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A new two-stage based evolutionary algorithm for solving multi-objective optimization problems

INFORMATION SCIENCES 2022年 611卷 649-659页

作者： Wang, Yiming Gao, Weifeng Gong, Maoguo Li, Hong Xie, Jin Xidian Univ Sch Math & Stat Xian 710126 Peoples R China Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Int Res Ctr Intelligent Percept & Computat Xian 710071 Peoples R China

It is a challenge to balance the convergence and the diversity in multi-objective optimization problems. In this paper, a new two-stage based evolutionary algorithm (MOEA/TS) is proposed, where the convergence and the diversity are handled in two inde-pendent phases. In the first stage, the convergence is accelerated by using the gradient information of constrained sub-problems. In the second stage, the diversity is improved by adopting the dominance based multi-objective evolutionary algorithm. The compara-tive experiments are presented in terms of two performance indicators for benchmark test problems. The results indicates that MOEA/TS has the competitive performance. (c) 2022 Elsevier Inc. All rights reserved.

关键词： multi -objective optimization Constrained sub -problem Evolutionary algorithm

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multi-objective optimization of protonic ceramic electrolysis cells based on a deep neural network surrogate model

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APPLIED ENERGY 2024年 365卷

作者： Li, Zheng Yu, Jie Wang, Chen Bello, Idris Temitope Yu, Na Chen, Xi Zheng, Keqing Han, Minfang Ni, Meng Hong Kong Polytech Univ Res Inst Sustainable Urban Dev RISUD Dept Bldg & Real Estate Hung HomKowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy RISE Hung Hom Kowloon Hong Kong Peoples R China Jiangsu Univ Sci & Technol Sch Energy & Power Zhenjiang 212100 Peoples R China China Univ Min & Technol Sch Low Carbon Energy & Power Engn Xuzhou 221116 Jiangsu Peoples R China Tsinghua Univ Dept Energy & Power Engn Beijing 100084 Peoples R China

Protonic ceramic electrolysis cell (PCEC) stands out as a promising device to realize large-scale green hydrogen production. This research is dedicated to advancing the optimization of PCEC, specifically targeting key performance indicators including voltage, current density, and Faradaic efficiency (FE). The central aim is the expeditious determination of optimal trade-off points that harmonize electrochemical performance and FE. To achieve this, a comprehensive framework is proposed, integrating three distinct methodologies: multiphysics model, deep neural network, and multi-objective optimization algorithms. The investigation reveals the leakage current along the thickness of the cell is significant relative to its length. Furthermore, an increase in current density from 0.4 A cm-2 to 0.8 A cm-2 results in a reduction of FE and the uniformity of FE by 21.3% and 8.8%, respectively. The identified optimal point at 0.78 A m- 2, 600 degrees C delivers a 12.8% improvement in performance compared to the base case. The primary contribution of this work includes introducing a novel framework which substantially accelerates the optimization of PCEC as well as highlighting the importance of addressing leakage current issues during PCEC operation. The proposed framework has broader applicability for addressing optimization problems of PCEC and advancing other clean energy technologies.

关键词： Protonic ceramic electrolysis cell Faradaic efficiency multi -objective optimization Deep neural network

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Parameter identification of unsaturated seepage model of core rockfill dams using principal component analysis and multi-objective optimization

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STRUCTURES 2022年 45卷 145-162页

作者： Xu, Yunpeng Wu, Zhenyu Sichuan Univ State Key Lab Hydraul & Mountain River Engn 24 South Sect 1 Yihuan Rd Chengdu 610065 Peoples R China Sichuan Univ Coll Water Resources & Hydropower 24 South Sect 1 Yihuan Rd Chengdu 610065 Peoples R China

