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Robust multi-objective optimization of methanol steam reforming for boosting hydrogen production

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2021年第58期46卷 29795-29811页

作者： Bayat, M. Asil, A. Garmroodi Univ Bojnord Dept Chem Engn Fac Engn Bojnord Iran

Methanol steam reforming (MSR) has been considered as a promising method for pro-ducing pure hydrogen in recent decades. A comprehensive two-dimensional steady-state mathematical model was developed to analyze the MSR reactor. To improving high purity hydrogen production, a triple-objective optimization of the MSR reactor is per-formed. Non-dominated Sorting Genetic algorithm-ii (nsga-ii) is employed as a robust optimization approach to maximize the three objectives, termed as, methanol conver-sion, CO selectivity, and H2 selectivity. The Pareto optimal frontier has also been pro-vided and the ultimate solution of the Pareto front has been found by the three decision-making methods (TOPSIS, LINMAP, and Shannon's Entropy). Among the three distinct decision-making approaches, LINMAP presents better results according to the deviation index parameter. It has been shown that a perfect agreement is available between the plant and simulation data. Operating under the optimum values based on the LINMAP method confirms an almost 47.04% enhancement of H2 mass fraction compared to the conventional industrial MSR reactor. The predicted results advocate that the key superiority of the optimized-industrial reactor is the remarkable higher production rate of hydrogen compared to the conventional MSR reactor which makes optimized-industrial reactor both feasible and beneficial. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hydrogen production Methanol steam reforming Two-dimensional model Multi-objective optimization nsga-ii algorithm Decision-making approach

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THE OPEN LOCATION-ROUTING PROBLEM FOR MULTI-OBJECTIVE OPTIMIZATION OF SUSTAINABLE SUPPLY CHAIN CONSIDERING SOCIAL CONCERNS

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2023年第10期19卷 7423-7446页

作者： Eydi, Alireza Mansouri, Zahra Univ Kurdistan Fac Engn Sanandaj Iran Univ Kurdistan Ind Engn Sanandaj Iran

Taking into account the variety of real-world constraints and in-corp orating them into classical problems are essential in designing supply chain networks. Meanwhile, given the importance of outsourcing, the open location-routing problem (OLRP) is proposed to fulfill customers' demands and mini-mize costs by determining the optimal location of vehicle facilities and routes. In addition, supply chain sustainability is attracting increasing attention since it promotes a bright future for the coming generations by striking a balance between economic, environmental, and social performances in supply chain design. In this study, we contribute to the scarce literature on supply chain sustainability and OLRP. First, using OLRP, we model a sustainable supply chain that addresses all sustainability pillars. Second, we use the nsga-ii meta-heuristic method to solve the proposed model and finally compare its ef-ficiency with Cplex. The model characterized in this study can be considered a basic reference on the subject of supply chain sustainability and OLRP. More-over, we indicate that both of the proposed solution methods are capable of solving the developed model;however, the meta-heuristic method, nsga-ii, is significantly more efficient than the classical one, Cplex, in terms of compu-tation time, particularly in solving medium-and large-scale problems.

关键词： Sustainable supply chain management environmental and social im-pacts multi-objective optimization open location-routing nsga-ii algorithm

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Optimization of Passenger and Freight Collaborative Transportation for Urban Rail Transit under Virtual Coupling Condition

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ENGINEERING LETTERS 2024年第1期32卷 179-192页

作者： Hou, Zunzun He, Ruichun Liu, Chenning Wan, Shixiang Yang, Keqiang Dai, Cunjie Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China Wuxi Metro Wuxi 214000 Peoples R China

Urban rail transit has the potential to utilize surplus capacity to carry out urban freight transportation during off-peak periods. Based on the characteristics of virtual coupling operation and organization mode, this paper comprehensively considers the competition between passenger flow and freight flow, takes the train stopping plan and train carriage allocation as the main decision variables, and takes the minimization of passenger waiting time and the maximization of average full load rate as dual objectives to construct a multi-objective optimization model of passengerfreight collaborative transport scheme of urban rail transit under virtual coupling conditions. The multi-objective optimization model is developed to obtain the system optimization of the train stopping plan, running plan, carriage allocation plan, and passenger-freight collaborative transport plan. Numerical experiments are designed to verify the model's validity, using Wuxi Metro Line 3 as an example, and the nsga-ii algorithm solves all experiments. The results of the algorithm show that, compared to the traditional single passenger transport with a fixed large coupling, the scheme in this paper can simultaneously improve the average full capacity of the whole line by about 38.5% during the study period with only 0.65 minutes increase in the average passenger waiting time for the same number of rolling stocks, which can effectively shorten passenger travel time and improve the quality of passenger service in urban rail transit while ensuring transport efficiency.

