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Öğe Advanced exergy analysis of a PEM fuel cell with hydrogen energy storage integrated with organic Rankine cycle for electricity generation(Elsevier, 2022) Montazerinejad, H.; Fakhimi, E.; Ghandehariun, S.; Ahmadi, PouriaIn this study, a system consisting of PEMFC and ORC is modeled and analyzed from the thermodynamic aspects. Accordingly, to have a better understanding of the performance of the system, advanced exergy analysis is applied. The analysis is developed in Intel Fortran Compiler within the Microsoft Visual Studio. For modeling all dependent components, separate subroutines in the Microsoft Visual Studio platform were made by implementing the first and second law of thermodynamics. Based on the conventional exergy destruction rate, for the hybrid power system, ?x,D,total is 484.4 kW. The results show that the exergy destruction rate in the PEMFC stack is the highest among other elements. The PEM fuel cell exergy efficiency is 46.89% according to the conventional exergy study. Moreover, the PEM fuel cell and overall electrical efficiencies are 39.06% and 44.81%, respectively. According to the advanced exergy analysis, except for the condenser and compressor, the avoidable part of exergy destruction rates is higher than those unavoidable. The largest endogenous and exogenous exergy destruction rates belong to PEMFC and compressor correspondingly. In addition, the compressor has a poor performance since its endogenous exergy destruction is 34.24 kW, and its unavoidable one is 19.55 kW. Besides, a large portion of its unavoidable (i.e., 57.09%) cannot be wiped out even we use the best technology. Moreover, the enhancement of components performance breeds eliminating the percentage of exogenous available exergy destruction rate of the turbine (57.66%) with the highest value. In the present work, the parametric study is conducted by increasing the current density as an essential design parameter to analyze its effects on some critical parameters such as the overall efficiency, the net generated power, and so on. For implementing the parametric study, the Engineering Equation Solver (EES) was used. The combination of Fortran and EES helped to obtain the required outcomes from the overall system. Besides, the overall efficiency increases until it reaches a maximum value of 47.31%and drops thereafter.Öğe A comprehensive approach for tri-objective optimization of a novel advanced energy system with gas turbine prime mover, ejector cooling system and multi-effect desalination(Elsevier, 2022) Ahmadi, Pouria; Fakhari, I.; Rosen, Marc A.This research presents an innovative approach for optimization based on a Genetic Algorithm optimization method. The system is configured by the integration of a gas turbine cycle, a dual-pressure heat recovery steam generator, a multi-effect desalination unit, a refrigeration organic Rankine cycle with an ejector, and a proton-exchange membrane electrolyzer. The proposed system is optimized utilizing five single- and multi-objective methods and investigating each objective's effect on the optimum range of the decision variables. As a result of these optimization five best points are extracted. The base condition, and these five best points are identified as six conditions, and the performance and reliability of the optimization results are investigated in a comparative parametric study. The single-objective optimizations results show that the maximum possible exergy efficiency and freshwater production rate are 72% and 1354 m3/day, respectively, and the lowest possible total cost rate is 611 $/h. However, tri-objective optimization demonstrates for these parameters that the best point has efficiency, cost, and freshwater production rate values of 69%, 791 $/h, and 1063 m3/day, respectively. The comparative parametric study shows that the tri-objective optimization result (Condition 5) is favorable in terms of objectives and reliability. © 2022 Elsevier LtdÖğe Design and optimization of a novel wind-powered liquefied air energy storage system integrated with a supercritical carbon dioxide cycle(WILEY, 2021) Sadeghi, Shayan; Javani, Nader; Ghandehariun, Samane; Ahmadi, PouriaIn this paper, a novel liquefied air energy storage (LAES) system driven by wind energy and natural gas, integrated with a two-stage supercritical carbon dioxide cycle is proposed and investigated. Three different sensible thermal energy storage (TES) systems are considered in this study to store the heat produced in the compressions stage and use it later to improve the performance. The proposed system is analyzed in terms of energy, exergy, exergo-economic, and environmental impacts. A detailed