The seepage behavior of core walls is an important aspect of safety monitoring of core rockfill dams. The permeability coefficient of unsaturated soil of the core wall is a nonlinear function not a constant. Since there are many seepage monitoring points in the core wall, parameter identification of unsaturated seepage model of soil core wall based on the data of these points is a high-dimensional multi-objective optimization problem. In this paper, a parameter identification method of unsaturated seepage in the core wall is proposed, in which an un-saturated seepage model, principal component analysis (PCA), and elitist non-dominated sorting genetic algo-rithm (NSGA-II) are combined. The seepage monitoring sequence of measuring points in saturated and unsaturated regions of the core wall is synthesized to a few comprehensive variables by PCA respectively. The NSGA-II is adopted to optimize the objective functions established by these comprehensive variables to obtain the Pareto-optimal solutions. The efficiency, accuracy, and robustness to the error of the proposed method have been confirmed in a fictitious dam and an actual dam. The predicted seepage pressure with the Brooks-Corey (BC) model is in best agreement with the measured data, and the simulated seepage field is more in line with the actual situation.

关键词： Unsaturated seepage Principal component analysis multi -objective optimization Parameter inversion

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Sensitivity analysis and multi-objective optimization of the energy, exergy and thermo-economic performance of a Brayton supercritical CO2-ORC configurations

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ENERGY REPORTS 2023年 9卷 4437-4455页

作者： Ochoa, Guillermo Valencia Castilla, Dora Villada Casseres, Daniel Mendoza Univ Atlantico Mech Engn Dept Efficient Energy Management Res Grp Carrera 30 Numero 8-49 Puerto Colombia 080007 Colombia Univ Francisco Paula Santander Fac Engn Cucuta 540003 Colombia Univ Atlantico Ind Engn Dept Res Grp 3id Carrera 30 Numero 8-49 Puerto Colombia 080007 Colombia

The following research compared some energy, exergetic and thermo-economic indicators of a supercritical CO2 simple Brayton cycle integrated with a simple organic Rankine cycle (SORC), and a regenerative organic Rankine cycle (RORC). A thermodynamic model was developed to determine the net power, thermal efficiency, the fuel consumption, and the exergy destruction of all the components of the system. Also, a thermo-economic model was developed to determine some economic indicators such as the levelized cost of energy (LCOE), the payback period (PBP) and specific investment cost (SIC). A sensitivity analysis was carried out to study the influence of the primary turbine inlet temperature (TIT), the high-pressure in the compressor (PHigh), the evaporator pinch point temperature difference (PPT), and the pressure ratio (Pr) on the indicators performance. Three different working fluids were selected in this study: acetone, toluene and cyclohexane. The results showed that cyclohexane had the best energy performance giving an efficiency of 48.02% for the RORC system. Besides, it presented the best thermo-economic results for the LCOE (0.26 USD/kWh), SIC (2626.75 USD/kWh), and a PBP (11.2 years). Finally, a multi-objective optimization was developed based on energy, exergy and thermo-economic performance parameters as objective functions to obtain a technical and economic feasible solution able to implement them in industrial applications.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Brayton Supercritical CO 2 Simple organic Rankine cycle Regenerative organic Rankine cycle Thermo-economic multi -objective optimization

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Comprehensive sustainability assessment and multi-objective optimization of a novel renewable energy driven multi-energy supply system

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APPLIED THERMAL ENGINEERING 2024年 236卷

作者： Liu, Lintong Zhai, Rongrong Xu, Yu Hu, Yangdi Liu, Siyuan Yang, Lizhong North China Elect Power Univ Sch Energy Power & Mech Engn Natl Thermal Power Sch Energy Power & Mech Engn Beijing Peoples R China North China Elect Power Univ Natl Thermal Power Engn & Technol Res Ctr Beijing Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing Peoples R China North China Elect Power Univ Key Lab Power Stn Energy Transfer Convers & Syst Minist Educ Beijing 102206 Peoples R China Nanyang Technol Univ Energy Res Inst 1 Clean Tech Loop Singapore 637141 Singapore

