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Thermal and hydraulic optimization of plate heat exchanger using multi objective genetic algorithm

CASE STUDIES IN THERMAL ENGINEERING 2017年 10卷 570-578页

作者： Imran, Muhammad Pambudi, Nugroho Agung Farooq, Muhammad Univ Management & Technol Dept Energy Engn Sch Engn CII Johar Town Lahore Pakistan Kyushu Univ Int Inst Carbon Neutral Reserch WPI I2CNER Nishi Ku 744 Motooka Fukuoka Fukuoka 8190395 Japan Univ Negeri Sebelas Maret Dept Mech Engn Educ Jl Ahmad Yani 200A Pabelan Indonesia Univ Engn & Technol Lahore Dept Mech Engn KSK Campus Kala Shah Kaku 39020 Pakistan

In this paper thermal and hydraulic optimization of water to water chevron type plate heat exchanger is presented. The optimization is performed using the multi objective genetic algorithm in MATLAB optimization environment. Constrain matrix is a set of different geometrical parameters of plate heat exchanger within the logical bounds. The two objective functions are pressure drop of hot side and heat transfer. Due to conflicting nature of these objective functions, no single solution can satisfy both of the objective function simultaneously. The increase in heat transfer will results in increase in pressure drop, therefore, optimization results are presented as Pareto Front. multi objective genetic algorithm tool was employed to find a set of optimum solution which was trade-off between pressure drop and heat transfer. M the end, sensitivity analysis was performed to analyse the effect of geometrical parameters of heat exchanger on thermal and hydraulic performance. The sensitivity results show that the heat transfer and pressure drop are greatly affected by the vertical port centre distance, plate spacing and number of thermal plates.

关键词： Thermal optimization Hydraulic optimization Plate heat exchanger multi objective genetic algorithm Heat transfer Pressure drop

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A multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第12期39卷 10352-10358页

作者： Aiello, Giuseppe La Scalia, Giada Enea, Mario Univ Palermo Dipartimento Tecnol Meccan Prod & Ingn Gest I-90133 Palermo Italy

This paper proposes a new multi objective genetic algorithm (MOGA) for solving unequal area facility layout problems (UA-FLPs). The genetic algorithm suggested is based upon the slicing structure where the relative locations of the facilities on the floor are represented by a location matrix encoded in two chromosomes. A block layout is constructed by partitioning the floor into a set of rectangular blocks using guillotine cuts satisfying the areas requirements of the departments. The procedure takes into account four objective functions (material handling costs, aspect ratio, closeness and distance requests) by means of a Pareto based evolutionary approach. The main advantage of the proposed formulation, with respect to existing referenced approaches (e.g. bay structure), is that the search space is considerably wide and the practicability of the layout designs is preserved, thus improving the quality of the solutions obtained. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Facility layout problems multi objective genetic algorithm Slicing structure

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Optimal simultaneous coordination of PSS and TCSC using multi objective genetic algorithm

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JOURNAL OF ELECTRICAL SYSTEMS 2013年第4期9卷 410-421页

作者： Davarani, Roohalamin Zeinali Ghazi, Reza Grad Univ Adv Technol Haftbagh highway Kerman Iran Grad Univ Adv Technol Dept Elect Engn Kerman Iran Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Iran

Power system stability enhancement via optimal simultaneous coordinated design of a power system stabilizer (PSS) and a thyristor-controlled series capacitor (TCSC) for electric power systems oscillations damping is investigated in this paper. A SMIB system equipped with PSS and TCSC controllers is considered in this study. Although these controllers are used for stabilization of power system oscillations but the system must preserve its stability when subjected to sever disturbances. Therefore, the overall stability of the system should be considered. To do so, in the present paper the problem of controllers design is formulated as a multi objective optimization problem. Then the multi objective genetic algorithm (MOGA) is explored to solve this optimization problem. Pareto method type of selection is used in the present MOGA approach.

关键词： Power system stabilizer Thyristor-controlled series capacitor multi objective genetic algorithm

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Optimization of energy consumption and environmental impacts of chickpea production using data envelopment analysis(DEA)and multi objective genetic algorithm(MOGA)approaches

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Information Processing in Agriculture 2016年第3期3卷 190-205页

作者： Behzad Elhami Asadollah Akram Majid Khanali Department of Agricultural Machinery Engineering Faculty of Agricultural Engineering and TechnologyUniversity of TehranKarajIran

