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two-objective optimization of a transcritical carbon dioxide based Rankine cycle integrated with evacuated tube solar collector for power and heat generation

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APPLIED THERMAL ENGINEERING 2021年 182卷 116079-116079页

作者： Kizilkan, Onder Khanmohammadi, Shoaib Yamaguchi, Hiroshi Isparta Univ Appl Sci Fac Technol Dept Mech Engn TR-32200 Isparta Turkey Kermanshah Univ Technol Dept Mech Engn Kermanshah Iran Doshisha Univ Dept Mech Engn Kyoto 6100321 Japan

In this study, two-objective optimization of a solar-driven carbon dioxide (CO2) based transcritical Rankine cycle for power and heat generation is carried out. With Engineering Equation Solver (EES), the monthly thermodynamic performance of the integrated system is determined. Results show that the exergy rate of recovered heat in the winter is 0.029 kW, which is relatively low due to low solar intensity for the winter season (512 W/m(2)). The results revealed that the highest overall energy efficiency of the system takes place in July, and the highest overall exergy efficiency is calculated for August. The results of the parametric study indicate that changing the considered decision parameters has different effects on the recovered heat and net output power of the integrated system. Hence, a two-objective optimization with net output power and recovered heat as optimization targets is performed. In order to achieve the optimization solutions, the objective functions are defined seasonally. The results of optimum states regarding the concept of ideal point show that the best performance of the system is in summer with 0.262 kW for net power output and 0.921 kW for recovered heat.

关键词： Evacuated tube solar collector Transcritical Rankine cycle CO2 two-objective optimization Decision variables

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Performance evaluation and optimization of a distributed generation system integrating high-temperature proton exchange membrane fuel cell and recuperative-regenerative Organic Rankine Cycle

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ENERGY 2025年 319卷

作者： Xu, Shiyi Leng, Shuang Xie, Junen Liu, Zekuan Li, Chengjie Wang, Jingyi Chen, Zhengjian Liao, Mei Qin, Jiang Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Peoples R China Harbin Inst Technol Chongqing Res Inst Chongqing 401120 Peoples R China Harbin Inst Technol Sch Sci Shenzhen 518055 Peoples R China Shenzhen Energy Grp Co Ltd Shenzhen 518000 Peoples R China

Improving the electrical efficiency of distributed generation systems based on high-temperature proton exchange membrane fuel cells (HT-PEMFC) is essential for its promotion. This paper proposes a distributed generation system that uses the recuperative-regenerative Organic Rankine Cycle (RR-ORC) to recover waste heat from the HT-PEMFC for electricity generation. To further increase the evaporation temperature of the RR-ORC, it innovatively utilizes organic fluid to replace conventional coolants for stack cooling. The energy, exergy, and economic analysis method is adopted to evaluate the system performance comprehensively. The results suggest that toluene is an optimal organic fluid. Under the typical condition, the total cost per hour, the net power, and the energy efficiency of this novel system are 44.86 $/h, 119.66 kW, and 49.48 %, respectively. Compared with the conventional system integrating HT-PEMFC and ORC, the system net power can be improved by 7.55 %. Moreover, the parameterization results suggest that the current density, fuel cell temperature, cathode stoichiometry, and pinch point temperature difference of the condenser can significantly affect the system performance. On this basis, the optimal design parameters under which the total cost per hour and the system exergy efficiency are 44.88 $/h and 53.87 %, respectively, have been obtained through a two-objective optimization.

关键词： High-temperature proton exchange membrane fuel cells Distributed generation systems Recuperative-regenerative Organic Rankine Cycle two-objective optimization Economic analysis

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A genetic algorithm optimization of ring-stiffened cylindrical shells for axial and radial buckling loads

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ARCHIVE OF APPLIED MECHANICS 2011年第11期81卷 1639-1649页

作者： Bagheri, M. Jafari, A. A. Sadeghifar, M. Shahid Sattari Air Univ Dept Aerosp Engn Tehran Iran KN Toosi Univ Technol Dept Mech Engn Tehran Iran Islamic Azad Univ Nowshahr Branch Dept Mech Engn Nowshahr Iran

