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multi-objective optimal droop control of solid oxide fuel cell based integrated energy system

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第30期48卷 11382-11389页

作者： Zhou, Yujie Hua, Qingsong Liu, Ping Sun, Li Southeast Univ Key Lab Thermal Sci & Power Engn Minist Educ THU Nanjing 210096 Jiangsu Peoples R China Beijing Normal Univ Coll Nucl Sci & Technol Key Lab Beam Technol Minist Educ Beijing 100875 Peoples R China Sun Yat sen Univ Guangzhou 510275 Guangdong Peoples R China Shenzhen Faroad Intelligent Equipment Co Ltd Shenzhen 518125 Guangdong Peoples R China 135 West Xingang Rd Guangzhou Peoples R China

Solid oxide fuel cell (SOFC) based integrated energy system (IES) is promising in the future low-carbon power generation market, due to the high efficiency and flexibility. However, it is challenging for the dynamic control design in dealing with the conflicting objectives in terms of fast power tracking and overall efficiency during the transient process of load response. To this end, this paper develops a multi-objective optimal droop control strategy for the real-time power dispatch of the IES. Firstly, a nonlinear implicit dynamic model consisting of SOFC, lithium-ion battery, photovoltaic array and DC-DC converter is devel-oped. Then, a multi-objective optimization is formulated to balance the power tracking performance and transient efficiency. Non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to search the optimal parameters for droop controller. Simulation results demonstrates that the electricity loss of the proposed method can be reduced by 96.26% with a slight compromise in power tracking performance.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Solid oxide fuel cell Integrated energy system Dynamic model Optimal control multi-objective genetic algorithm

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Optimization investigation on configuration parameters of sine wavy fin in plate-fin heat exchanger based on fluid structure interaction analysis

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2019年 131卷 385-402页

作者： Wen, Jian Li, Ke Wang, Chunlong Zhang, Xing Wang, Simin Xi An Jiao Tong Univ Dept Refrigerat & Cryogen Engn Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Dept Proc Equipment & Control Engn Sch Chem Engn & Technol Xian 710049 Shaanxi Peoples R China

The comprehensive performance of sine wavy fin in plate-fin heat exchangers (PFHEs) is numerically studied based on fluid structure interaction (FSI) analysis in this paper. The analysis results of stress distribution reveal that the highest stress is located in the inlet and outlet region of fin structure, and the fluctuant stress reaches to the peak in the wave crest. By way of analyzing Full 2nd-Order Polynomial response surface (RS), the effects of inlet velocity and five configuration parameters (fin height, fin space, fin thickness, fin wavelength and double amplitude) on heat transfer, flow resistance and stress of sine wavy fin structure are quantitatively assessed. The results reveal that the j factor increases with the increase of fin space and fin height, and decreases with the increase of fin thickness, wavelength and inlet velocity. The j factor firstly increases with the increase of double amplitude and then decreases. The f factor increases with double amplitude, fin space and fin height, and decreases with fin thickness, wavelength and inlet velocity. The maximum stress increases with the increase of wavelength and fin space, and decreases with the increase of fin thickness and double amplitude. The interaction effects of input parameters on the j factor and f factor are not obvious. While the interaction effects of fin thickness and wavelength, double amplitude and wavelength on the maximum stress are obvious. Based on RS, multi-objective genetic algorithm (MOGA) is performed to optimize the fin structure comprehensively, with multiple objectives of increasing the JF factor (JF = j/f(1/3)) and decreasing the maximum stress to the best. The optimization results shows that, compared with the original design, the JF factor of optimal design 1, 2, and 3 increases by 11.0%, 8.4% and 15.9% respectively, and the maximum stress decreases by 32.3%, 42.4% and 20.7% respectively. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Plate-fin heat exchanger Wavy fin Stress analysis Response surface multi-objective genetic algorithm

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Optimization of shape and angle of attack of winglet vortex generator in a rectangular channel for heat transfer enhancement

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APPLIED THERMAL ENGINEERING 2015年 81卷 376-387页

作者： Abdollahi, Azita Shams, Mehrzad KN Toosi Univ Technol Dept Mech Engn Tehran Iran