economical and technical investigation is carried out to determine system hotspots, exergy destruction rates, and the performance of the system and subsystems. Finally, by considering ten design variables, the nondominated sorting genetic algorithm-II (NSGA-II) is employed to evaluate the optimal design solutions for double objective functions, including cost per unit of exergy and overall system exergy efficiency. Results indicate that for a 50 MW of discharging power, 80.75 MW of charging electricity and 1.22 kg/s of fuel are required. Additionally, the product cost per unit of exergy and the levelized costs are calculated as 32.02 $/GJ and 0.133 $/kWh, respectively. The results of the optimization study also show that for the optimal Pareto solution, the energy efficiency would be 52.2% while the exergy efficiency is 45.26%.Öğe A novel intelligent transport system charging scheduling for electric vehicles using Grey Wolf Optimizer and Sail Fish Optimization algorithms(TAYLOR & FRANCIS, 2022) Rajamoorthy, Rajasekaran; Arunachalam, Gokulalakshmi; Kasinathan, Padmanathan; Devendiran, Ramkumar; Ahmadi, Pouria; Pandiyan, Santhiya; Muthusamy, Suresh; Panchal, Hitesh; Kazem, Hussein A.; Sharma, PrabhakarIntelligent Transport System (ITS) intentions to attain traffic efficiency by diminishing traffic difficulties. It supplies information like traffic issues, real-time traveling information, parking availability, etc., in advance to the users who are connected with the smart cities that ensure travelers' safety and comfort. This ITS technique should merge with Electric Vehicles (EVs) because nowadays, EVs have become familiar in the last decade owing to the requirement to cut greenhouse gas emissions and fossil fuels. However, traffic jams caused by EVs driven to the charging stations (CSs) can result in the complex charging scheduling of EVs. Therefore, an effective algorithm is developed for optimal charging scheduling using the proposed Grey Sail Fish Optimization (GSFO). The proposed charging scheduling algorithm integrates Grey Wolf Optimizer (GWO) and Sail Fish Optimization (SFO). For each EV, the demand when charging is computed. The path used by the EV to travel to the charging station is determined by computing the path decision factor. In comparison to existing techniques, the proposed GSFO-based charging algorithm schedules EVs to charging stations based on the fitness function, and the performance was improved with a traffic density of 26.11 km, a distance of 0.0278 kW, and a power of 2.3377. To be more specific, the proposed GSFO improved when many vehicles were considered.Öğe A novel WaveNet-GRU deep learning model for PEM fuel cells degradation prediction based on transfer learning(Pergamon-Elsevier Science Ltd, 2024) Izadi, Mohammad Javad; Hassani, Pourya; Raeesi, Mehrdad; Ahmadi, PouriaPrecise prediction of Remaining Useful Life (RUL) within the transportation industry is essential for cost reduction and enhanced energy efficiency, focusing on extending the operational lifespan of proton exchange membrane fuel cells (PEMFCs). In pursuit of this objective, this study employs data -driven prediction methodologies centered on deep neural networks and transfer learning. The fundamental premise is that these approaches hinge on the compatibility of functional conditions across diverse datasets. Multiple strategies, amalgamating transfer learning, and deep neural networks, are introduced to forecast the PEMFC stack's behavior and its associated RUL. Network hyperparameters are optimized through Bayesian optimization, targeting root -mean -square error (RMSE) minimization in voltage predictions. The efficacy of these prediction techniques is evaluated through essential performance metrics, including the mean absolute percentage error (MAPE), RMSE, and coefficient of determination (R2), applied to both voltage predictions and RUL estimations. For the first time, a WaveNet-GRU model has been developed. Comparative assessment of models trained on 50% of the dataset underscores its supremacy. This model attains R2, RMSE, and MAPE scores of 99.1, 2.16E-4, and 0.166E-1, respectively, in predicting stack voltage. Also, RUL has increased by 21% compared to the best contemporary research. The WaveNet-GRU model demonstrates exceptional transfer learning capabilities when applied to stacks influenced by current ripples. In this context, it achieves optimal R2, RMSE, and MAPE values of 99.69, 1.37E-4, and 0.31E-1, respectively.