The pressing challenge of over-reliance on fossil fuels and the resultant greenhouse gas emissions necessitates innovative solutions in the energy sector. Addressing this, our study delves into the design and optimization of a multi-source renewable energy system, distinctively integrating wind, photovoltaic, concentrated solar power, proton exchange membrane electrolyzers, and fuel cells. The system is designed to meet user energy demands while reducing grid power consumption. The approach is novel in its comprehensive energy management strategy, which not only mitigates the inconsistencies of wind and solar energy but also aligns with user demand proactively. To assess the system's sustainability, we employed a life cycle assessment method, encompassing energy, economic, environmental, and social dimensions. A unique aspect of our study is the exploration of how varying meteorological conditions and geographical locations influence the system's sustainability. Key results indicate that, with an increase in photovoltaic capacity from 25 MW to 150 MW, the hybrid system reduces grid power consumption by 18,457.9 MWh annually. This leads to a decrease in the average energy cost by 0.0324 $/kWh and an increase in the net present value by 0.2418 M$. Impressively, the system, when integrated with 150 MW of photovoltaic generation, achieves an optimal combined sustainability index of 0.999. Under the best capacity configuration, the levelized cost of energy stands at 0.3096$/kW, with CO2 emissions limited to 630 t/y. A noteworthy correlation emerged: a 1 % decrease in onsite CO2 emissions corresponds to a 1.714 % surge in the levelized cost of energy. This research enhances sustainable energy policy development and decision-making processes by offering a comprehensive understanding of multi-energy supply system sustainability. The results are invaluable for the future design and operation of such systems, facilitating a transition towards more sustainable energy systems.

关键词： Comprehensive sustainability assessment multi -objective optimization multi -energy supply system Proton exchange membrane electrolysis Proton exchange membrane fuel cell Life cycle method

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multi-objective optimization and long-term performance evaluation of a hybrid solar-hydrogen energy system with retired electric vehicle batteries for off-grid power and heat supply

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 62卷 867-882页

作者： Song, Ying Xu, Lei Li, Jun Taherian, Hessam Zhang, Yu Liu, Ding Li, Zhiwu Hou, Gaoyang Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Shaanxi Peoples R China Xidian Univ Sch Mechanoelect Engn Xian 671001 Shaanxi Peoples R China Penn State Univ Sch Sci Engn & Technol Harrisburg PA 17057 USA

This paper presents a novel off-grid hybrid renewable energy system integrated with hydrogen production and retired electric vehicle (EV) batteries for combined power and heat supply to a small rural community. The system consists of photovoltaic (PV) panels, evacuated tube solar collectors, a proton exchange membrane fuel cell (PEMFC), an alkaline electrolyzer, compressed hydrogen storage tanks, and reused lithium-ion batteries from EVs. A new battery model is developed in the Transient System Simulation Program (TRNSYS) to capture the capacity degradation of retired batteries. A two-stage energy management strategy is proposed to regulate power flow and maximize solar energy utilization. The system is optimized for minimum power supply disruption, hot water supply disruption, energy waste, and annualized costs over a 20-year lifetime using a multi-objective NSGA-II algorithm coupling MATLAB and TRNSYS. optimization results reveal tradeoffs between the competing objectives. Dynamic simulations show the water tank temperature fluctuates between 20 and 100 degrees C with mean values dropping slightly in later years due to battery degradation. Energy waste from components rises in later years for the same reason. The replacement interval for a retired EV battery module is around 2.5 years. The integrated system proves feasible for off-grid combined power and heat supply using solar energy and retired EV batteries as storage, though battery degradation impacts long-term performance.

