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Design of shell-and-tube heat exchangers for multiple objectives using elitist non-dominated sorting genetic algorithm with termination criteria

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APPLIED THERMAL ENGINEERING 2016年 93卷 888-899页

作者： Wong, Justin Y. Q. Sharma, Shivom Rangaiah, G. P. Natl Univ Singapore Dept Chem & Biomol Engn Singapore 117585 Singapore

In this work, Excel-based multi-objective optimization (EMOO) program, based on the elitist non-dominated sorting genetic algorithm, is improved by implementing two termination criteria: a Chi-squared test-based termination criterion and a novel termination criterion based on steady-state detection. The termination criteria were shown to perform reliably during the MOO of four constrained test functions. Then, the improved EMOO program was applied to the design of several shell-and-tube heat exchangers (STHE) from the literature;these STHE are simulated and then optimized using the EMOO program for two objectives: capital cost and operating cost. It provided better optimal results compared to those in the literature. The termination criteria performed reliably for MOO of STHE design, showing their applicability on industrial MOO problems. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Shell-and-tube heat exchangers Multi-objective optimization elitist non-dominated sorting genetic algorithm Termination criteria

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Multi-objective optimization of reverse osmosis desalination units using different adaptations of the non-dominated sorting genetic algorithm (NSGA)

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COMPUTERS & CHEMICAL ENGINEERING 2005年第9期29卷 1977-1995页

作者： Guria, C Bhattacharya, PK Gupta, SK Indian Inst Technol Dept Chem Engn Kanpur 208016 Uttar Pradesh India Birla Inst Technol Dept Polymer Engn Ranchi 835215 Bihar India

Multi-objective optimization using genetic algorithm (GA) is carried out for the desalination of brackish and sea water using spiral wound or tubular modules. A few sample optimization problems involving two and three objective functions are solved, both for the operation of an existing plant (which is almost trivial), as well as, for the design of new plants (associated with a higher degree of freedom). The possible objective functions are: maximize the permeate throughput, minimize the cost of desalination, and minimize the permeate concentration. The operating pressure difference, Delta P, across the membrane is the only important decision variable for an existing unit. In contrast, for a new plant, Delta P, the active area, A, of the membrane, the membrane to be used (characterized by the permeability coefficients for salt and water), and the type of module to be used (spiral wound/tubular, as characterized by the mass transfer coefficient on the feed-side), are the important decision variables. Sets of non-dominated (equally good) Pareto solutions are obtained for the problems studied. The binary coded elitist non-dominated sorting genetic algorithm (NSGA-II) is used to obtain the solutions. It is observed that for maximum throughput, the permeabilities of both the salt and the water should be the highest for those cases studied where there is a constraint on the permeate concentration. If one of the objective functions is to minimize the permeate concentration, the optimum permeability of salt is shifted towards its lower limit. The membrane area is the most important decision variable in designing a spiral wound module for desalination of brackish water as well as seawater, whereas Delta P is the most important decision variable in designing a tubular module for the desalination of brackish water (where the quality of the permeate is of prime importance). The results obtained using NSGA-II are compared with those from recent, more efficient, algorithms, namel

关键词： multi-objective optimization elitist non-dominated sorting genetic algorithm jumping genes genetic algorithm optimization desalination brackish water seawater reverse osmosis membranes

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Vapor recompressed batch distillation: Optimizing reflux ratio at variable mode

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COMPUTERS & CHEMICAL ENGINEERING 2019年 124卷 184-196页

作者： Parhi, Sidharth Sankar Rangaiah, Gade Pandu Jana, Amiya K. Indian Inst Technol Dept Chem Engn Energy & Proc Engn Lab Kharagpur 721302 W Bengal India Natl Univ Singapore Dept Chem & Biomol Engn Singapore 117585 Singapore