Energy consumption in agricultural products and its environmental damages has increased in recent *** cycle assessment(LCA)has been introduced as a suitable tool for evaluation environmental impacts related to a product over its life *** this study,optimization of energy consumption and environmental impacts of chickpea production was conducted using data envelopment analysis(DEA)and multi objective genetic algorithm(MOGA)*** were collected from 110 chickpea production enterprises using a face to face questionnaire in the cropping season of *** results of optimization revealed that,when applying MOGA,optimum energy requirement for chickpea production was significantly lower compared to application of DEA technique;so that,total energy requirement in optimum situation was found to be 31511.72 and 27570.61 MJ ha^-1 by using DEA and MOGA techniques,respectively;showing a reduction by 5.11%and 17%relative to current situation of energy *** of environmental impacts by application of MOGA resulted in reduction of acidification potential(ACP),eutrophication potential(EUP),global warming potential(GWP),human toxicity potential(HTP)and terrestrial ecotoxicity potential(TEP)by 29%,23%,10%,6%and 36%,*** capable of reducing the energy consumption from machinery,farmyard manure(FYM)diesel fuel and nitrogen fertilizer(the mostly contributed inputs to the environmental emissions)by 59%,28.5%,24.58%and 11.24%,***,the MOGA technique showed a superior performance relative to DEA approach for optimizing energy inputs and reducing environmental impacts of chickpea production system.

关键词： Data envelopment analysis Energy Life cycle assessment multi objective genetic algorithm

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A Hybrid Control Scheme for a Twin Rotor System with multi objective genetic algorithm

A Hybrid Control Scheme for a Twin Rotor System with Multi O...

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12th International Conference on Computer Modelling and Simulation (UKSim)

作者： Toha, S. F. Tokhi, M. O. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S10 2TN S Yorkshire England

ISBN: (纸本)9780769540160

Control of vibration suppressions is crucial for applications in engineering particularly in the area of aircraft system. A hybrid control approach encompasses of using a feedforward intelligent command shaping technique with combination of intelligent PID feedback control is presented in this paper. The advantage of using command shaping is to reduce system vibration. However, it can cause delay in system response resulting to a conflict between vibration suppression and rise time response. multi objective genetic algorithm is employ in this work to determine a set of solutions for the amplitudes and corresponding time locations of impulses on an extra sensitive (EI) command shaping as well as gain parameters for the PID controller. The effectiveness of the proposed technique is assessed both in the time domain and the frequency domain. Moreover, a comparative assessment of the performance of the technique with the system response and unshaped finite step input is presented.

关键词： Vibration suppression twin rotor system multi objective genetic algorithm

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Robust Image Watermarking In Contourlet Domain Using multi objective genetic algorithm

Robust Image Watermarking In Contourlet Domain Using Multi O...

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8th International Conference on Digital Information Management (ICDIM)

作者： Mubeen, M. Gilani, S. A. M. Zafar, K. Natl Univ Comp & Emerging Sci Dept Comp Sci Lahore Pakistan

ISBN: (纸本)9781479906154;9781479906130

This paper presents a novel digital watermarking algorithm using Contourlet and Discrete Cosine transforms to perceptually shape the watermark by taking care of two conflicting parameters imperceptibility and robustness. multi objective genetic algorithm is used to decide the optimal watermarking strength (alpha). Contourlet transform is used to get better quality by taking care of Human Visual System and better robustness under various kinds of linear and non-linear filtering attacks. Discrete Cosine Transform with Zigzag scanning is used to detect the watermark blindly. The watermark embedding process is applied over the sub-band coefficients that lie on the edges and around the edges where distortions are less noticeable. The proposed algorithm provides an excellent tradeoff between robustness and imperceptibility and it is image adaptive. The experimental results show that the proposed algorithm improved the resistance to attacks especially JPEG compression with 5% quality, Median filter with a mask of 6 X 6, resizing up to 20% and various non-linear filtering attacks. Motivation of presented work is to adapt a watermark sequence to those frequency components of an image which provide a transparent and robust watermark.

关键词： Digital watermark Contourlet transform multi objective genetic algorithm Perceptual shaping

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An Investigation on Optimization of Parameters for Injection Molded Polypropylene-Marble Composites with multi objective genetic algorithm

An Investigation on Optimization of Parameters for Injection...

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IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)

作者： Kumar, Deepak Rao, P. N. Dangayach, G. S. Malaviya Natl Inst Technol Mech Engn Dept Jaipur Rajasthan India Univ Northern Iowa Dept Technol Cedar Falls IA 50614 USA

ISBN: (纸本)9781509028078

In the era of sustainability, recycling of industrial waste material by using marble powder as filler in the Polypropylene matrix recommend an interesting opportunity. In this study, composites were fabricated by adding different weight percentages of marble powder (5-15 wt.%) in the Polypropylene by injection molding process. To evaluate the contribution of marble powder in the PP-marble composite, Mechanical characterizations such as Shore hardness test, compressive strength and impact strength were performed. For surface morphological characterizations Scanning Electron Microscopy (SEM) was performed for the Polypropylene-marble composites. Taguchi technique and multi objective genetic algorithm were used to optimize the parameters.

