Heat Exchanger Network Optimization Using Differential Evolution with Stream Splitting

2014 ◽  
Vol 625 ◽  
pp. 373-377 ◽  
Author(s):  
Ngo Thi Phuong Thuy ◽  
Rajashekhar Pendyala ◽  
Narahari Marneni
Keyword(s):  
Heat Exchanger ◽  
Differential Evolution ◽  
Network Optimization ◽  
Heat Recovery ◽  
Differential Evolution Algorithm ◽  
Main Tool ◽  
Heat Exchanger Network ◽  
Pinch Point ◽  
New Strategy ◽  
Evolution Algorithm

Reduction in energy consumption is an important task in process industry. The basic idea of heat exchanger network (HEN) is using cold streams to cool hot streams and hot streams to heat cold streams. Hence, synthesis and optimization of HEN is a main tool for improving heat recovery. This article introduces a new strategy for HEN optimization using differential evolution algorithm. The proposed method considers splitting stream at the pinch point, to minimize the total cost of the network. Primarily, the minimum approach temperature value is determined through super-targeting. Then, differential evolution is employed to specify the heat load of heat exchangers and splitting streams. The HEN structure obtained in this work has better economics and illustrates the better performance by this approach.

Heat Exchanger Network Optimization by Differential Evolution Method

2014 ◽  
Vol 564 ◽  
pp. 292-297 ◽  
Author(s):  
Ngo Thi Phuong Thuy ◽  
Rajashekhar Pendyala ◽  
Nejat Rahmanian ◽  
Narahari Marneni
Keyword(s):  
Heat Exchanger ◽  
Differential Evolution ◽  
Network Optimization ◽  
Energy Utilization ◽  
Heat Exchanger Network ◽  
De Algorithm ◽  
Global Cost ◽  
Pinch Technology ◽  
Recent Developments

The synthesis of heat exchanger network (HEN) is a comprehensive approach to optimize energy utilization in process industry. Recent developments in HEN synthesis (HENS) present several heuristic methods, such as Simulated Annealing (SA), Genetic Algorithm (GA), and Differential Evolution (DE). In this work, DE method for synthesis and optimization of HEN has been presented. Using DE combined with the concept of super-targeting, the optimization is determined. Then DE algorithm is employed to optimize the global cost function including the constraints, such as heat balance, the temperatures of process streams. A case study has been optimized using DE, generated structure of HEN and compared with networks obtained by other methods such as pinch technology or mathematical programming. Through the result, the proposed method has been illustrated that DE is able to apply in HEN optimization, with 16.7% increase in capital cost and 56.4%, 18.9% decrease in energy, global costs respectively.

scholarly journals An improved differential evolution algorithm with a restart technique to solve systems of nonlinear equations

2020 ◽  
Vol 10 (1) ◽  
pp. 118-136
Author(s):  
Jeerayut Wetweerapong ◽  
Pikul Puphasuk
Keyword(s):  
Differential Evolution ◽  
Nonlinear Equations ◽  
Nonlinear Models ◽  
Differential Evolution Algorithm ◽  
Systems Of Nonlinear Equations ◽  
Test Problems ◽  
De Algorithm ◽  
New Strategy ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

In this research, an improved differential evolution algorithm with a restart technique (DE-R) is designed for solutions of systems of nonlinear equations which often occurs in solving complex computational problems involving variables of nonlinear models. DE-R adds a new strategy for mutation operation and a restart technique to prevent premature convergence and stagnation during the evolutionary search to the basic DE algorithm. The proposed method is evaluated on various real world and synthetic problems and compared with the recently developed methods in the literature. Experiment results show that DE-R can successfully solve all the test problems with fast convergence speed and give high quality solutions. It also outperforms the compared methods.

An Opposition-Based Modified Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 309-313
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Recent Literature ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Algorithm Performance ◽  
De Algorithm ◽  
New Strategy ◽  
Evolution Algorithm

In this paper, a new opposition-based modified differential evolution algorithm (OMDE) is proposed. This algorithm integrates the opposed-learning operation with the crossover operation to enhance the convergence of the algorithm and to prevent the algorithm from being trapped into the local optimum effectively. Besides, we employed a new strategy to dynamic adjust mutation rate (MR) and crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark functions tested, the OMDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and its two improved algorithms (JADE and SaDE) that reported in recent literature.

Opposition-Based Modified Differential Evolution Algorithm for Power System Economic Load Dispatch

2013 ◽  
Vol 365-366 ◽  
pp. 178-181
Author(s):  
Hong Gang Xia ◽  
Wei Xiang Ding
Keyword(s):  
Power System ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Economic Load Dispatch ◽  
Algorithm Performance ◽  
Opposition Based Learning ◽  
De Algorithm ◽  
New Strategy ◽  
Evolution Algorithm

Opposition-based modified differential evolution algorithm (OMDE) is proposed for solving power System economic load dispatch in this paper. This algorithm integrates the opposition-based learning operation with the crossover operation to enhance the convergence of the algorithm and to prevent the algorithm from being trapped into the local optimum effectively. Besides, we employed a new strategy to dynamic adjust mutation rate (MR) and crossover rate (CR), which is aimed at further improving algorithm performance. Based on 6 units and 13 units power system experiment simulations, the OMDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other improved algorithms that reported in recent literature.

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