关键词： Urban rail transit Virtual coupling trains Multi-objective optimization model nsga-ii algorithm

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Multi-objective optimization of chemical reaction conditions based on a kinetic model

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JOURNAL OF MATHEMATICAL CHEMISTRY 2019年第2期57卷 484-493页

作者： Koledina, K. F. Koledin, S. N. Karpenko, A. P. Gubaydullin, I. M. Vovdenko, M. K. RAS Inst Petrochem & Catalysis 141 Oktyabrya Prospect Ufa 450075 Bashkortostan R Russia Ufa State Petr Technol Univ Cosmonavtov Str 1 Ufa 450062 Bashkortostan R Russia Bauman Moscow State Tech Univ 2nd Baumanskaya Str 5 Moscow 105005 Russia

The main purpose of the study is to introduce the multi-objective optimization using Pareto approximations to problems of chemical kinetics. We report the setting up and solution of the multi-objective optimization problem for conditions of a chemical reaction on the basis of a kinetic model. The study addresses the reaction of alcohols with dimethyl carbonate catalyzed by cobalt or tungsten carbonyl. The objective functions for optimization of chemical reaction conditions based on a kinetic model are presented. The nsga-ii algorithm was applied to determine the Pareto set and front for the multi-objective optimization problem applied to the reaction of alcohols with dimethyl carbonate for two catalysts, which make it possible to find the compromise values of variable parameters providing extrema of the objective functions.

关键词： Multi-objective optimization Reaction of dimethyl carbonate with alcohols catalyzed by metal complexes Kinetic model Economic criteria Variable parameters Pareto approximations nsga-ii algorithm Pareto front Pareto set

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Application of improved nsga-ii model in multi-objective optimization model for sustainable building construction projects

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第3期24卷 1679-1694页

作者： Hu, Fengyu Zhengzhou Urban Construct Vocat Coll Dept Architecture & Civil Engn Zhengzhou Henan Peoples R China

Sustainable and environmentally friendly construction is the way of the future for building projects, and it is a practical illustration of how sustainable development is being implemented in the construction sector, where the control objectives are interdependent and constricting. However, when green construction is carried out, a conundrum arises since important factors like cost, environmental preservation, and safety cannot be addressed simultaneously. This issue limits the promotion of green construction. In order to solve the obstacles in the green construction process, the study chose to introduce three objectives to establish a multi-objective optimization model for project optimization. The local search idea in the mountain climbing algorithm was introduced into the non controlled sorting genetic algorithm to improve it, and a green construction multi-objective optimization model was established. Experimental verification of the feasibility and efficiency of improving non controlled sorting genetic algorithms;And evaluate and solve the established sustainable construction project. The results represent the maximum optimization values for each objective. The construction period is mainly distributed between 183-245 days, the cost distribution is between 16.6855 million yuan and 200861 million yuan, the quality distribution is between 0.864 and 0.878, the safety distribution is between 0.874 and 0.999, the environmental distribution is between 133.76 and 190.72, and the resource distribution is between 0.834 and 0.999, all of which meet the standards. Provide theoretical solutions for managers to manage green construction projects.

