent to which the column is operating at or near its flood capacity as calculated in equation (7). It's important to note that the accuracy of these predictions may vary, and it is recommended to validate the results against actual plant data to ensure reliability.

$$\%\mathrm{flood}=100\mathrm{x}\frac{{\mathit{C}}_{\mathit{S}\mathit{B}}}{{\mathit{C}}_{\mathit{S}\mathit{B}\mathit{d}\mathit{e}\mathit{r}\mathit{a}\mathit{t}\mathit{e}\mathit{d}}}$$

(2)

2.2.2. Fluid Package Selection

The selection of a suitable fluid package in ASPEN Hysys plays a crucial role in accurately simulating the thermodynamic behavior and equilibrium of the system under investigation. The fluid package, or thermodynamic model, serves as the foundation for calculating properties such as phase behavior, heat capacity, enthalpy, and vapor-liquid equilibrium.

In the context of this journal publication, the fluid package selection process was of particular importance. It involved replicating the thermodynamic model using a fluid program, guided by the work of Yadav (2020) [11]. The choice of fluid package depends on the specific requirements of the components involved in the separation process or the particular application area.

To assist users in selecting an appropriate fluid package, ASPEN Hysys provides a method assistant tool. This tool aids users in identifying the most suitable method based on component properties or the specific application area. By utilizing the method assistant, users without prior knowledge or expertise in fluid package selection can make informed choices to ensure reliable and accurate simulation results.

By addressing the significance of fluid package selection and highlighting the availability of the method assistant tool, this journal publication emphasizes the importance of choosing the appropriate thermodynamic model to enhance the reliability and accuracy of the simulation results in the context of the distillation cascade model being studied. A summary of fluid package selection from the method assistant tool for each distillation column under study is shown in Table 5.

2.2.3. Model Development

Based on the provided assumptions, the model development for the distillation cascade in the Olefins Plant aims to achieve the separation of components to obtain pure products. Here is a summary of the assumptions [19,23]:

• Ideal Solution: Both vapor and liquid phases are assumed to be ideal solutions, and constant average relative volatilities are considered. This assumption simplifies the thermodynamic calculations involved in the separation process.
• Condenser and Operating Conditions: The column top temperature is estimated based on the assumed condenser operating pressure. The feed and reflux streams are assumed to be at their dew point and bubble point, respectively. A total condenser is assumed for the reflux condenser.
• Constant Molal Overflow: The assumption of constant molal overflow simplifies the calculation of liquid and vapor flows in the column.
• Stage Efficiency: The stages in the column are initially assumed to be 100% efficient with respect to mass transfer. However, adjustments may be made to match the simulation results with actual plant conditions.
• Pressure Drop and Reboiler Temperature: The pressure drop through the tower is calculated based on the sieve tray characteristics. The temperature of the reboiler is estimated based on the tower bottom pressure.
• Stream Configuration: The distillation column is assumed to have only the feed, distillate, and bottoms streams, without any other side streams.
• Uniform Composition: The liquid hold-up in the reboiler, reflux drum, and on each tray of the column is assumed to be well-mixed regions with uniform composition.
• Negligible Dynamics: The dynamics of the piping, reboiler, and condenser are considered negligible, implying that there are no significant time lags in the system. Vapor phase dynamics are neglected as they are much faster compared to the liquid phase.
• Constant Liquid Hold-ups: The liquid hold-ups are assumed to be constant on each tray, as well as in the reboiler and reflux drums.
• Adiabatic Column: The column is assumed to be adiabatic, neglecting any heat release from components. Decay heat is also neglected in this case.
• Actual Feed Composition: The feed composition to the column is obtained from actual plant operation data, ensuring that the model reflects the real-world conditions.
• The equations for the non-equilibrium state describe liquid and heat accumulation. The equilibrium conditions are only valid at bubble-point temperature. Once the bubble-point temperature is reached, the equations are switched to the equilibrium state. We assumed that all the vapor entering the stage is condensed until the temperature on each stage reaches the equilibrium state.

Overall, the objective of the distillation cascade model is to accurately simulate the separation process in the Olefins Plant, allowing for the production of pure products by effectively separating the components.

The simulation block under study is depicted in Figure 2, illustrating the configuration of the distillation column in ASPEN Hysys.

The data input stages for the simulation block are illustrated in Figure 3.

2.2.4. Model Validation

The simulation of distillation columns using the rate-based approach involves solving a large and highly non-linear system of algebraic equations [20]. In this research work, the equations are implemented in the Aspen Hysys simulation software, which is a process simulator tool. One advantage of using Aspen Hysys is the availability of the ASPEN Properties Database, which provides reliable thermodynamic properties for the calculations.

