Synthesis of cost-optimal heat exchanger networks using differential evolution

Yerramsetty, Krishna; Murty, C.V.S.

doi:10.1016/j.compchemeng.2007.10.005

Cited by 106 publications

(38 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yerramsetty and Murty [33] reported a network of 43,538 $/yr in 4980 s by their DE algorithm. The twolevel method proposed by this paper is then applied to the 10SP1.…”

Section: Casementioning

confidence: 98%

“…It seems that the optimal results involving stream splits are better than the results with no splits as seen from Table 2. However, it should be pointed out that stream splits will not only increase the computational complexity, but also result in additional cost such as valves, piping, control system, etc [33].…”

Section: Casementioning

confidence: 99%

“…Theoretically, an infinite time schedule of SA can guarantee the global optimum and a good suboptimal solution can be obtained in a finite time. Consider the following optimization problem: min f ðxÞ Subject to x2S (33) where S denotes the feasible region of x. Supposed x new is a random move generated in the neighbor of x.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

“…Another contribution of their work is to introduce the concept of 'HEN level' for structure representation which was then used in a two-level synthesis method of HENs combining harmony search (HS) and sequential quadratic programming (SQP) [32]. Yerramsetty and Murty [33] proposed a differential evolution (DE) algorithm for the synthesis of HENs and also utilized the structure representation similar to 'HEN level'. Silva et al [34] and Huo et al [35] respectively presented a particle swarm optimization (PSO) method and a GA/PSO algorithm for finding cost optimal network.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Efficient simultaneous synthesis for heat exchanger network with simulated annealing algorithm

Peng

Cui

2015

Applied Thermal Engineering

View full text Add to dashboard Cite

“…Yerramsetty and Murty [33] reported a network of 43,538 $/yr in 4980 s by their DE algorithm. The twolevel method proposed by this paper is then applied to the 10SP1.…”

Section: Casementioning

confidence: 98%

Section: Casementioning

confidence: 99%

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient simultaneous synthesis for heat exchanger network with simulated annealing algorithm

Peng

Cui

2015

Applied Thermal Engineering

View full text Add to dashboard Cite

“…Recently, there have been successful applications of heuristic optimization methods motivated by nature-inspired evolution, e.g. genetic algorithms (GAs) (Lewin, Wang, and Shalev 1998), SA (Athier et al 1997), differential evolution (DE) (Yerramsetty and Murty 2008) and particle swarm optimization (PSO) (Silva et al 2010), to handle the optimization of the integer variables. Other algorithms have also been used, such as tabu (Lin and Miller 2004) and harmony search (Khorasany and Fesanghary 2009).…”

Section: Introductionmentioning

confidence: 99%

Bi-level heat exchanger network synthesis with evolution method for structure optimization and memetic particle swarm optimization for parameter optimization

Wang

Cui

Xiao

et al. 2016

Engineering Optimization

View full text Add to dashboard Cite

2016):Bi-level heat exchanger network synthesis with evolution method for structure optimization and memetic particle swarm optimization for parameter optimization, Engineering Optimization, ABSTRACTThe synthesis of heat exchanger networks (HENs) is a complex problem because of the nonlinearity that results from the integer and continuous variables. Here, a bi-level algorithm for the optimal design of a HEN is proposed that attempts to optimize separately the integer and continuous variables on two levels. The master level is a problem-oriented evolution method generating new candidate HEN structures. The slave level is a memetic particle swarm optimization, an improved particle swarm optimization combined with a local search component, improvement of neighbourhood topologies and control parameter preference. The slave level minimizes the total annual cost (TAC) of a given structure received from the master level, and then sends this value back to the master level for structure evolution. The proposed bi-level method is applied to several cases taken from the literature, which demonstrate its reliable search ability in both structure space and continuous variable space and its ability to optimize the system, producing generally lower TACs than previously used methods. ARTICLE HISTORY

show abstract

Large‐scale heat exchanger networks synthesis using simulated annealing and the novel rocket fireworks optimization

et al. 2016

View full text Add to dashboard Cite

Heat Exchanger Network (HEN) synthesis is an important field of study in process engineering. However, obtaining optimal HEN design is a complex task. When mathematically formulated, it may require sophisticated methods to achieve good solutions. The complexity increases even more for large-scale HEN. In this work, a hybrid meta-heuristic method is presented. A rather simple Simulated Annealing approach is used for the combinatorial level, while a strategy named rocket fireworks optimization is developed and applied to the continuous domain. An advantage over other approaches is that the algorithm was written in C11, which is free and faster when compared to many other languages. The developed method was able to provide the lowest costs solutions reported so far to six cases well studied in the literature. An important feature of the approach here proposed is that, differently from other approaches, it does not split HEN into smaller problems during the optimization.

show abstract

Synthesis of cost-optimal heat exchanger networks using differential evolution

Cited by 106 publications

References 26 publications

Efficient simultaneous synthesis for heat exchanger network with simulated annealing algorithm

Efficient simultaneous synthesis for heat exchanger network with simulated annealing algorithm

Bi-level heat exchanger network synthesis with evolution method for structure optimization and memetic particle swarm optimization for parameter optimization

Large‐scale heat exchanger networks synthesis using simulated annealing and the novel rocket fireworks optimization

Contact Info

Product

Resources

About