关键词： nsga-ii algorithm hill climbing searching environmental construction multi-objective model project management

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Simulation and evaluation of tomato growth by AquaCrop model under different agricultural waste materials

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International Journal of Agricultural and Biological Engineering 2024年第5期17卷 112-119页

作者： Changquan Zhou Wenju Zhao Haolin Li Feng Ma Keqian Wu College of Energy and Power Engineering Lanzhou University of TechnologyLanzhou 730050China Key Laboratory of Smart Agriculture Irrigation Equipment Ministry of Agriculture and Rural AffairsLanzhou 730050China College of Civil Engineering Lanzhou University of Information Science and TechnologyLanzhou 730300China College of Environmental Science and Energy Engineering Beijing University of TechnologyBeijing 100124China College of Water Resources and Civil Engineering China Agricultural UniversityBeijing 100083China

This study aimed to enhance the utilization of agricultural waste and identify the most suitable agricultural waste materials for tomato cultivation. It utilized a locally modified substrate labeled as CK, along with five different groups of agricultural waste materials, designated as T1 (organic fertilizer: loessial soil: straw in a ratio of 4:5:1), T2 (organic fertilizer: loessial soil: straw: grains in a ratio of 3:5:1:1), T3 (organic fertilizer: loessial soil: straw: grains in a ratio of 2:5:1:2), T4 (organic fertilizer:loessial soil:straw:grains in a ratio of 1:5:1:3), and T5 ( loessial soil:straw:grains in a ratio of 5:1:4), the AquaCrop model was employed to validate soil water content and tomato growth and yield under these treatments. Furthermore, a multi-objective genetic algorithm was employed to determine the optimal agricultural waste materials that would ensure maximum tomato yield, water use efficiency (WUE), partial factor productivity of fertilizer (PFP) and sugar-acid ratio. The results indicated that the AquaCrop model reasonably simulated volumetric soil water content, tomato canopy cover, and biomass, with root mean square error (RMSE) ranges of 20.0-69.4 mm, 15.2%-25.1%, and 1.093-3.469 t/hm2, respectively. The CK group exhibited an R-squared (R2) value of 0.63 for volumetric soil water contents, while the ratio scenarios showed R2 values exceeding 0.80. The multi-objective genetic optimization algorithm identified T5 as the optimal ratio scenario, resulting in maximum tomato yield, WUE, PFP, and quality. This study offers a theoretical foundation for the efficient utilization of agricultural wastes and the production of high-quality fruits and vegetables.

关键词： agricultural waste materials AquaCrop model nsga-ii algorithm tomato quality

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Simulation-optimization approach for the multi-objective production and distribution planning problem in the supply chain: using nsga-ii and Monte Carlo simulation

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SOFT COMPUTING 2022年第17期26卷 8661-8687页

作者： Kabiri, Niloofar Nadim Emami, Saeed Safaei, Abdul Sattar Babol Noshiravani Univ Technol Dept Ind Engn Babol *** Iran

With the growth of multinational companies, increasing international and domestic competition between companies, upgrading information technology, and increasing customer expectations, accurate supply chain (SC) planning is essential. In such an environment, pollution has become more severe in recent decades, and with the weakening of the environment and global warming, green SC management strategies have become significant issues in recent decades. In this research, we consider the integrated production and distribution planning problem of a multi-level green closed-loop SC system, which includes multiple recycling, manufacturing/ remanufacturing, and distribution centers. We present a three-level bi-objective programming model to maximize profit and minimize the amount of greenhouse gas emissions. A hierarchical iterative approach utilizing the LP-metric method and the non-dominated sorting genetic algorithm (nsga-ii) is introduced to solve the proposed model. The Taguchi approach is applied to find optimum control parameters of nsga-ii. Moreover, Monte Carlo (MC) simulation is applied to tackle uncertainty in demand, and the nsga-ii algorithm is integrated with MC simulation (MCnsga-ii). Through numerical experiments on randomly generated instances, we observe that the average of the relative gaps nsga-ii and LP-metric method are 7%, 7%, and 1% for the three levels, respectively. Furthermore, it is observed that the value of convergence (C) and spacing (S) metrics of the MCnsga-ii algorithm are 0 and 1.82E + 07, which are better than nsga-ii.