In this research, the general axial and radial buckling optimization of ring-stiffened cylindrical shells is implemented by the genetic algorithm (GA). The stiffened shell is subjected to four constraints including the fundamental frequency, the structural weight, the axial buckling load, and the radial buckling load. In addition, six design variables including shell thickness, number of stiffeners, stiffeners width and height, stiffeners eccentricity distribution order, and stiffeners spacing distribution order are considered. In analytical solution, the Ritz method is applied and stiffeners are treated as discrete elements. The effect of the weighting coefficients of the objective functions on the optimum solution is studied. The results show that optimized stiffening a cylindrical shell leads to a lower structural weight, higher natural frequencies, and larger axial and radial buckling loads, simultaneously. In addition, the upper and lower bounds of the design variables influence the optimum results considerably. It is also found that the distributions of eccentricity and spacing of the stiffeners influence the magnitudes of the axial and radial buckling loads considerably.

关键词： Cylindrical shells Ring stiffeners Buckling loads two-objective optimization Genetic algorithm

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Evolutionary multi-objective optimization of energy efficiency in electrical discharge machining

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2018年第10期32卷 4775-4785页

作者： Gostimirovic, Marin Pucovsky, Vladimir Sekulic, Milenko Radovanovic, Miroslav Madic, Milos Univ Novi Sad Dept Prod Engn Fac Tech Sci Trg D Obradovica 6 Novi Sad 21000 Serbia Univ Nis Lab Machine Tools & Machining Fac Mech Engn A Medvedeva 14 Nish 18000 Serbia

Electrical energy, which in the machining zone is transformed into heat, is of key importance in electrical discharge machining (EDM). Machining performance of EDM is determined by the characteristic of discharge energy. Therefore, an experimental-analytical approach of discharge energy efficiency was analyzed. The main input parameters for controlling the discharge energy are discharge current and discharge duration. The EDM process is monitored considering the two output machining performance, i.e., material removal rate and surface roughness, which are important for increasing productivity and quality. We modeled the energy efficiency of electrical discharge machining by the use of genetic algorithm. With this action an attempt was made to find even more precise dependence of discharge energy parameters with machining performance. Finally, this was followed by optimization of the discharge energy efficiency in EDM process using multi-objective approach. Evolutionary two-objective optimization is leading to the set of optimal solutions for the discharge energy considering the two machining parameters. Using this set of solutions, EDM discharge energy parameters can be selected to achieve high material removal rate with good surface roughness.

关键词： EDM Discharge energy Machining performance Genetic algorithm two-objective optimization Pareto optimal set

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Multi-objective optimization Model for the Allocation of Water Resources in Arid Regions Based on the Maximization of Socioeconomic Efficiency

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WATER RESOURCES MANAGEMENT 2016年第3期30卷 927-946页

作者： Davijani, M. Habibi Banihabib, M. E. Anvar, A. Nadjafzadeh Hashemi, S. R. Univ Tehran Univ Coll Abureyhan Dept Irrigat & Drainage Tehran Iran Politecn Milan Univ Dept Civil & Environm Engn Milan Italy Univ Birjand Dept Water Resources Engn Birjand Iran

The escalating world population has led to a drastic increase in water demand in the municipal and drinking water, agriculture and industry sectors. This situation necessitates application of effective measures for the optimal and efficient management of water resources. With this respect, a two-objective socioeconomic model (aimed at job creation) has been presented in this study for the optimum allocation of water resources to industry, agriculture and municipal water sectors. In the agriculture sector, the production function of each product has been determined and then, based on the production functions, areas under cultivation, product yield and the income obtained from each product, the combined objective function has been specified. In the industry sector, since water demand is a function of the amount of produced products, price of supplied water and the price of other supplies, the demand function of this sector was determined regionally. Also, considering the existing necessity in meeting the municipal water requirement, the total amount of water needed by this sector was fully allocated. Then by using two meta-heuristic algorithms, i.e. genetic algorithm (GA) and particle swarm optimization (PSO), the objective functions were maximized and the water resources were optimally allocated between agriculture and industry sectors and the results

关键词： Allocation of water resources Job creation Agriculture and industry two-objective optimization Genetic algorithm Particle swarm optimization algorithm

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CFD simulation of the preheater cyclone of a cement plant and the optimization of its performance using a combination of the design of experiment and multi-gene genetic programming

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POWDER TECHNOLOGY 2018年 327卷 430-441页

作者： Kashani, Elham Mohebbi, Ali Heidari, Mandi Ghaedi Shahid Bahonar Univ Kerman Dept Chem Engn Fac Engn Kerman Iran Kerman Cement Plant Kerman Iran