This work aims at optimizing a vapor recompressed batch distillation that runs at variable reflux mode by employing a multi-objective optimization (MOO) strategy. This involves the formulation of optimization problem using factorial analysis for identifying dominating variables followed by solving the optimization problem using the elitist non-dominated sorting genetic algorithm. The selection of an optimal point is made by employing the technique for order of preference by similarity to ideal solution (TOPSIS) method with entropy information for weighting of objective functions. Here, two conflicting performance indicators, i.e., total annual cost (TAC) and total annual production (TAP) are considered as objective functions. At first, for the existing plant scenario, the conventional batch distillation column operated at variable reflux ratio mode is optimized and then its retrofit is proposed by employing an external thermal arrangement under vapor recompression framework. Subsequently, the optimal vapor recompressed batch distillation is separately developed for setting up a new plant. Finally, the proposed vapor recompressed schemes are demonstrated with an example system and their performances are quantified in terms of energy savings, TAC and TAP. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Batch distillation with variable reflux ratio Vapor recompression Factorial design methodology Multi-objective optimization elitist non-dominated sorting genetic algorithm Energy and cost savings

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Multi-objective optimization to improve energy, economic and, environmental life cycle assessment in waste-to-energy plant

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WASTE MANAGEMENT 2021年 127卷 147-157页

作者： Mayanti, Bening Songok, Joel Helo, Petri Univ Vaasa Vaasa Energy Business Innovat Ctr Yliopistonranta 10 Vaasa 65200 Finland Univ Vaasa Sch Technol & Innovat Elect Engn Yliopistonranta 10 Vaasa 65200 Finland Univ Vaasa Dept Prod Networked Value Syst POB 700 FI-65100 Vaasa Finland

This paper presents a multi-objective optimization (MOO) of waste-to-energy (WtE) to investigate optimized solutions for thermal, economic, and environmental objectives. These objectives are represented by net efficiency, total cost in treating waste, and environmental impact. Integration of the environmental objective is conducted using life cycle assessment (LCA) with endpoint single score method covering direct combustion, reagent production and infrastructure, ash management, and energy recovery. Initial net efficiency of the plant was 16.27% whereas the cost and environmental impacts were 75.63(sic)/ton-waste and -1.21 x 10(8) Pt/ton-waste, respectively. A non-dominated sorting genetic algorithm (NSGA-II) is applied to maximize efficiency, minimize cost, and minimize environmental impact. Highest improvement for single objective is about 13.4%, 10.3%, and 14.8% for thermal, economic, and environmental, respectively. These improvements cannot be made at once since the objectives are conflicting. These findings highlight the significance role of decision makers in assigning weight to each objective function to obtain the optimal solution. The study also reveals different influence among decision variable, waste input, and marginal energy sources. Finally, this paper underlines the versatility of using MOO to improve WtE performance regarding the thermal, economic, and environmental aspects without requiring additional investment. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Multi-objective optimization Life cycle assessment Life cycle costing Energy efficiency Waste-to-energy elitist non-dominated sorting genetic algorithm

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Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis

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APPLIED SOFT COMPUTING 2011年第1期11卷 1427-1438页

作者： Qasem, Sultan Noman Shamsuddin, Siti Mariyam Univ Teknol Malaysia Fac Comp Sci & Informat Syst Soft Comp Res Grp Skudai 81310 Johor Malaysia

This paper proposes an adaptive evolutionary radial basis function (RBF) network algorithm to evolve accuracy and connections (centers and weights) of RBF networks simultaneously. The problem of hybrid learning of RBF network is discussed with the multi-objective optimization methods to improve classification accuracy for medical disease diagnosis. In this paper, we introduce a time variant multi-objective particle swarm optimization(TVMOPSO) of radial basis function (RBF) network for diagnosing the medical diseases. This study applied RBF network training to determine whether RBF networks can be developed using TVMOPSO, and the performance is validated based on accuracy and complexity. Our approach is tested on three standard data sets from UCI machine learning repository. The results show that our approach is a viable alternative and provides an effective means to solve multi-objective RBF network for medical disease diagnosis. It is better than RBF network based on MOPSO and NSGA-II, and also competitive with other methods in the literature. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Radial basis function network Hybrid learning Particle swarm optimization Time variant multi-objective particle swarm optimization Multi-objective particle swarm optimization elitist non-dominated sorting genetic algorithm