To solve the complex system of equations, good starting values are required. In this work, these values are generated through simulations using a model with lower complexity, specifically the equilibrium stage model. The column can be divided into segments, and different correlations for the process hydraulics are implemented in each segment based on the column internal modeling approach derived from the plant data sheet.

To ensure the simulation results match the actual plant results, calculated parameters for the composition of the top and bottom components are compared. The comparison is done using the Absolute Percent Error (% Error) calculation, as shown in equation (8). The tray efficiency in the Aspen Hysys simulator software is adjusted iteratively to minimize the Absolute Percent Error (AAPE, %).

$$\mathrm{Absolute}\mathrm{Percent}\mathrm{Error}(\mathrm{AAPE},\%)=100\mathrm{x}\frac{{|\mathit{V}\mathit{a}\mathit{l}\mathit{u}\mathit{e}}_{\mathit{S}\mathit{i}\mathit{m}\mathit{u}\mathit{l}\mathit{a}\mathit{t}\mathit{i}\mathit{o}\mathit{n}}-{\mathit{V}\mathit{a}\mathit{l}\mathit{u}\mathit{e}}_{\mathit{A}\mathit{c}\mathit{t}\mathit{u}\mathit{a}\mathit{l}}|}{{\mathit{V}\mathit{a}\mathit{l}\mathit{u}\mathit{e}}_{\mathit{A}\mathit{c}\mathit{t}\mathit{u}\mathit{a}\mathit{l}}}$$

(8)

By iteratively adjusting the tray efficiency and minimizing the Absolute Percent Error, the simulation results can be improved to better match the actual plant results, thereby enhancing the accuracy and reliability of the simulation model.

2.2.5. Sensitivity Analysis Substitution of Naphtha to LPG into % Flooding

To determine the maximum percentage of naphtha substitution with LPG without exceeding 100% flooding, a trial and error approach can be employed using the simulation model. The simulation model can be run for different percentages of LPG substitution in incremental steps, and the corresponding flooding percentages can be calculated. Table [3] can be used to guide the trial process by specifying the number of furnaces run with LPG feed and the corresponding flowrates of naphtha and LPG. The simulation model will calculate the flooding percentage for each case.

It is important to note that the simulation should be limited to ensure that the flooding percentage does not exceed 100%. Flooding occurs when the liquid flowrate in the column exceeds its capacity to separate the components effectively, resulting in reduced separation efficiency and potential operational issues. Exceeding 100% flooding can lead to severe flooding conditions and loss of column performance.

Therefore, during the trial process, the LPG substitution percentage should be monitored to ensure it does not exceed the maximum allowable value that would result in 100% flooding. By carefully selecting the substitution percentages and evaluating the corresponding flooding percentages, an optimal point can be determined where the desired naphtha substitution is achieved without exceeding the flooding limit [6,8].

2.2.6. Economic Calculation

To calculate the benefit of feed substitution from naphtha to LPG, the equation (9) can be used. The benefit is determined by considering the product flowrate, product prices, feed flowrates, feed prices, and utilities cost.

Here's how the calculation can be performed using the provided data from Table 4.

2.2.6.1. Determine the relevant flowrates:

Product Flowrate: This is the flowrate of the desired product (e.g., ethylene, propylene, mixed C4, or pygas) obtained from the simulation model.

Naphtha Flowrate: This is the initial flowrate of naphtha before substitution, which can be obtained from the plant data or the simulation model.

LPG Flowrate: This is the flowrate of LPG obtained from the simulation model.

2.2.6.2. Determine the relevant prices:

Product Price: This is the price per ton of the desired product (e.g., ethylene, propylene, mixed C4, or pygas) obtained from the cost reference in Table 4.

Naphtha Price: This is the price per ton of naphtha obtained from the cost reference in Table 4.

LPG Price: This is the price per ton of LPG obtained from the cost reference in Table 4.

2.2.6.3. Determine the Utilities Cost:

Utilities Cost: This is the cost per ton of product for utilities (e.g., energy, water, etc.) required for the operation of the distillation column, obtained from the cost reference in Table 4.

Benefit = (Product Flowrate x Product Price) – [(Naphtha Flowrate x Naphtha Price) + (LPG Flowrate x LPG Price)] – Utilities Cost (9)

By plugging in the corresponding values into equation (8), the benefit of feed substitution from naphtha to LPG can be calculated. The result will represent the economic advantage or savings achieved by substituting naphtha with LPG as the feed in the distillation column.