关键词： Polypropylene-Marble composite injection molding morphological characterization mechanical properties Taguchi technique multi objective genetic algorithm

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Present a new multi objective optimization statistical Pareto frontier method composed of artificial neural network and multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for non-Newtonian binary fluids

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2019年 535卷 122409-000页

作者： Wu, Huawei Bagherzadeh, Seyed Amin D'Orazio, Annunziata Habibollahi, Navid Karimipour, Arash Goodarzi, Marjan Quang-Vu Bach Hubei Univ Arts & Sci Hubei Key Lab Power Syst Design & Test Elect Vehi Xiangyang 441053 Peoples R China Hubei Univ Arts & Sci Sch Automot & Traff Engn Xiangyang 441053 Peoples R China Islamic Azad Univ Dept Mech Engn Najafabad Branch Najafabad Iran Sapienza Univ Roma Dipartimento Ingn Astronaut Elettr & Energet Via Eudossiana 18 I-00184 Rome Italy Ton Duc Thang Univ Sustainable Management Nat Resources & Environm R Fac Environm & Labour Safety Ho Chi Minh City Vietnam

This work aims to present a new statistical optimization approach of artificial neural network modified by multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for a non-Newtonian nanofluid composed of Fe3O4 nanoparticles dispersed in liquid paraffin. Hence the mixture pressure lose & convection coefficient are evaluated and then optimized so that to maximize the convection heat transfer and minimize the pressure drop. The results showed that the proposed model of multi objective optimization of GA Pareto optimal front, quantified the trade-offs to handle 2 fitness functions of the considered non-Newtonian pipe flow. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Fe3O4/liquid paraffin Thermo-physical properties multi objective genetic algorithm Optimization

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Modelling of photovoltaic cells using Boole's rule-based multi-objective genetic algorithm implemented in Indoor hardware setup

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期45卷 5075-5092页

作者： IssanRaj, R. Visalakshi, S. SRM Valliammai Engn Coll Dept EIE Kattankulathur Tamilnadu India

Triple Diode Solar Cell Module (TDSCM) circuit with nine parameters for various environmental circumstances represents the behavior and practical performance of solar *** precise extraction of photovoltaic (PV) module parameters is essential for optimising the energy conversion efficiency of PV systems. Usually the equations describing solar panels are implicit in nature, and parameter extraction has been very complicated. The solar cell is mathematically modelled with nonlinear I-V (Current -Voltage) characteristics behavior, and it cannot be directly determined from the PV's datasheet due to the lack of data offered by the PV manufacturers. On the basis of the technical datasheet of the photovoltaic module (PV), only four equations can be obtained in single diode, double diode, and triple diode parameters. To be implemented with fifth equation, many researchers have been done with multiple approximations and it becomes with low accuracy, complexity of computation, convergence problem. To resolve these issues, a new multi-objective optimization (GA) genetic algorithm method is prescribed to frame the fifth equation using the Boole rules implemented with the curved area concept. The proposed Boole's rule based model offers superior non-linearity performance and high precision modelling, and the error shows a significant reduction when compared to the single and double diode approaches used in the existing approach. The effectiveness of the proposed I-V curve characteristics efficiency was improved by the implementation of the proposed Boole's rule with RMSE error 0.000034.

关键词： Photovoltaic cell model solar cell modelling multi objective genetic algorithm triple diode model boole's rule

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Comparing the N-branch genetic algorithm and the multi-objective genetic algorithm

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AIAA JOURNAL 2004年第7期42卷 1495-1500页

作者： Martin, ET Hassan, RA Crossley, WA Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA

An investigation using four test cases compared performance of aselection-based and a ranking fitness-based GA approach on different types of three-objectiveproblems. For these problems, both approaches reasonably approximate the Pareto set. Results of thesimple constrained and 10-bar truss problems suggest that the N-branch GA is better suited tosolving constrained problems. The adjacent minima problem results favor the MOGA ***'s speed reducer is both highly constrained and has two similar objectives, and it is notclear which approach is preferred here. The MOGA finds more evenly distributed points on the Paretofront than the N-branch GA; however, the N-branch GA finds better solutions near the extremes of thePareto set where constraints are active. Based on the investigations, it is difficult to make adefinitive choice for the preferred MOGA approach. Like many aspects of the GA, the choice appearsproblem dependent. It seems that the N-branch tournament GA is preferred for three-objectiveproblems with solutions that have numerous active conslraints. The MOGA appears preferred forproblems in which two or more objectives are adjacent in the design space. However, for manyengineering problems, it may not be known whether two or more objectives are adjacent a priori. Thesuccess of the N-branch tournament GA demonstrates that a selection-based method can generatemulti-objective solutions that are comparable to those generated by a nondominance ranking method.

关键词： multi objective genetic algorithm Trusses Radiative Transfer Chemical Kinetics Premixed Flame Hydrocarbon Fuels Young's Modulus Astronautics Aeronautics Material Properties

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