A three dimensional numerical simulation was performed to study the effect of the shape and the angle of attack of the winglet vortex generator on the heat transfer and fluid flow characteristics in a rectangular heat sink. The mass, momentum and energy equations were solved using finite volume method by considering the steady state, laminar and incompressible fluid flow. The average and local Nusselt number and pressure drop were investigated in the presence of the vortex generators at different shapes and angles of attack. To achieve a maximum heat transfer enhancement and a minimum pressure drop, the optimal values of these parameters were calculated using the Pareto optimal strategy. For this purpose, computational fluid dynamics analyses, multi-objective genetic algorithm and artificial neural networks were combined together and used in the optimization process. Finally, the optimal values of these parameters were presented. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Heat transfer enhancement Vortex generator multi-objective genetic algorithm Artificial neural network

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A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin

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APPLIED ENERGY 2019年 241卷 331-361页

作者： Ascione, Fabrizio Bianco, Nicola Mauro, Gerardo Maria Vanoli, Giuseppe Peter Univ Naples Federico II Dept Ind Engn Piazzale Tecchio 80 I-80125 Naples Italy Univ Sannio Dept Engn Piazza Roma 21 I-82100 Benevento Italy Univ Molise Dept Med & Hlth Sci Vincenzo Tiberia Via Gazzani 47 I-86100 Campobasso Italy

The comprehensive optimization of building energy design is fundamental to promote sustainability but it is an arduous issue that involves a huge domain of variables and objectives. The proposed investigation addresses this issue through a novel comprehensive framework - Harlequin - that performs a multi-phase and multi-objective design optimization. Three phases are carried out to optimize design variables related to the whole building-plants system, considering different energy, comfort, economic and environmental performance indicators. Phase 1 implements a genetic algorithm to achieve the Pareto optimization of envelope, geometry and space conditioning set points. Phase 2 performs a smart exhaustive sampling of design scenarios to find optimal energy systems. Phase 3 provides the most sustainable, the cost-optimal and the lowest investment (but energy-efficient) design solutions. Among these, the stakeholders can choose the best solution according to their wills and needs. Harlequin uses EnergyPlus (only in phase 1) and MATLAB (R) and it is so-called because building geometry and envelope are optimized for each exposure, thereby providing "Harlequin buildings". The novelty and scientific significance consist in ensuring a reliable design optimization by investigating a domain of variables and objectives, as comprehensive as never before. As a case study, Harlequin is applied to design a typical Italian office in Milan. Compared to a reference design, significant reductions of primary energy consumption (PEC), global cost (GC) and CO2-eq emissions can be achieved, depending on the chosen solution. The maximum reductions are 43.9 kWh(p)/m(2) a for PEC, 63.9 (sic)/m(2) for GC (discount rate of 3%) and 12.3 kg/m(2) a for CO2-eq.

关键词： Building design Energy efficiency Building energy simulation Building energy optimization multi-objective genetic algorithm Cost-optimal analysis

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The potential of time-multiplexed steering in phased array microwave hyperthermia for head and neck cancer treatment

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PHYSICS IN MEDICINE AND BIOLOGY 2018年第13期63卷 135023-135023页

作者： Cappiello, G. Drizdal, T. Mc Ginley, B. O'Halloran, M. Glavin, M. van Rhoon, G. C. Jones, E. Paulides, M. M. Natl Univ Ireland Coll Engn & Informat Discipline Elect & Elect Engn Galway Ireland Erasmus MC Canc Inst Dept Radiat Oncol Hyperthermia Unit Rotterdam Netherlands Galway Mayo Inst Technol Dept Comp Sci & Appl Phys Galway Ireland Natl Univ Ireland Lambe Inst Translat Res Translat Med Device Lab Galway Ireland

Clinical studies have shown that hyperthermia sensitizes tumor cells for conventional therapies. During phased-array microwave hyperthermia, an array of antennas is used to focus the electromagnetic waves at the target region. Selective heating, while preserving the healthy tissue, is a demanding challenge and currently patient specific pre-treatment planning is used to optimize the amplitudes and phases of the waves. In addition, when needed, this single optimal heat distribution is adapted using the simulations based on the feedback from thermo-sensors and the patient. In this paper, we hypothesize that sequential, i.e. 'time-multiplexed', application of multiple Pareto optimal heating patterns provides a better time-averaged treatment quality. To test the benefit of such a time-multiplexed approach, a multi-objective genetic algorithm was introduced to balance two objectives that both focus the specific absorption rate (SAR) delivered to the target region but differ in the suppressing of pre-defined hotspots. This step leads to two Pareto optimal distributions. These 'diverse' antenna settings are then applied sequentially and thermal simulations are used to evaluate the effectiveness of the time-multiplexed steering. The proposed technique is tested using treatment planning data of a representative dataset of five head and neck patients for the HYPERcollar3D. Steering dynamics are analysed and the time-multiplexed steering is compared to the current static solution used in the clinic, i.e. hotspot-target SAR quotient optimization using particle swarm optimization. Our results demonstrate that realistic steering periods of 10s suffice to stabilize temperatures within 0.04 degrees C and the ability to enhance target heating while reducing hotspots, i.e. 0.3 degrees C-1.2 degrees C improvement in T-50 while reducing hotspot temperatures by 0.6 degrees C-1.5 degrees C.