Table 4. Cost reference for economic calculation, data taken from plant data year 2022 [4].

Table 4. Cost reference for economic calculation, data taken from plant data year 2022 [4].

Description	Unit	Price
Naphtha	$/ton	835
LPG	$/ton	750
Ethylene	$/ton	1230
Propylene	$/ton	1310
Mixed C4	$/ton	560
Pygas	$/ton	621
Utilities Cost	$/ton product	8.9

3. Results and Discussion

3.1. Simulation Matching with Plant Actual Data

Table 5 provides a summary of the assessment of simulation results in ASPEN Hysys compared to actual plant data, including the efficiencies achieved in each distillation tower. The fluid package used in ASPEN Hysys was selected based on the guidelines for properties specific to each column under study. The column efficiency was determined through trial and error, following the rule of thumb for distillation column operation. The average absolute percent error (AAPE) was calculated to evaluate the goodness of fit between the simulation and plant data.

Analyzing the efficiencies obtained in each distillation tower (as shown in Table 5), it is evident that they vary across the columns under study. The Demethanizer column achieved an efficiency of 72.87%, while the Deethanizer column had an efficiency of 74.31%. Similarly, the Ethylene Fractionator exhibited an efficiency of 71.92%. It is important to note that the efficiencies were determined through a trial and error process, taking into account the specific operational characteristics of each column in order to achieve a minimum error percentage in the simulated column compared with the actual data. These efficiency values highlight the effectiveness of the separation process within the distillation cascade.

In terms of validation, the AAPE values provide an assessment of the agreement between the simulation results and the actual plant data. In this study, the AAPE values for all columns were found to be less than 2%, indicating a good fit between the simulation results and the experimental data. This suggests that the simulation model accurately predicts the behavior of the distillation columns and provides reliable results for the study.

It is crucial to consider the level of validation achievable through the experimental data, taking into account the obtained error. The AAPE values below 2% indicate a high level of agreement between the simulation and plant data. However, it is important to acknowledge the limitations and uncertainties associated with experimental measurements. Factors such as measurement accuracy, instrumentation, and process variations may contribute to the observed error. Nevertheless, the small magnitude of the AAPE values suggests a favorable level of validation for the simulation results.

By discussing the different efficiencies achieved in each distillation tower and considering the level of validation achievable through the experimental data, this journal publication provides a comprehensive evaluation of the simulation results and highlights the reliability of the findings.

Table 5. Summary of assesment simulation result in ASPEN Hsys compare with actual plant data, Fluid Package in ASPEN Hsys selected based on guideline of properties in each column under study [10], column Efficiency as trial result by following rule of thumb in distillation column operating [18], and the average absolute percent error (AAPE) to asses the goodness of fit.

Table 5. Summary of assesment simulation result in ASPEN Hsys compare with actual plant data, Fluid Package in ASPEN Hsys selected based on guideline of properties in each column under study [10], column Efficiency as trial result by following rule of thumb in distillation column operating [18], and the average absolute percent error (AAPE) to asses the goodness of fit.

Column	Fluid Package in ASPEN Hysys	Column Efficiency (%)	AAPE (%)
Demethanizer	UNIQUAQ	72.87	0.81
Deethanizer	Peng-Robinson	74.31	0.92
Ethylene Fractionator	Peng-Robinson	71.92	0.77
Depropanizer No.1	Peng-Robinson	73.14	0.84
Depropanizer No.2	Peng-Robinson	73.19	0.95
Propylene Fractionator No. 1	SRK-Twu (Soave-Redlich-Kwong)	74.15	0.88
Propylene Fractionator No.2	SRK-Twu (Soave-Redlich-Kwong)	74.92	1.12
Propylene Fractionator No.3	SRK-Twu (Soave-Redlich-Kwong)	74.08	1.13
Debutanizer	Peng-Robinson	73.12	1.15

Based on calculation result shown in Table 5 can be concluded that model have satificatory result due to AAPE (%) < 2% [6,8].