Hurriclon cyclone is a specially designed preheater cyclone with two outlet connector pipes of cleaned gas in the cement industry. In Kerman cement plant, Iran, the initial structure of this cyclone was changed. This caused a decrease in the cyclone efficiency. In this study, to optimize the changed cyclone performance, one of the twin cyclones in the first-stage of the preheater tower, which had the most significant effect on particle separation from gas was simulated and validated by computational fluid dynamics. Using the design of experiment based on the simulation results, the effects of three dimensions (vortex-finder length, cylinder height, and cone tip diameter) were investigated on cyclone performance. The turbulent gas flow inside the cyclone was modelled using the Reynolds stress model due to the swirling flow inside the cyclones. The discrete phase model was used to calculate the trajectory of particles. It was observed that because of high gas inlet velocity and particle density as well as the geometry of the preheater cyclone, particles larger than the critical diameter continue spinning in the cyclone. The Multi-Gene Genetic Programming (MGGP) was used to obtain two equations for efficiency and pressure drop in order to optimize the preheater cyclone performance. For this purpose, two-objective optimization using the Genetic Algorithm (GA) was performed. The optimization results showed that by using the optimized dimensions for the preheater cyclone, the pressure drop decreases by 2.2% and the efficiency increases by 13.4%. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Preheater cyclone Cement plant Computational fluid dynamics Design of experiment Multi-gene genetic programming two-objective optimization

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Thermoeconomic and environmental analysis and multi-criteria optimization of an innovative high-efficiency trigeneration system for a residential complex using LINMAP and TOPSIS decision-making methods

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2022年第3期147卷 2369-2392页

作者： Sanaye, Sepehr Khakpaay, Navid Chitsaz, Ata Yahyanejad, Mohammad Hassan Iran Univ Sci & Technol Mech Engn Sch Energy Syst Improvement Lab Tehran Iran Urmia Univ Fac Engn Mech Engn Dept Orumiyeh Iran

A combined cooling, heating, and power integrated system suitable for a residential complex, with two new cycles in hot and cold seasons, is proposed and designed here. By a comprehensive modeling approach (in four aspects of energy, exergy, economic, and environmental), the integrated system is optimized for variable electrical, heating, and cooling loads during a year. two objective functions (exergy efficiency, eta(Ex,tot), and relative annual benefit, RAB) and six design parameters are considered for multi-objective Genetic Algorithm optimization. Also, a novel variable operational price method during the system lifetime was applied. optimization results showed that selecting 14 gas engines (with 912 kW nominal power output) and 9 backup boilers (with a heating capacity of 1450 kW) leads to 74% of overall thermal efficiency and 1.6 years' payback period for the above studied integrated system. Furthermore, the comparison of results in integrated and traditional (buying electricity from the grid and burning fuel in boiler for providing heat) systems showed a 2.46 x 10(7) m(3) year(-1) (68% in comparison with traditional system) saving in boiler fuel volume flow rate, a 2.11 x 10(6) $ year(-1) saving in boiler fuel cost, a 4.55 x 10(8) kg year(-1) (87.5% in comparison with traditional system) reduction in CO, CO2 and NOx emissions and a 9.46 x 10(6) $ year(-1) reduction in its corresponding penalty cost.

关键词： Integrated system Energy efficiency Exergy efficiency Residential complex two-objective optimization

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Using non-metric multi-dimensional scaling analysis and multi-objective optimization to evaluate green algae for production of proteins, carbohydrates, lipids, and simultaneously fix carbon dioxide

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BIOMASS & BIOENERGY 2020年 141卷 105711-105711页

作者： Hussain, Javid Wang, Xiaoqing Sousa, Leandro Ali, Roshan Rittmann, Bruce E. Liao, Wei Michigan State Univ Dept Biosyst & Agr Engn E Lansing MI 48824 USA Univ Fed Bahia Inst Biol Salvador BA Brazil Khyber Med Univ Dept Mol Biol & Genet Peshawar Pakistan Arizona State Univ Biodesign Swette Ctr Environm Biotechnol POB 875701 Tempe AZ 85287 USA