关键词： hyperthermia hyperthermia treatment planning multi-objective genetic algorithm

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Design, optimization and control of extractive distillation for the separation of isopropanol-water using ionic liquids

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SEPARATION AND PURIFICATION TECHNOLOGY 2019年 209卷 833-850页

作者： Ma, Shoutao Shang, Xianyong Zhu, Minyan Li, Jinfang Sun, Lanyi China Univ Petr East China Coll Chem Engn State Key Lab Heavy Oil Proc Qingdao 266580 Shandong Peoples R China

As an excellent solvent, ionic liquids (ILs) have received much attention in the last two decades. One of the most important applications is to be used as an extractant for separation of azeotropic mixtures via extractive distillation (ED). In this work, 1-ethyl-3-methylimidazolium dicyanamide ([emim][N(CN)2]) is selected as the extractant to separate isopropanol (IPA)-water azeotrope based on two ED processes. multi-objective genetic algorithm (MOGA) is used to determine the operating parameters for these ED processes. Total annual cost (TAO, efficiency indicator of extractive section (E-ext), efficiency indicator of per tray in extractive section (e(ext)) and energy consumption per product flow rate (GEO are selected as objective functions in the ED process optimization, and the Pareto front provides a series of solutions satisfying the constraints with different operating parameters. The results show that TAC of the process 2 with two evaporators used in the phase of solvent recovery is 4.26% lower than that of process 1 with one evaporator. In addition, the thermodynamic efficiency (eta) and CO2 emissions are calculated for different cases, and they provide the reference for the selection of operating parameters. On this basis, the control structures of ED processes are further studied, and the traditional two-point temperature control structure works pretty well for disturbances of both feed rate and feed composition.

关键词： Ionic liquids (ILs) Extractive distillation multi-objective genetic algorithm Process optimization Dynamic control

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Strategy development for retrofitting ships for implementing shore side electricity

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2019年第Sep.期74卷 201-213页

作者： Yu, Jingjing Voss, Stefan Tang, Guolei Dalian Univ Technol Fac Infrastruct Engn State Key Lab Coastal & Offshore Engn Dalian 116023 Liaoning Peoples R China Univ Hamburg Inst Informat Syst Von Melle Pk 5 D-20146 Hamburg Germany

Shore side electricity (SSE) is the use of electricity from the shore to power a ship's system when it is berthing at ports. It is a solution to reduce the emissions of auxiliary engines at berth and to improve the local and regional air quality. This paper develops a multi-objective model that integrates spatial and temporal dimensions for strategic planning regarding whether and when to retrofit ships to use SSE. The strategic planning problem is to maximize the environmental benefit and to minimize the payback period of the investment over the entire planning horizon. By adopting an improved multi-objective genetic algorithm (NSGA-II), a series of retrofit strategies for container ships calling at Dalian Port in China are obtained under a projected annual visiting frequency of ships with different ship sizes and shipping lines. Depending on a selected retrofit strategy, the sum of the payback periods of ships that are determined to be retrofitted is 46 years (on average less than four years) and the environmental benefit of using SSE can be up to 128 million USD for Dalian Port in the planning horizon from 2020 to 2035. The results show that using SSE is an efficient method to decline emissions within the maritime sector and validate that the proposed methodology can be useful to ship owners to decide upon investing in SSE applications and to promote the development of green shipping and green ports.