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Improved heat exchanger network retrofitting using exchanger reassignment strategies and multi-objective optimization

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ENERGY 2014年 67卷 584-594页

作者： Sreepathi, Bhargava Krishna Rangaiah, G. P. Natl Univ Singapore Dept Chem & Biomol Engn Singapore 117585 Singapore

HEN (heat exchanger network) retrofitting is useful to reduce energy consumption in the existing process industries. In this study, several ERS (exchanger reassignment strategies) for HEN retrofitting are proposed and tested by performing single objective optimization on retrofit problems using IDE (integrated differential evolution). A reassignment strategy which includes practical considerations is also developed and used in MOO (multi-objective optimization) on retrofit problems. Results of application problems show that ERS play an important role in obtaining better solutions, and that MOO provides many optimal solutions for HEN retrofitting to decision makers for choosing one of them for implementation. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Heat exchanger network retrofitting Exchanger reassignment strategy Multi-objective optimization Integrated differential evolution elitist non-dominated sorting genetic algorithm

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Multi-objective optimization of vapor recompressed distillation column in batch processing: Improving energy and cost savings

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APPLIED THERMAL ENGINEERING 2019年 150卷 1273-1296页

作者： Parhi, Sidharth Sankar Rangaiah, Gade Pandu Jana, Amiya K. Indian Inst Technol Kharagpur Dept Chem Engn Energy & Proc Engn Lab Kharagpur 721302 W Bengal India Natl Univ Singapore Dept Chem & Biomol Engn Singapore 117585 Singapore

This works aims at formulating a multi-objective optimization (MOO) strategy to improve the energetic and economic potential of a batch distillation through vapor recompression. The optimization strategy is developed based on elitist non-dominated sorting genetic algorithm along with the selection of an optimal point implementing the technique for order of preference by similarity to ideal solution method by using entropy information for weighting. The factorial design methodology is incorporated to find the dominating variables, which are further utilized for the formulation of MOO problem. Process optimization involves two or more objectives, which are often conflicting in nature that leads to many equally-good optimal solutions from the perspective of the given objectives. Here, two conflicting performance criteria, i.e., total annual cost and total annual production are proposed as two objective functions. With this, we first optimize a conventional batch distillation (CBD) followed by its retrofitted scheme with vapor recompression. Then, we propose an optimal vapor recompressed batch distillation keeping in mind the case of setting up a new plant. Finally, the energetic and economic potential of the vapor recompression based schemes are evaluated with reference to the CBD by simulating and optimizing a nonreactive and a reactive example system.

关键词： Vapor recompressed batch distillation Factorial design methodology elitist non-dominated sorting genetic algorithm Energy savings Total annual cost savings

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Multi-objective optimization of two alkali catalyzed processes for biodiesel from waste cooking oil

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ENERGY CONVERSION AND MANAGEMENT 2014年 85卷 361-372页

作者： Patle, Dipesh S. Sharma, Shivom Ahmad, Z. Rangaiah, G. P. Univ Sains Malaysia Sch Chem Engn Nibong Tebal 14300 Penang Malaysia Natl Univ Singapore Dept Chem & Biomol Engn Singapore 117576 Singapore

In view of the finite availability and environmental concerns of fossil fuels, biodiesel is one of the promising fuel alternatives. This study considers waste cooking palm oil with 6% free fatty acids (FFA) as feed-stock, which facilitates its better utilization and promotes sustainability. Two biodiesel production processes (both involving esterification catalyzed by sulfuric acid and trans-esterification catalyzed by sodium hydroxide) are compared for economic and environmental objectives. Firstly, these processes are simulated, considering detailed constituents of palm oil and also detailed kinetics for both esterification and trans-esterification, in Aspen Plus simulator. Subsequently, both the processes are optimized considering profit, heat duty and organic waste as objectives, and using an Excel-based multi-objective optimization (EMOO) program for the elitist non-dominated sorting genetic algorithm-II (NSGA-II). The results show that the profit improves with the increase in heat duty, and that the profit increase is accompanied by larger amount of organic waste. Process 1 having three trans-esterification reactors produces significantly lower organic waste (by 32%), requires lower heat duty (by 39%) and slightly more profitable (by 1.6%) compared to Process 2 having a single trans-esterification reactor and also a different separation sequence. Overall, the obtained quantitative trade-offs between objectives enable better decision making about the process design for biodiesel production from waste cooking oil. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Biodiesel Waste cooking oil Multi-objective optimization elitist non-dominated sorting genetic algorithm MS Excel