Öğe The optimum solution for a biofuel-based fuel cell waste heat recovery from biomass for hydrogen production(Elsevier, 2022) Cao, Yan; Hani, E.H. Bani; Khanmohammadi, Shoaib; Ahmadi, PouriaThe current research examined and compared two new hydrogen production systems as well as optimization procedures to determine the best optimum conditions. Developed systems include two integrated systems in one of them a vanadium chloride cycle is used to produce hydrogen from biogas and the other a proton exchange membrane electrolyzer (PEME) is used. Both suggested systems are integrated with a solid oxide fuel cell (SOFC) and an anaerobic digester (AD). A parametric investigation is performed on the systems to determine the effects of changing parameters such as solid oxide fuel cell temperature, fuel cell working pressure, current density, and utilization factor the thermodynamic, economic, and environmental performance of the systems. Findings indicated that the current density of 0.54 was about 5.5 % higher than the system with electrolysis, while the cost rate of the system was 22% percent lower. With the change of SOFC temperature from 900 K to 1300 K, the results showed that in 1000 K, the emission reach 0.273 kg/kWh, which was the lowest value. Moreover, by enhancing the utilization factor from 0.65 to 0.9, the amount of pollution in the system with vanadium chloride cycle was reduced by 24%, which the reduction for the system with PEME was about 20%. Carried out analysis based on the thermodynamic, economic, and environmental index indicated a multi-objective optimization for studied systems. Both developed systems were optimized and the best performance conditions were determined based on the definition of the ideal point.Öğe Soft computing based optimization of a novel solar heliostat integrated energy system using artificial neural networks(Elsevier, 2022) Alirahmi, S.M.; Khoshnevisan, A.; Shirazi, P.; Ahmadi, Pouria; Kari, D.This study proposes and investigates a novel energy system based on biomass and solar energy. This plant is composed of a biomass unit, a solar unit, and a waste-heat recovery unit. This novel proposed integrated system can provide the needs such as electricity, hydrogen, freshwater, heating, and hot water production. For electricity generation, two gas turbines, one steam Rankine cycle, and one organic Rankine cycle are used. In contrast, for utilization of solar energy, a heliostat field, and for biomass conversion, a gasifier is used. In addition, the desalination unit and PEM electrolyzer are utilized to produce fresh water and hydrogen, respectively. Firstly, the present work aims to investigate the developed system from the exergoeconomic and environmental perspective. Multi-objective optimization is conducted to determine the maximum amount of exergetic efficiency and the minimum value of the cost rate. An artificial neural network (ANN) is employed as a mediator tool to accelerate the optimization process. The relation between objective functions and design parameters is studied utilizing ANN to obtain the plant optimal decision variables. Employing the Pareto Envelope-based selection algorithm II (PESA-II) method, the optimum amount for the total cost rate and exergy efficiency is found 224.1 $/h and 26.7%, respectively. In addition, three evolutionary-based optimization algorithms are applied to determine the optimum results of the suggested plant. © 2021 Elsevier LtdÖğe Techno-economic assessment of small-scale gas to liquid technology to reduce waste flare gas in a refinery plant(Elsevier Ltd, 2023) Zayer Kabeh, Kaveh; Teimouri, Aidin; Changizian, Sina; Ahmadi, PouriaThe dissemination of toxic substances to the atmosphere and wasting energy by flaring are important challenges for natural gas facilities, and small-scale gas to liquid (GTL) technology can be used as an efficient method to reduce flaring. In this study, a validated model for technical simulation and variable sales prices of GTL products in five different scenarios are used to perform an accurate, comprehensive, and realistic techno-economic assessment of this technology to reduce flaring in the natural gas refinery. The detailed model of gas sweetening, syngas generation and Fischer-Tropsch synthesis (FTS) units have been simulated in the Aspen HYSYS software. The technical results illustrate that the capacity of this plant is 686 barrels per day, and the main products of this plant are diesel and propane. The results demonstrate the economic feasibility of a small-scale GTL plant in all economic conditions unless oil prices decrease significantly in the following years. Based on the obtained results, rising oil price increases the simulated plant's internal rate of return (IRR) to 39.99%. Finally, the sensitivity analysis results indicate that the profitability of this plant can be guaranteed with low oil prices by reducing CapEx to US$ 50,000 per barrel. © 2022 Elsevier Ltd
