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multi objective optimization using artificial neural network to maximize the power output of PEMFCs

INDIAN CHEMICAL ENGINEER 2024年第4期66卷 323-336页

作者： Ghosh, Sankhadeep Routh, Avijit Rahaman, Mehabub Ghosh, Avijit Jadavpur Univ Dept Chem Engn Kolkata India Heritage Inst Technol Dept Chem Engn Kolkata 700107 W Bengal India

Designing a PEM fuel cell model is exceedingly challenging because of its multivariate in nature. optimization is required to achieve highest operating condition. Neural Network Model is one of the possible methods to solve complex problems. The polarisation curve of a PEMFC (Proton Exchange Membrane Fuel Cell) is investigated in this paper in relation to the effects of seven parameters, including temperature, relative humidity in the cathode, relative humidity in the anode, anode stoichiometry, cathode stoichiometry, partial pressure of H2, and partial pressure of O2, using an ANN (artificial neural network) model. Where model geometric parameters i.e. Channel width, Channel depth, Channel length, Rib width, Cell width, GDL thickness, CL thickness, Membrane thickness of PEMFC was constant. Initially single objective Function (Output Power) is predicted. The research presented here makes predictions about a PEMFC stack's electrical performance under multiple operating conditions. Mathematical model was further verified using laboratory data. Co-efficient of Determination (R2), Mean Square Error (MSE), and Mean Absolute Error (MAE) was determined using the fuel cell stack voltage model and stack power model. The model results show the possibility of using ANN in the implementation of such models to predict the PEMFC system's steady-state behaviour. Highlights. Experimentally data useful for investigation and work on PEMFCs. ANN models to predict the steady state behaviour of the PEMFC system for different operating conditions. Single objective Function (Output Power Predicted). ANN-multi objective function is presented to predict Efficiency and output Power simultaneously. ANN-MOO model is Validated using Laboratory Dat

关键词： Proton exchange membrane (PEM) fuel cell (PEMFC) objective function co-efficient of determination (R-2) artificial neural network (ANN) multi objective optimization

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multi objective optimization based on SPEA for the microgrid energy dispatch

Multi objective optimization based on SPEA for the microgrid...

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第37届中国控制会议

作者： Xiaomin Wu Weihua Cao Dianhong Wang Min Ding School of Automation China University of Geosciences Hubei KEY Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Mechanical Engineering and Electronic Information China University of Geosciences

Microgrid provides an effective technical approach for distributed generation system connected to the power *** are renewable sources such as wind power, photovoltaic in the microgrid, which the power volatility of distributed generation limits the effective scheduling and real-time performance of microgrid. Energy storage device is used to stabilize the volatility of distributed generation, make sure the feasibility of distributed generation optimal scheduling. Microgrid optimizes scheduling the energy allocation od various types of distributed generation in technology, economy, environment and other aspects. In order to solutio the multi objective optimization of microgrid energy allocation, a Strength Pareto Evolutionary Algorithm based optimal method was proposed. Finally, the correctness and feasibility of the proposed method were verified through the simulation.

关键词： Microgrid Distributed generation multi objective optimization Strength Pareto Evolutionary Algorithm

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An Efficient multi objective optimization Approach for Robust Tuning of Power System Stabilizers

An Efficient Multi Objective Optimization Approach for Robus...

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National Power Systems Conference (NPSC)

作者： Krishan, Ram Verma, Ashu Mishra, Sukumar Indian Inst Technol Ctr Energy Studies New Delhi India Indian Inst Technol Dept Elect Engn New Delhi India

ISBN: (纸本)9781467399685

This paper presents the robust tuning of power system stabilizer (PSS) with modified harmony search (MHS) algorithm. The problem of selecting appropriate design parameters of PSS is formulated as an optimization problem with minimization of eigenvalue based multi objective function. This objective function is optimized to alleviate the poorly damped or unstable oscillatory modes of the power system in order to meet the guaranteed stability. The proposed design of PSS under large disturbances includes the Integral of Time Square Error (ITSE) index in the objective function. The performance of proposed modified harmony search based PSS (MHSPSS) and robust modified harmony search based PSS (RMHSPSS) under different operating conditions and disturbances are investigated for Single Machine Infinite Bus (SMIB) system. The effectiveness of the approach is demonstrated with eigenvalue analysis and nonlinear time domain simulations for the given system.

关键词： Power system stabilizer modified improved harmony search dynamic stability power system operation and control multi objective optimization

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Solving multi objective optimization problems using particle swarm optimization

Solving multi objective optimization problems using particle...

