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Determination of Optimal Location and Capacity of Detention Facilities

Procedia Engineering 2014年 70卷 1037-1045页

作者： S. Lim V.H. Ho S.Y. Lee D.G. Yoo J.H. Kim Sch. Of Civil. Environmental & Architecture Engineering Coll. of Engineering Korea Univ Anamdpng Seongbuk-gu Seoul 136-713 Korea

The importance of the effective distribution of runoff in sewer network throughout a detention facility has recently been recognized. This study aims to determine the location and the capacity of detention facilities in urban sewer network. The optimal location and capacity of the detention facilities can be determined by numerous iterations of an optimization model. In this paper, an interfacing Visual Basic (VB) programming tool with a SWMM (Storm Water Management Model) DLL for hydraulic and hydrological computation in sewer network completes the optimization routine. A multi-objective genetic algorithm (MOGA) is adopted to minimize both the total overflow and the installing cost. The developed model is applied to S city in South Korea. The goal in prevention performance of urban disaster is used as rainfall. The model will help engineers find effective alternatives to achieve the goal of the urban disaster prevention performance.

关键词： optimal location and capacity detention facility goal in prevention performance of urban disaster multi-objective genetic algorithm

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multi-objective optimization on structural parameters of torsional flow heat exchanger

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APPLIED THERMAL ENGINEERING 2019年 161卷

作者： Gu, Xin Wang, Tongtong Chen, Weijie Luo, Yuankun Tao, Zhilin Zhengzhou Univ Key Lab Proc Heat Transfer & Energy Saving Henan Zhengzhou 450002 Henan Peoples R China CNTC Zhengzhou Tobacco Res Inst Zhengzhou 450001 Henan Peoples R China

Torsional flow heat exchanger is a new type of shell-and-tube heat exchanger with high heat transfer efficiency and low flow resistance. In this paper, the holistic model of the torsional flow heat exchanger is established, and the pressure drop, heat transfer coefficient and thermal performance factor in the shell-side are numerically studied. In order to achieve a better heat transfer performance in a torsional flow heat exchanger, the multi objective structure optimization on the baffle was carried out by using the method of response surface optimization. The results show that the shell-side pressure drop is significantly affected by the angle and width of the baffle;however, for the heat transfer performance, such as the heat transfer coefficient, is mainly attributed to the inclination angle. Three parameters were fitted with the multi-objective genetic algorithm in Ansys, with which the heat transfer performance of the torsional heat exchanger is improved by the value of 15.17%. This study provides a novel method to estimate and optimize the structure of the shell-and-tube heat exchanger.

关键词： Shell-and-tube heat exchanger Torsional flow Structural parameter multi-objective genetic algorithm Response surface method Numerical simulation

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Optimized Design of Neural Networks for a River Water Level Prediction System

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SENSORS 2021年第19期21卷 6504页

作者： Lineros, Miriam Lopez Luna, Antonio Madueno Ferreira, Pedro M. Ruano, Antonio E. Univ Seville Design Engn Dept Seville 41013 Spain Univ Seville Aerosp Engn & Fluid Mech Dept Seville 41013 Spain Univ Lisbon Dept Informat LASIGE Fac Ciencias P-1749016 Lisbon Portugal Univ Algarve Fac Sci & Technol P-8005139 Faro Portugal Univ Lisbon Inst Super Tecn IDMEC P-1049001 Lisbon Portugal

In this paper, a multi-objective genetic algorithm (MOGA) framework for the design of Artificial Neural Network (ANN) models is used to design 1-step-ahead prediction models of river water levels. The design procedure is a near-automatic method that, given the data at hand, can partition it into datasets and is able to determine a near-optimal model with the right topology and inputs, offering a good performance on unseen data, i.e., data not used for model design. An example using more than 11 years of water level data (593,178 samples) of the Carrion river collected at Villoldo gauge station shows that the MOGA framework can obtain low-complex models with excellent performance on unseen data, achieving an RMSE of 2.5 x 10(-3), which compares favorably with results obtained by alternative design.

关键词： multi-objective genetic algorithm artificial neural networks river stage data

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An integrated multicriteria decision-making approach for distribution system expansion planning

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第9期36卷 4962-4989页

作者： Rosa Mussoi, Fernando Luiz Ghizoni Teive, Raimundo Celeste Fed Inst Santa Catarina IFSC Dept Elect Engn Florianopolis SC Brazil Vale Itajai Univ UNIVALI Appl Intelligence Lab Itajal Brazil