关键词： Shore side electricity Ship retrofitting multi-objective optimization multi-objective genetic algorithm Green port

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multi-objective selection and structural optimization of the gantry in a gantry machine tool for improving static, dynamic, and weight and cost performance

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CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS 2016年第1期24卷 83-93页

作者： Besharati, S. R. Dabbagh, V. Amini, H. Sarhan, Ahmed A. D. Akbari, J. Hamdi, M. Ong, Z. C. Univ Malaya Fac Engn Dept Mech Engn Ctr Adv Mfg & Mat Proc Kuala Lumpur 50603 Malaysia Assiut Univ Fac Engn Dept Mech Engn Assiut Egypt Sharif Univ Technol Sch Mech Engn Tehran Iran

In this investigation, the multi-objective selection and optimization of a gantry machine tool is achieved by analytic hierarchy process, multi-objective genetic algorithm, and Pareto-Edgeworth-Grierson-multi-criteria decision-making method. The objectives include maximum static deformation, the first four natural frequencies, mass, and fabrication cost of the gantry. Further structural optimization of the best configuration was accomplished using multi-objective genetic algorithm to improve all objectives except cost. The result of sensitivity analysis reveals the major contribution of columns of gantry with respect to the crossbeam's contribution. After determining the most effective geometrical parameters using sensitivity analysis, multi-objective genetic algorithm was performed to obtain the Pareto-optimal solutions. In order to choose the final configuration, Pareto-Edgeworth-Grierson-multi-criteria decision-making was applied. The procedure outlined in this article could be used for selection and optimization of gantry as quantitative method as opposed to traditional qualitative method exploited in industrial application for design of gantry.

关键词： gantry machine tool structural optimization multi-criteria decision-making multi-objective genetic algorithm Pareto-Edgeworth-Grierson-multi-criteria decision-making

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Solving multi-objective routing and wavelength assignment in WDM network using hybrid evolutionary computation approach

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COMPUTER COMMUNICATIONS 2010年第18期33卷 2246-2259页

作者： Leesutthipornchai, Pakorn Charnsripinyo, Chalermpol Wattanapongsakorn, Naruemon King Mongkuts Univ Technol Thonburi Fac Engn Dept Comp Engn Bangkok 10140 Thailand Natl Elect & Comp Technol Ctr Klongluang 12120 Pathumthani Thailand

Routing and wavelength assignment (RWA) is a well-known issue in wavelength division multiplexing (WDM) optical networks. In this paper, we present RWA design for WDM networks by considering multiple design objectives which are maximizing the number of traffic demands to be served and minimizing the number of wavelength channels to be assigned. A hybrid evolutionary computation approach consisting of genetic algorithm for routing allocation with minimum degree first for wavelength assignment (GA-MDF) and the fast non-dominated sorting genetic algorithm (NSGA-II) to search for non-dominated solutions is applied for solving the multi-objective RWA network design problem. The hybrid evolutionary algorithm is used as a meta-heuristic technique for obtaining good solutions for various problem sizes. The obtained results are provided as candidate choices or non-dominated front. We compare the simulation results obtained from the NSGA-II with those obtained from the traditional Weighted-Sum approach. Numerical results show that our hybrid evolutionary computation approach is effective in solving the multi-objective RWA problem. The GA-MDF can outperform the FAR-FF method in both design objectives. In addition, the solutions from the NSGA-II are more diverse on the multi-objective space than those of the Weighted-Sum method. We also apply a Pruned mechanism to help cutting off numerous non-dominated solutions for making decision on the final solution. (C) 2010 Elsevier B.V. All rights reserved.

关键词： multi-objective genetic algorithm Routing and wavelength assignment WDM network

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Optimal Short-term Reservoir Operation with Integrated Long-term Goals

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WATER RESOURCES MANAGEMENT 2012年第10期26卷 2833-2850页

作者： Sreekanth, J. Datta, Bithin Mohapatra, Pranab K. CSIRO Land & Water Ecosci Precinct Dutton Pk Qld 4102 Australia James Cook Univ Townsville Qld 4811 Australia IIT Kanpur Dept Civil Engn Kanpur 208016 Uttar Pradesh India

A methodology to incorporate long-term goals within the short-term reservoir operation optimization model is proposed. Two conflicting objectives for the management of hydropower generation in two different power plants are incorporated. A chance-constrained optimization model is used to derive long-term (annual) operation strategies. With the time horizon of operation for the short-term optimization model kept equal to a single time-step of the long-term optimization model, the optimum end storages derived from the long-term model are incorporated as constraints (storage lower bounds) within the short-term model. The long-term benefits accrued from such an operation model are illustrated for a small reservoir, in South India. The solutions are compared with the historic operation. These are also compared with the solutions of a short-term optimal operation model ignoring long-term goals. The optimization model is solved using a multi-objective genetic algorithm.

关键词： Reservoir operation Short-term, long-term optimization Hydro-power multi-objective genetic algorithm

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