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Congress on Evolutionary Computation (CEC)

作者： Zhang, LB Zhou, CG Liu, XH Ma, ZQ Ma, M Liang, YC Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)0780378040

An algorithm for solving multi objective optimization problems is presented based on PSO through the improvement of the selection manner for global and individual extremum. The search for the Pareto Optimal Set of multi objective optimization problems is performed. Numerical simulations show the effectiveness of the proposed algorithm.

关键词： particle swarm optimization multi objective optimization pareto optimal

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Compensation Regulation of Reservoir Groups Based on multi objective optimization 11

Compensation Regulation of Reservoir Groups Based on Multi O...

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11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yang, Liu Wang, Ni Xie, Jiancang Zhu, Jiwei Xian Univ Technol Fac Water Resources & Hydroelect Engn Xian Peoples R China

ISBN: (纸本)9781467376792

In order to search for a rational manner to compensate the effect of Heisonglin irrigation district after the operation of Tunzhuang reservoir which will be built, this paper established a joint water supply system and set several schemes. The improved artificial fish-swarm algorithm was used to solve problems and the ant colony algorithm was used to verify the solutions. The results showed that the efficiency of the irrigation in Heisonglin district should increase to 0.8, which was the same as the one in Tunzhuang irrigation district. The irrigation area of Tunzhuang reservoir could remain 6km(2). Qinzhuang reservoir and Nihegou reservoir supplied compensation to Heisonglin irrigation district. When the above-mentioned four reservoirs ran together, the available water for Heisonglin irrigation district reached 943.4x10(4)m(3)/a and the irrigation guarantee rate was 64.10% which was higher than the one before the operation of Tunzhuang reservoir. The optimal value of the ecology flow of Tunzhuang reservoir was 0.0517m(3)/s. The available water for living and irrigation of Tunzhuang reservoir were 161.24x10(4)m(3)/a and 111.23x10(4)m(3)/a respectively. Their guarantee rates were 94.23% and 80.29% respectively, both of which could meet the requirements of the targets. The final optimized scheme provided a good suggestion for the ecological operation of Tunzhuang reservoir and the healthy development of Heisonglin irrigation, which was beneficial for the harmonious utilization of water resources in the river basin.

关键词： multi objective optimization Artificial Fish Swarm Algorithm (AFSA) compensation regulation reservoir group

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multi objective optimization for object recognition

Multi objective optimization for object recognition

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2010 2nd International Conference on Education Technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

作者： Abdolhossein Alipoor Mehdi Fesharaki Science & Research Branch Islamic Azad University CE Department Tehran Iran Maleke Ashtar University CE Department Tehran Iran

ISBN: (纸本)9781424463671;9781424463688;9781424463701

Relevant with some important subjects like target recognition, sensor fusion systems can be considered as one of the main issues highlighting here. Environmental condition, target characteristic and sensor efficiency are three parameters which can impress on sensor value in target recognition so for recognizing targets, a group of sensors which have more recognition rates, must be selected intelligently. Utilizing many sensors to acquire the highest object recognition rate would have extra cost and decrease energy of mobile sensors rapidly. Therefore make a tradeoff between sensor numbers and object recognition rate would be imperatively. This paper attempts to design a multi objective optimization service by using optimization algorithm and neural network. This service specifies highest recognition rate for each distinct sensor numbers. We propose multi objective optimization algorithm to help accessing the best sensory configuration for a definite environment regarding to the environmental conditions, sensors performance, and object features. Our multi objective optimization algorithm has two functions. Genetic algorithm is used to perform as one of functions to specify object recognition rates of each sensor group. Neural network is used to perform as fitness function of each genetic algorithm chromosomes. Another function is sensor numbers determinant. Highest recognition rate and lowest sensor numbers are two objects which multi objective optimization algorithm wants to make a balance between them. We define 500 different scenarios for 6 different sensors in different conditions. Object recognition rate of each sensor is collected. These rates are used for neural networks training process. By defining new scenario and run multi objective optimization algorithm in this scenario, this algorithm makes a Pareto front between sensor numbers and object recognition rate. Finally this algorithm finds, by distinct numbers of sensor, which sensors by which recognition ab

关键词： Automatic Sensor Management Intelligent Sensor Selection Objects Recognition Rate multi objective optimization Genetic Algorithm Neural Network

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Refined Ranking Relations for multi objective optimization and Application to P-ACO 13

Refined Ranking Relations for Multi Objective Optimization a...