The project selection problem in an electric distribution utility, which is part of the network expansion planning task, requires a formal process of prioritizing some projects since the budget is limited. A complete solution to this problem, involving optimization and a decision-making model, is still a few explored in the literature. We proposed an integrated multicriteria decision-making methodology, including optimization and solutions ranking. The optimization module finds a set of Pareto-optimal portfolios, using the multi-objective genetic algorithm nondominated sorting genetic algorithm-II to select and schedule the reinforcement projects in a multistage planning horizon. The proposed decision support module ranks the project portfolios based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Simple multi-Attribute Rating Technique (SMART), considering the decision-maker profile embedded into the model by the Rank-Order Centroid (ROC) and Analytic Hierarchy Process (AHP) criteria weights. This paper tackles this methodology's theoretical foundations, taking into account some essential attributes, such as power quality and operational performance indexes, the number of consumers, and projects' financial impacts. We presented realistic case studies to show how portfolios are composed, not only when all nine attributes of projects are included in the optimization module, but also when the criteria weights are changed in the decision support module. This paper's main contribution lies in proposing a unique model to find optimal expansion plans and rank these project portfolios, considering the decision-maker profile adherent to its goals. About the decision-making methods applied in this problem, we infer that the TOPSIS performance overcomes SMART in all analyzed problems and the ROC and AHP weights indicate the same alternative in most of the analyzed cases.

关键词： distribution expansion planning multi-objective genetic algorithm multiple attribute decision-making Pareto optimization portfolio management

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Altitude and roll control of a hovering quad-rotor air vehicle using the multi-objective approximate optimization of proportional-integral-differential control

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ENGINEERING OPTIMIZATION 2017年第10期49卷 1704-1718页

作者： Yoon, Jaehyun Lee, Jongsoo Yonsei Univ Sch Mech Engn Seoul South Korea

This study explores the optimal proportional-integral-differential (PID) gains of a hovering quad-copter to allow recovery from disturbed altitude and roll positions. Computational fluid dynamics was used to determine the rotor distance and the blade shape parameters for maximizing the hovering thrust. Using a six-degree-of-freedom quad-copter dynamics model, a control algorithm was then used to obtain PID gains. The PID control was approximated using back-propagation neural networks (BPNs). Position control of the quad-copter model was performed by determining the optimal PID gains required to minimize the control duration for altitude and roll. The non-dominated sorting genetic algorithm (NSGA-II) was used for multi-objective optimization and a BPN was used for meta-modelling the PID control. The PID gains generated from bi-objective optimal designs were compared with the initial design. The results confirmed that the recovery time from an unbalanced position was reduced and that the motion of the quad-copter was better stabilized.

关键词： Quad-rotor air vehicle altitude and roll control back-propagation neural network multi-objective genetic algorithm

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Structural optimization of phase change material based heat exchanger for breathing air cooling

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APPLIED THERMAL ENGINEERING 2021年 191卷

作者： Lv, You Huang, Yawen Duan, Qiyao Xiao, Jie Feng, Naiyi Zhu, Yuan Chen, Tinghan Zhou, Shaoxin Southern Univ Sci & Technol Sch Microelect Shenzhen 518055 Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst TBSI Shenzhen 518005 Peoples R China Southern Univ Sci & Technol Sch Innovat & Entrepreneurship Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Key Lab Energy Convers & Storage Technol Minist Educ Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Engn Res Ctr Integrated Circuits Next Generat Com Minist Educ Shenzhen 518055 Peoples R China Peking Univ Sch Life Sci Beijing 100871 Peoples R China Yantai Nanshan Univ Coll Engn Yantai 265713 Peoples R China

The enormous amount of heat in fires can bring the inhaled air temperature to an extremely high level, which is fatal to the civilians. However, conventional rescue respirators are unable to control the breathing air temperature. In our previous work, a phase change material-based heat exchanger has been designed to cool down the inlet air from 500 K to a breathable 313 K and sustaining for more than 20 min. As a part of wearable respirator, its wearing comfort must be considered by minimizing its pressure drop and keeping it lightweight. Hence, the design is modified by proposing a cooling module with twin heat exchangers and the structural design of the heat exchangers is optimized by response surface methodology and multi-objective genetic algorithm. The optimized design can offer the required cooling performance with up to 65% lower breathing resistance and 30% lighter weight, which is beneficial for the further applications.

关键词： Heat exchanger Breathing air cooling Structure optimization Response surface method multi-objective genetic algorithm

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Scheduling of virtual manufacturing cells with outsourcing allowed

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2014年第12期27卷 1079-1089页

作者： Arkat, Jamal Ghahve, Homa Univ Kurdistan Dept Ind Engn Sanandaj Iran

This paper presents a bi-objective mixed integer programming model for operations scheduling in virtual manufacturing cells where outsourcing is allowed, and set-up times are considered to be sequence dependent. Two objective functions of the model are the minimisation of the maximum completion time (or makespan) and the minimisation of the total cost of inter and intra-plant transportation. Two multi-objective solution algorithms are then developed to solve the proposed model. The first algorithm is an epsilon-constraint method which can find the exact set of efficient solutions for small-size problems. Since the investigated problem is non-polynomial (NP) hard, exact algorithms cannot be used for large-scale real-world cases and, therefore, a bi-objective genetic algorithm (GA) is developed as the second algorithm. A numerical example is given to evaluate the effectiveness of the proposed model and solution algorithms. The results reveal the superiority of the proposed model over the base models and demonstrate that in comparison with the epsilon-constraint method, the proposed GA can obtain efficient solutions in much less computational time.

