Engineering

Abstract: Artificial high-reflectivity ground covers are a potential strategy to increase rear-side irradiance and energy yield in bifacial photovoltaic systems, especially in utility-scale plants with single-axis trackers. This paper reports field evidence from a pilot plant in southern Brazil (27.4°S, 48.4°W), where four reflective covers—a white film, a pearl-white film, a black-and-pearl film, and a white geomembrane—were evaluated against a gray gravel reference. The study combines albedo and spectral characterization, rear-to-front irradiation ratios, energy-yield comparisons, soiling assessment, thermal analysis, and operational observations. Broadband albedo increased from 25% for gray gravel to 53–58% for the reflective films and 72% for the geomembrane. Reflective films increased the rear-to-front irradiation ratio to around 20% and delivered energy gains close to 9%, while the geomembrane achieved the highest irradiance enhancement and gains exceeding 10%. Inverter current limitations led to clipping, indicating that measured gains may underestimate the full energy potential of the reflective covers. Estimated thermal losses were insignificant compared with measured gains, while soiling and fixation methods affected long-term feasibility. The results confirm the technical potential of reflective covers, while showing that utility-scale deployment must consider not only optical performance, but also optical stability, electrical limitations, cleaning and anchoring requirements, drainage adaptations, operation and maintenance practices, and cost constraints.

Abstract: Indoor environmental quality in university classrooms significantly influences the overall 11 well-being of occupants. However, implementing continuous monitoring networks is of-12 ten restricted by high equipment costs. This paper presents the architectural design, firm-13 ware implementation, and virtual validation of a low-cost, multi-sensor IoT system aimed 14 at monitoring key environmental parameters: air quality index indicators, ambient light, 15 and acoustic distress indicators. The system architecture centers on an ESP32 microcon-16 troller, integrated with simulated response curves for an MQ-135 sensor, an LDR, and a 17 sound detection module. Due to physical deployment constraints, the system was strictly 18 validated using electronic simulation software (Proteus VSM). The simulation results 19 demonstrate a reliable firmware execution with an algorithm latency under 10 ms during 20 multi-trigger alarm states. Furthermore, a virtual power consumption analysis was con-21 ducted, revealing an average current draw of 100 mA under normal operations and a peak 22 dynamic current draw spanning from 130 mA up to 280 mA during high-priority alarm 23 cycles. The proposed virtual framework establishes a technically viable, open-source blue-24 print for rapid-deployment environmental telemetry before transitioning to physical scal-25 ing.

Abstract: Sanitary sewer collection systems are among the least observable urban infrastructure assets, with most utilities operating fewer than one sensor per several hundred pipes; placement drives operational value. We develop DPP-SP, a Descriptive–Predictive–Prescriptive Sensor-Placement framework that links machine-learning failure prediction with risk-weighted maximum-coverage placement and apply it to a 33,349-pipe sewer system. The geographic information system (GIS) topology is rebuilt, raising the largest connected component from 29.8% to 89.4% of nodes. Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and a multilayer perceptron (MLP) are trained on combined 2020–2025 failure data; RF achieves the highest receiver-operating-characteristic area under the curve (ROC-AUC) of 0.7626 and supplies per-pipe risk weights. A maximum weighted set-cover problem is solved over 680 candidate sites using greedy, genetic algorithm (GA) and tabu search (TS). At K=48, RF with greedy covers 32.26% of network risk against an 11.73% baseline, a 174.9% improvement; all three metaheuristics converge on the same solution. Extending to K=400 exposes a 56.58% structural ceiling due to isolated fragments, and a six-radius sensitivity study (200–2,500 m) identifies detection range as the dominant design parameter. Risk coverage can be nearly tripled by redeploying the existing 48 stations at no capital cost.

Abstract: Standard H_∞ controller synthesis produces robust controllers with well-shaped sensitivity and complementary sensitivity transfer functions, S and T. However, at times H_∞ does not enforce strict requirements on sensitivity, in particular the desired requirement that T has unity gain at DC frequency. This results in typically negligible steady-state tracking error, as the H_∞ optimization produces T(0)≈1. In drive cycle applications where reference velocity profiles contain extended ramp segments, this negligible deviation is integrated over time into a growing, non-negligible bias. The conventional remedy is to augment the plant with an integrator prior to synthesis, but this increases the order of the plant model and can be inconvenient when the control designer’s modeling has already been completed. This paper presents a post-synthesis gain adjustment method using Youla parameterization that corrects the DC tracking deficiency without modifying the plant or repeating H_∞ synthesis. The poles and zeros corresponding to the H_∞ controller’s Youla transfer function Y are preserved, with a free parameter K replacing the gain of Y. Re-calculating the controller after solving for the value of K that enforces T(0)=1 results in a hybrid controller that retains the robustness of the original but with improved performance in ramp-input scenarios with minimal effort for the control designer. Simulation results on a vehicle speed tracking problem confirm elimination of accumulating bias while preserving robustness margins from the original H_∞ design.