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15th Genetic and Evolutionary Computation Conference (GECCO)

作者： Moritz, Ruby L. V. Reich, Enrico Schwarz, Maik Bernt, Matthias Middendorf, Martin Univ Leipzig Fac Math & Comp Sci D-04109 Leipzig Germany

ISBN: (纸本)9781450319638

Two new ranking methods for solutions of multi objective optimization problems are proposed in this paper. Theoretical results show that both new ranking methods form a total preorder and are refinements of the pareto dominance relation. These properties make the ranking methods suitable for the selection of a subset of good solutions from a set of non-dominated solutions as needed by meta-heuristics. In particular, this is shown experimentally for a Population-based ACO that uses the ranking methods to solve a multi objective flow shop problem.

关键词： multi objective optimization Ranking Relations Ant Colony optimization Flow Shop Scheduling Problem

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Using multi objective optimization in term of position and area of the air outlet shaft in Showadan

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2023年 132卷

作者： Heidari, Aliakbar Peyvastehgar, Yaghowb Khoshkalam, Negar Univ Yasuj Dept Tech & Engn Yasuj Iran Islamic Azad Univ Dept Architecture & Urban Design Yasooj Branch Yasuj Iran

Using the geothermal energy is one of the best solutions that led to construction of underground spaces in different parts of the world. "Shovadan" is one of the underground buildings which can be seen in the southwestern regions of Iran, especially in the cities of Dezful and Shushtar. Shovadan consists of a main space called "Sahn" that is usually built at a depth of 8 to 12 m under the ground and is accessed by a stairs. Fresh air enter the Sahn through these stairs and exit through a vertical channel called the air outlet shaft (AOSh). The configuration of various components of the Shovadan have a great impact on the thermal performance and ventilation efficiency inside it separately. In this paper a multi-objective optimization method is used to find the best position and the most optimal cross-section area of the AOSh. First, one of the most common forms of the Shovadan in Dezful city was selected as the base sample;then based on the different positions and cross sectional areas of the AOSh, 45 case studies were extracted for analysis. In the next step, the case studies were simulated in CFD and the average temperature and velocity inside their Sahns were calculated. Finally, using EDT analysis and Genetic algorithm (GA), the most optimal position and cross-section area of the AOSh were determined. The results showed that the most optimal case for thermal comfort in summer is formed when the distance between the stairs and the AOSh is increased and the cross-sectional area of the AOSh is decreased.

关键词： Shovadan Underground spaces Natural ventilation Thermal performance CFD Air outlet shaft (AOSh) multi objective optimization

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MILP-based decomposition algorithm for dimensionality reduction in multi-objective optimization: Application to environmental and systems biology problems

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COMPUTERS & CHEMICAL ENGINEERING 2014年 67卷 137-147页

作者： Copado-Mendez, Pedro J. Guillen-Gosalbez, Gonzalo Jimenez, Laureano Univ Rovira & Virgili Dept Engn Quim E-43007 Tarragona Spain

multi-objective optimization has recently gained wider interest in different domains of engineering and science. One major limitation of this approach is that its complexity grows rapidly as we increase the number of objectives. This work proposes a computational framework to reduce the dimensionality of multi-objective optimization (MOO) problems that identifies and eliminates in a systematic manner redundant criteria from the mathematical model. The method proposed builds on a mixed-integer linear programming (MILP) formulation introduced in a previous work by the authors. We illustrate the capabilities of our approach by its application to two case studies: the design of supply chains for hydrogen production and the multi-objective optimization of metabolic networks. Numerical examples show that our method outperforms other existing algorithms for dimensionality reduction. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Dimensionality reduction multi objective optimization Enviromental system biology Divide-and-conquer MILP MOO

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multi-objective optimization of facility planning for energy intensive companies

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JOURNAL OF INTELLIGENT MANUFACTURING 2013年第6期24卷 1095-1109页

作者： Yang, Lei Deuse, Jochen Jiang, Pingyu TU Dortmund Univ Chair Ind Engn Dortmund Germany Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

Because of the energy shortage and energy price rise, energy efficiency becomes a worldwide hot spot problem. It is not only a problem about cost reduction, but also a great contribute to the environmental protection. However, the energy efficiency was always ignored in the past decades. In order to gain more benefit and become more competitive in the market, energy efficiency should be considered as an essential factor in early planning phase. To overcome these problems, a new approach, which introduces energy efficiency as a key criterion into the planning process, is presented in this article. An energy recovery network is built according to the analysis of process and product demands. Afterwards the energy loss of the whole system, transport performance and space demand are simultaneously taken into account with the purpose of finding good facility planning from both energy and economic aspects. Finally, a practical expanding case is used to validate the correctness and effectiveness of the proposed approach.

关键词： Energy efficiency Facility planning multi objective optimization Local search

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