关键词： Hybrid energy system Retired EV battery multi -stage control strategy multi -objective optimization NSGA-II

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Wastewater treatment process enhancement based on multi-objective optimization and interpretable machine learning

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 364卷 121430页

作者： Liu, Tianxiang Zhang, Heng Wu, Junhao Liu, Wenli Fang, Yihai Huazhong Univ Sci & Technol Natl Ctr Technol Innovat Digital Construct Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Hubei Peoples R China Monash Univ Dept Civil Engn Clayton Vic 3800 Australia

optimization and control of wastewater treatment process (WTP) can contribute to cost reduction and efficiency. A wastewater treatment process multi-objective optimization (WTPMO) framework is proposed in this paper to provide suggestions for decision-making in setting parameters of WTP. Firstly, the prediction models based on Extreme Gradient Boosting (XGB) with Bayesian optimization (BO) are developed for predicting effluent water quality (EQ) and energy consumption (EC) for different influent quality and process parameter settings. Then, the SHapley Additive exPlanations (SHAP) algorithm is used to complement the interpretability of machine learning to quantitatively evaluate the impact of different features on the predicted targets. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Technique for Ordering Preferences on Similarity of Ideal Solutions (TOPSIS) is introduced to solve and make decisions on the multi-objective optimization problem. The WTPMO applicability is validated on Benchmark Simulation Model 1 (BSM1). The results show that BOXGB achieves accurate prediction for EQ and EC with R2 values of 0.923 and 0.965, respectively, indicating that BO can effectively select the model hyperparameters in XGB. Based on SHAP supplemented the interpretability of the model to fully explain how the influent water quality and decision variables affect the EQ and EC of the WTP. In addition, the optimized process parameters are determined based on NSGA-II and TOPSIS, and the EC optimization rate is 1.552% while guaranteeing water quality compliance. Overall, this research can effectively achieve the optimization of WTP, ensure that the effluent water quality meets the standards while reducing energy consumption, assist Wastewater treatment plants (WWTPs) to achieve more intelligent and efficient operation and maintenance management, and provide strong support for environmental protection and sustainable development goals.

关键词： Wastewater treatment plants Interpretable machine learning multi -objective optimization Bayesian optimization

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multi-objective optimization of machining parameters in complete peripheral milling process with variable curvature workpieces

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JOURNAL OF MANUFACTURING PROCESSES 2024年 117卷 95-110页

作者： Li, Chunxiao Zhao, Guoyong Meng, Fanrui Yu, Shuo Yao, Baicheng Liu, Hao Shandong Univ Technol Sch Mech Engn Zibo 255000 Peoples R China Zibo Water Ring Vacuum Pump Factory CO LTD Zibo 255000 Peoples R China

The mechanism of peripheral milling with variable curvature has yet to be revealed. There is an urgent need to develop a composite optimization approach for the full milling process, including rough and fine machining. The influence of curvature on the energy consumption mechanism in peripheral milling is investigated in this paper. Firstly, the geometric parameters of peripheral milling are analyzed, and the material removal rate calculation method with variable curvature is proposed. The effects of workpiece curvature, machining parameters, and tool wear on energy consumption and surface quality are revealed. Secondly, prediction models for machine tool specific energy and surface roughness are developed in accordance with the examined material removal mechanism. Then, the carbon emission calculation method for the complete peripheral milling process is put forward based on the developed energy consumption mechanism. Finally, a genetic algorithm with elite strategy is used to optimize the multi-dimensional and multi-objective machining parameters. The objective is to achieve the maximum machining efficiency, minimize carbon emissions, and attain excellent surface quality during the complete milling process. The research aims to concurrently optimize machining parameters for rough and fine machining processes, thereby enabling effective and environmentally friendly peripheral milling while maintaining machining quality. The determination coefficient R2 for the proposed model exceeds 0.97, while the average accuracy of predictions surpasses 96 %. The research holds particular importance in providing guidance for peripheral milling operations involving workpieces with variable curvature.