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Task scheduling for multi-tier shuttle warehousing systems

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2015年第19期53卷 5884-5895页

作者： Wang, Yanyan Mou, Shandong Wu, Yaohua Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China

Multi-tier shuttle warehousing systems are increasingly popular because of their high flexibility and robustness. These systems consist of a multi-tier shuttle sub-system that controls horizontal movement and a lift sub-system that manages vertical movement. The combination of shuttles and lifts undertakes inbound/outbound tasks instead of the stacker crane in conventional automated storage and retrieval systems. Scheduling different devices to reduce expected cycle time is an important concern. Thus, we propose a time sequence mathematical model of task operation on the basis of the movement characteristics of shuttles and lifts. The task scheduling problem between shuttles and lifts is converted into an assembly line parallel job problem by analysing the mathematical model, which generates the scheduling task queue model in the specified time window. In addition, a Pareto optimal based on an elitist non-dominated sorting genetic algorithm is adopted to solve the multi-objective optimisation function in the task scheduling problem. Finally, we illustrate the findings of the study through a practical example. Results show that the optimisation scheduling solution can reduce the total lift idle time and the total shuttle waiting time. Furthermore, it can improve warehousing efficiency and lower operation costs.

关键词： task scheduling problem multi-tier shuttle warehousing system elitist non-dominated sorting genetic algorithm warehouse operation process

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Optimal geometric tolerance design framework for rigid parts with assembly function requirements using evolutionary algorithms

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第9-12期73卷 1219-1236页

作者： Saravanan, A. Balamurugan, C. Sivakumar, K. Ramabalan, S. MAM Coll Engn Dept Mech Engn Tiruchirappalli 621105 Tamil Nadu India Bannari Amman Inst Technol Dept Mech Engn Sathyamangalam 638401 Tamil Nadu India EGS Pillay Engn Coll Nagapattinam 611002 Tamil Nadu India

Tolerance design is always a challenging task for engineers, since it need to satisfy multidisciplinary functions. Engineering design is done in two stages: assembly design and detail design. In the first stage, an assembly is designed considering certain system level functions and in secondary detail design stage;decomposition of the assembly is done and process tolerancing is employed for the parts. At the secondary detail design stage, designer adopts geometrical dimensioning and tolerancing (GD&T) concepts for process tolerancing. Hence, assembly and detail design are done in different phases with dissimilar perspectives. As a result, geometric tolerance design often lands in conflict, redesign, and in the case of concurrent engineering, costly reiterations are performed. This conflict occurs because of two vital reasons: (1) a gap exists between these two design stages and no common relation between them;(2) GD&T is adopted in the secondary stage, which is not available in primary stage. This paper offers a framework for a design engineer to bridge the gap and to establish the relation between these stages. A nonlinear combinatorial optimization problem is framed based on assembly function requirement (AFR), and tolerance values are optimized with appropriate constraints. nontraditional elitist non-dominated sorting genetic algorithm (NSGA-II) and differential evolution (DE) algorithms are used to solve the problem. For the allocated position tolerances, appropriate sensitive factors are indicated to facilitate design improvement. Finally, a case study is used to illustrate the complete framework.

关键词： Assembly function requirement Composite position tolerance Differential evolution algorithm elitist non-dominated sorting genetic algorithm Geometrical dimensioning and tolerancing Sensitive factor and tolerance optimization

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