关键词： Production and distribution planning Simulation-optimization Green closed-loop supply chain Monte Carlo simulation Three-level bi-objective programming nsga-ii algorithm

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A multi-objective optimization model of urban passenger transportation structure under low-carbon orientation considering participating subjects

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第54期30卷 115839-115854页

作者： Li, Xinguang Zhan, Jun Pan, Fuquan Lv, Tong Wang, Shen Qingdao Univ Technol Sch Mech & Automot Engn Qingdao 266000 Shandong Peoples R China

The optimization of urban passenger transport structure can effectively save energy and reduce the carbon emission of transport. However, the carbon emission and energy consumption generated by the construction of transport projects are worthy of attention. In this paper, a multi-objective optimization model of urban passenger transport structure oriented to low carbon is proposed considering passenger, operator, and construction. The optimal solution of the model is obtained based on the nsga-ii algorithm, and the validity of the model is verified with a case of Qingdao. The optimal ratio of Qingdao passenger traffic structure considering only the passenger perspective (PS), considering the passenger and operator (POS), and considering the three parties together (POCS) is obtained, respectively. The results show that the optimal structures obtained by the PS, POS, and POCS models increase the public transport passenger share by 12.74%, 20.74%, and 23.70%, compared with the actual values. From both the supply and demand sides, there has been an increase in the passenger share of public transport. The POS model is more suitable for solving structural optimization problems that do not involve construction carbon emissions in the short term. The POCS model is more suitable for long-term comprehensive structural optimization problems. The results of the study provide a reference basis for optimizing the urban passenger transport structure.

关键词： Low-carbon transportation Urban passenger transportation structure Multi-objective optimization model Traffic carbon emission nsga-ii algorithm Participating subject

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Investigation of a falling film tube bank heat exchanger with baffle design for water recovery applications

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Energy and Built Environment 2024年第5期5卷 817-828页

作者： Liang Tang Xiaoling Cao Wei Zhong Long Yu Linlin Yin Shandong Electric Power Engineering Consulting Institute Corp. LTD.Jinan 250013PR China School of Mechanical Engineering Southwest Jiaotong UniversityChengdu 610031PR China

Shell and tube heat exchangers(STHE)are essential thermal equipment and widely used in daily life.A novel thermosyphon system called falling-film thermosyphon(FFTS)is introduced and integrated into STHE system,resulting in a better thermal *** this study,a rectangular solid tube bank of FFTS bundles with a baffle design is *** numerical simulation for heat and mass transfer of the FFTS heat exchanger is developed to predict the condensation rate of the vapor in the flue gas,and a lab-scale prototype is also built up in *** prediction is validated with the experimental data from references,and the model’s accuracy is verified within 10%-12%***,the Non-dominated Sorting Genetic algorithm,version 2(nsga-ii)is implemented to improve the thermal performance of rectangular tube banks in this *** parameters,e.g.,baffle number,tube number,and tube space,are *** a result,compact configurations with more baffles are preferred to enhance the performance associated with a high-pressure drop *** optimized layout for the lab-scale prototype can increase by 18 to 32%condensation with a pressure loss of less than 200 Pa.

关键词： Falling film Heat transfer Condensation nsga-ii algorithm Heat exchanger

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Multi-fuzzy-objective graph pattern matching in big graph environments with reliability, trust and social relationship

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2020年第1期23卷 649-669页

作者： Li, Lei Zhang, Fang Zhang, Zan Li, Peipei Bu, Chenyang Hefei Univ Technol Key Lab Knowledge Engn Big Data Hefei Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China

With the advent of the era of big data, the scale of data has grown dramatically, and there is a close correlation between massive multi-source heterogeneous data, which can be visually depicted by a big graph. Big graph, especially from Web data, social networks, or biometric data, has attracted more and more attention from researchers, which usually contains complex relationships and multiple attributes. How to perform efficient query and matching on big graph data is the basic problem on analyzing big graph. Using multi-constrained graph pattern matching, we can design patterns that meet our specific requirements, and find matched subgraphs to locate the required patterns to accomplish specific tasks. So how to find matched subgraphs with good attributes in big graph becomes the key problem on big graph research. Considering the possibility that a node in a subgraph may fail due to reliability, in order to select more and better matched subgraphs, in this paper, we introduce fuzziness and reliability into multi-objective graph pattern matching, and then use a multi-objective genetic algorithm nsga-ii to find the subgraphs with higher reliability and better attributes including social trust and social relationship. Finally, a reliability-based multi-fuzzy-objective graph pattern matching method (named as RMFO-GPM) is proposed. The experimental results on real data sets show the effectiveness of the proposed RMFO-GPM method comparing with other state-of-art methods.

关键词： Reliability Graph Pattern Matching Multi-Fuzzy-Objective nsga-ii algorithm

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