关键词： virtual manufacturing cell scheduling outsourcing multi-objective genetic algorithm epsilon-constraint method

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Optimization of rapeseed production using exergy analysis methodology

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 43卷

作者： Esmaeilpour-Troujeni, Mahdi Rohani, Abbas Khojastehpour, Mehdi Ferdowsi Univ Mashhad Dept Biosyst Engn Mashhad Razavi Khorasan Iran

The exergy analysis helps to determine the energy losses of different inputs through identifying the actual amount of energy consumed in a process. In this regard, a new approach was applied in this study to predict and optimize the rainfed production systems (RPS) and irrigated production systems (IPS) of rapeseed in the north of Iran, by using exergy analysis methodology. Two indices of cumulative degree of perfection (CDP) and renewability index (RI) were used to evaluate the sustainability of systems. The Support Vector Machine (SVM) and multi-objective genetic algorithm (MOGA) were also employed to predict and optimize the rapeseed production systems, respectively. In the study of conventional system, CDP and RI were determined to be 2.19 and 0.72, on average, respectively, in which, the values of both indices were higher in the RPS than IPS. Optimization results indicated that by reducing the consumption of irrigation water, electricity and biocides as well as increasing the use of chemical fertilizers, farmyard manure and diesel fuel, the yield increased by 24.55%. Sustainability assessment in optimal scenario revealed that CDP and RI increased to 2.75 and 0.81, respectively. As a result, under optimal conditions, the production process becomes more environmentally friendly.

关键词： Exergy multi-objective genetic algorithm Optimization Rapeseed Sustainability

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A heuristic solution and multi-objective optimization model for life-cycle cost analysis of solar PV/GSHP system: A case study of campus residential building in Korea

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 47卷

作者： Sim, Minjeong Suh, Dongjun Kyungpook Natl Univ Dept Convergence & Fus Syst Engn Sangju 37224 South Korea

Because of the unavailability of standard renewable power generation capacity and the ambiguous sales methods for each building type, there has been indiscriminate installation of renewable energy sources to increase the mandatory installation rate of renewable energy. Importantly, campus buildings are high energy-consuming buildings that have considerable energy-saving potential. Herein, we present a model that maximizes the total life cycle cost (LCC) of a building using energy simulation programs (EnergyPlus & DesignBuilder) by considering various costs and efficiencies depending on the capacity and efficiency of the photovoltaic (PV) and ground-source heat pumps (GSHP) systems and optimization analysis using heuristic solution and multi-objective genetic algorithm. Based on economic factors, a Korean campus residential building was used as a case study, and the expense of the optimization models of seven applicable scenarios were analyzed. Each scenario is a method for selling renewable energy power, and it is possible to determine the building type and the effect of the sales method by analyzing the results of these scenarios. This method could be used to develop installation guidelines for integration of renewable energy systems into newly built buildings and provide the basis for decision-making by studying retrofitting of existing buildings to enhance energy efficiency.

关键词： Optimization PV GSHP LCC multi-objective genetic algorithm

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A Method for Design of a Growing Complex Network

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ELECTRONICS AND COMMUNICATIONS IN JAPAN 2014年第1期97卷 70-81页

作者： Mizuno, Haruki Okamoto, Takashi Koakutsu, Seiichi Hirata, Hironori Chiba Univ Chiba Japan Chiba Univ Grad Sch Engn Chiba Japan

A complex network design method that finds a desired network structure can be a powerful tool in large-scale system design. Conventional complex network design methods tackle only static networks, that is, they do not consider growth of the target network. In this paper, we propose a new method for the design of a growing complex network. First, we consider evaluation functions which quantitatively represent the characteristics of desired structures by using feature quantities. Then, we formulate the design problem of a growing complex network as a multi-objective optimization problem in order to determine the connection targets of new nodes by using the evaluation functions. By solving the problem, we grow the network, thus obtaining the desired network. We try to generate networks which have the desired clustering coefficient and average path length concurrently. Through numerical experiments, we confirm that the proposed method is effective as a method for the design of growing complex networks. (c) 2013 Wiley Periodicals, Inc. Electron Comm Jpn, 97(1): 70-81, 2014;Published online in Wiley Online Library (***). DOI 10.1002/ecj.10382

关键词： complex network multi-objective optimization growing network network design multi-objective genetic algorithm

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