Eight green-algae species were investigated to elucidate the effects of growth phases on biomass productivity (CO2 fixation rate) and accumulation of algal macromolecules (proteins, carbohydrates, and lipids). Non-metric multi-dimensional scaling (NMDS) and multiple-objective optimization were applied to evaluate the effects of algal species and growth phases on biomass productivity and algal macromolecule accumulation. Algal species and growth phases had significant impacts on biomass production and composition. NMDS delineated that biomass productivity and protein accumulation of all species were dominant in the log phase, while the stationary phase was favorable for lipid and carbohydrate accumulation. Among the eight algal species, Chlorella vulgaris had the highest biomass productivity (75 mg/L/day) and a high protein content in the exponential phase (53%). Scenedesmus obliquus had the highest protein content (55%) in the log phase, Botryococcus braunii showed the highest lipid content (40%) in the stationary phase, and Staurastrum sp. presented the highest carbohydrate content (37%) in the stationary phase. two-objective optimization for algal biomass productivity and composition led to the conclusion that Chlorella vulgaris, Botryococcus braunii, and Coelastrum microporum are the preferred strains for accumulation of proteins, lipids, and carbohydrates, respectively, along with good CO2 fixation (biomass productivity). NMDS and multi-objective optimization are useful tools to select preferred strains and corresponding culture conditions from a large pool of candidates for simultaneous biomass and macromolecule accumulation.

关键词： Biomass productivity Carbon dioxide fixation Biomass composition Growth phase Principal components analysis two-objective optimization

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Double-objective optimization for a pulp washing process based on neural network

Double-objective optimization for a pulp washing process bas...

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IEEE International Conference on Mechatronics and Automation

作者： Tang, Wei Wang, Mengxiao Chao, Yuyan He, Lifeng Itoh, Hidenori Shaanxi Univ Sci & Technol Microcomp Appl & Dev Inst Xianyang 712081 Shaanxi Peoples R China Nagoya Inst Technol Dept Artificial Intelligence & Comp Sci Nagoya Aichi 4668555 Japan

ISBN: (纸本)9781424408276

For a countercurrent paper pulp washing system, the requirements of craft to residual soda in the final washed pulp and Baume degree in the first stage filtrate tank are usually inconsistent. To compromise this pair of contradiction, a neural network (NN) based two-objective optimization algorithm is proposed. In terms of a two-step identification method, the NN dynamic and steady models on the residual soda in the final washed pulp and the Baume degree in the first stage filtrate tank are identified, respectively. A double-objective optimization algorithm on the hot clean water input and the final washed pulp output is proposed by an external penalty function method. This control strategy has been imbedded into a pulp washing process DCS running in two paper mills in China and resulted in notable profits.

关键词： pulp washing process two-step identification method two-objective optimization neural network DCS

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Optimum arrangement of two-stage plug and concentrate recycling RO systems using thermodynamic and exergy analysis

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INTERNATIONAL JOURNAL OF NUMERICAL METHODS FOR HEAT & FLUID FLOW 2020年第6期30卷 3323-3348页

作者： Naeimi, Abbas Ahmadi, Mohammad Hossein Sadeghzadeh, Milad Kasaeian, Alibakhsh Shahid Beheshti Univ Fac Mech & Energy Engn Tehran Iran Shahrood Univ Technol Shahrood Iran Univ Tehran Dept Renewable Energy & Environm Engn Tehran Iran

Purpose This paper aims to determine the optimum arrangement of a reverse osmosis system in two methods of plug and concentrate recycling. Design/methodology/approach To compare the optimum conditions of these two methods, a seawater reverse osmosis system was considered to produce fresh water at a rate of 4,000 m3/d for Mahyarkala city, located in north of Iran, for a period of 20 years. Using genetic algorithms and two-objective optimization method, the reverse osmosis system was designed. Findings The results showed that exergy efficiency in optimum condition for concentrate recycling and plug methods was 82.6 and 92.4 per cent, respectively. The optimizations results showed that concentrate recycling method, despite a 36 per cent reduction in the initial cost and a 2 per cent increase in maintenance expenses, provides 6 per cent higher recovery and 19.7 per cent less permeate concentration than two-stage plug method. Originality/value optimization parameters include feed water pressure, the rate of water return from the brine for concentrate recycling system, type of SW membrane, feedwater flow rate and numbers of elements in each pressure vessel (PV). These parameters were also compared to each other in terms of recovery (R) and freshwater unit production cost. In addition, the exergy of all elements was analyzed by selecting the optimal mode of each system.

关键词： Concentrate recycling Reverse osmosis two-objective optimization Unit production cost two-stage plug

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