4. Summary and Conclusions

Nomenclature

References

1. Kirk, and Othmer, Encyclopedia of Chemical Technology, 2007 4th Ed., pp. 416-418, John Willey and Sons, New York.
2. J. J. Duffy, Argus, UK and P. Morse, Argus, “Asia and Europe Join the Feedstock Evolution with Steam Crackers”, Hydrocarbon Processing, April 2018, Gulf Publishing Company Houston, Texas. Available online: Business Trends (hydrocarbonprocessing.com) (accessed on: 12 Maret 2021).
3. Schaschke, C. , 2014, A Dictionary of Chemical Engineering, pp.816-819 Oxford University Press, England.
4. PT Chandra Asri Petrochemical TbK, Production Flow PT Chandra Asri Petrochemical TbK; Available online: https://www.chandra-asri.com/our-business/production-flow (accessed on: 12 Maret 2021).
5. Fakhroleslam, Mohammad, and Sadrameli, Seyed Mojtaba, 2020. Thermal Cracking of Hydrocarbons for the Production of Light Olefins; A Review on Optimal Process Design, Operation, and Control. Ind. Eng. Chem. Res., Vol 59, pp. 88−103. [CrossRef]
6. Kister, Henry Z., 1992, Distillation Design, Brown and Root Braun, California.
7. Lummus Technology and CB&I Company, 2012, Basic Engineering Package for Chandra Asri Petrochemical ECC Expansion Project.
8. CEP, AIChE Publication, 13, Evaluating Distillation Column Performance p 27. 20 June.
9. Hanley, Brian, et.all., 2016, Column Analysis in Aspen Plus® and Aspen HYSYS®: Validation with Experimental and Plant Data, ASPEN Technology Inc., United States.
10. W. L. Luyben, 2013, Distillation design and control using Aspen simulation John Wiley & Sons, New York.
11. Yadav, Eadala Sarath, Indiran, Thirunavukkarasu, Nayak, Dayananda, and Kumar, CHVB Aditya, 2020, “Simulation Study of Distillation Column using Aspen plus”, Mat. Pr., Vol. 29, pp. 60-69. [CrossRef]
12. F. G. Shinskey, 1991, Process Control Systems: Application, Design and Tuning, McGraw-Hill, Inc., New York.
13. Loshchev, C. Cardona, and Y.A. Pisarenko, 2010, ”Degrees of freedom Analysis for a Distillation Column”, Theor. Found. Chem. Eng., Vol.44 (5), pp. 686–697. [CrossRef]
14. H.J Qestha, S. H.J Qestha, S., Abuyahya, P. Pal and F., Banat, 2015, ”Sweetening Liquified Petroleum Gas (LPG): Sensitivity Analysis using ASPEN HYSYS ”, J. Nat. Gas. Sci. Eng., Vol. 25, pp. 1011−1017. [CrossRef]
15. Huang, D. , and Luo, X.-L, 2018, ”Process Transition Based on Dynamic Optimization with the Case of a Throughput-Fluctuating Ethylene Column”, Ind. Eng. Chem. Res., Vol. 57, pp. 292−302. [CrossRef]
16. Kessler, D.P. and Wankat, P.C., 1988, “Correlations for Column Parameters”, Chem. Eng., vol. 71, pp.71-84.
17. Wood MacKenzie, 2019, Ethylene Global Supply Demand Analytics Service, https://www.woodmac.com/news/editorial/ethylene-global-supply-demandanalytics-service/, (accessed on: 12 Maret 2021).
18. Z.-Y. Liu, M. Z.-Y. Liu, M. Jobson, 2004,“Retrofit Design for Increasing the Processing Capacity of Distillation Columns: 1. A Hydraulic Performance Indicator”, Chem. Eng. Res. Des., Vol.82, pp.3-9. [CrossRef]
19. Miresmaeil, Masoumi., Mohammad, Shahrokhi., Mojtaba, Sadrameli., and Jafar, Towfighi, 2006, “Modeling and Control of a Naphtha Thermal Cracking Pilot Plant”, Ind. Eng. Chem. Res., Vol. 45, 3574-3582.
20. E.Y. Kenig, A. E.Y. Kenig, A. Górak, A. Pyhälahti, K. Jakobsson, J.Aittamaa, K. Sundmacher, 2004, “Advanced Rate-based Simulation Tool for Reactive Distillation”, AIChE J., Vol.50, pp. 322-342. [CrossRef]
21. Yaws, C.L. Yaws, C.L. Yaws’ Handbook of Thermodynamic and Physical Properties of Chemical Compounds; Knovel: New York, NY, USA 2003. ISBN 978-1-59124-444-8.
22. “What Is R-Squared?”, Investopedia. Available online: https://www.investopedia.com/terms/r/r-squared.asp (accessed on 6 November 2022).
23. Bruns, B.; Fasel, H.; Grünewald, M.; Riese, J. , 2021, “Development of a Dynamic Modeling Approach to Simulate a Segmented Distillation Column for Flexible Operation”, ChemEngineering 2021, 5(4), 66. [CrossRef]