关键词： Specific energy Surface roughness Carbon emission Peripheral milling multi -objective optimization Variable curvature

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multi-objective optimization and performance improvement of CD nozzle design parameters for cold spray coating process using RSM, ANN and GA

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MATERIALS TODAY COMMUNICATIONS 2024年 40卷

作者： Mehmood, Ayaz Zunaid, Mohammad Madan, Ashok Kumar Mewat Engn Coll Waqf Dept Mech Engn Nuh 122107 Haryana India Delhi Technol Univ Dept Mech Engn Bawana Rd Delhi 110042 India

This study presents the multi-objective optimization of the convergent-divergent (CD) nozzle for the cold spray (CS) process and investigates the impact of five key nozzle design parameters. A 2D axisymmetric baseline nozzle was optimized through face central composite design (FCCD), computational fluid dynamics (CFD) simulations, response surface methodology (RSM), artificial neural networks (ANN), and genetic algorithms (GA). The nozzle performance in the CS process advances the manufacturing technologies by significantly optimizing the four response variables. Comparative analysis shows that RSM models (R2 = 0.9893) were more accurate on average than ANN models (R2 = 0.9848), although ANN provided a deeper understanding of complex interactions with an overall correlation coefficient of 0.99191. Sensitivity analysis suggested that increasing convergent length (Lc) increases particle velocity (PV) before it stabilizes while particle temperature (PT) decreases due to cooling effects. Divergent length (Ld) increases PV to an optimal range, but reduces PT, indicating energy loss. Smaller injector inner diameter (Did) enhances PV but reduces PT. Injector length (Li) increases PV to a certain limit, beyond which benefits stabilizes, and also increases surface pressure (SP). Expanding the exit diameter (De) increases PV, drops PT and heat flux (HF), and reduces SP, indicating decreased flow resistance. optimization with RSM coupled with numerical simulation (RSM-DF) improved PV, PT, HF, and SP by 9.1 %, 4.4 %, 17.9 %, and 27.6 % respectively. Further optimization with a 5-6-4 ANN and GA (NSGA-II) improved PV by 5 %, PT by 5.4 %, HF by 15.7 %, and reduced SP by 20.9 %. Integrating CFD, RSM, ANN, and GA provides an effective approach for optimizing CD nozzle parameters.

关键词： Cold spray CD nozzle Computational methods Response surface methodology multi -objective optimization Performance evaluation

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Modeling and multi-objective optimization of a stand-alone photovoltaic-wind turbine-hydrogen-battery hybrid energy system based on hysteresis band

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第22期48卷 7959-7974页

作者： Li, Zhe Liu, Yuhan Du, Mingpu Cheng, Yonghong Shi, Le Xi An Jiao Tong Univ Ctr Nanomat Renewable Energy Sch Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China

Hybrid renewable energy system (HRES) can provide power without emission for off-grid areas. Due to intermittency of renewable energy, energy storage system (ESS) is essential for reliable power supply, while its cost is still relatively high. Appropriate power management strategy (PMS) can help to delay the degradation of energy storage devices and reduce the system cost. In this study, power management strategy and configuration optimization of the system are focused and the study includes three main contributions. First, mathematical models of the system, including photovoltaics (PVs), wind turbines (WTs), batteries, fuel cells (FCs), electrolyzers (ELZs), and hydrogen tanks are developed. The degradation of fuel cells and electrolyzers is considered in the modeling process. Second, power management strategy considering hysteresis band is employed to control energy flow to delay fuel cell and electrolyzer degradation. Third, a multi-objective optimization function including the system net annual value (NAV), loss of power supply probability (LPSP) and excess energy (Eexcess) is established. Non-dominating Sorting Genetic Algorithm II (NSGA-II) is used to solve objective function. The results demonstrate that using hysteresis band help improve the system performance and reduce the cost. In addition, by setting the goal of excess energy, system reliability is well preserved with a LPSP as low as 0.92%. Compared with other optimization algorithms such as MOEA/D, NSGA-II has a smaller SI value of 422.10 and a larger DI value of 830.78, therefore the Pareto solution obtained by NSGA-II has a more uniform distribution and larger coverage.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hybrid renewable energy system Hysteresis band Degradation multi -objective optimization NSGA-II

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