Heat Exchanger Network Synthesis without stream splits using parallelized and simplified simulated Annealing and Particle Swarm Optimization

Pavão, Leandro V.; Costa, Caliane Bastos Borba; Ravagnani, Mauro A.S.S.

doi:10.1016/j.ces.2016.09.030

Cited by 90 publications

(29 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In some cases, parameters must be tuned specifically in each problem, keeping in mind a trade‐off between solutions quality and computational time. In our tests, as well as previously presented in Pavão et al ., when the combinatorial SA initial temperature ( T 0 ) is set to a multiple of ten with two orders of magnitude less than usual problem TAC, the uphill moving features of SA are applied efficiently. Lower values lead the scheme to fast stagnation in local minima, while high values may cause excessive uphill moves to be performed and the method to be too time consuming, with a marginal gain in the final solutions quality.…”

Section: Case Studiesmentioning

confidence: 99%

“…Notably, SA and PSO was a promising hybridization, as presented in Pavão et al With relatively small number of iterations and a simplified no‐splits formulation, the method was able to find interesting results for medium sized problems. In this work, some features of that method are taken as basis to develop a new method to solve the nonisothermal mixing SWS (NIM‐SWS) formulation on large‐scale HENs.…”

Section: Introductionmentioning

confidence: 96%

“…Pavão et al have incorporated parallel processing (PP) techniques to a GA/PSO two‐level method. They also presented a technique based on SA and PSO applied to a nonsplit SWS, which used PP schemes as well …”

Section: Introductionmentioning

confidence: 99%

“…Some important current trends in HEN synthesis research use two level meta‐heuristic or hybrid schemes, which have achieved promising results in many benchmarking problems, as illustrated on Case studies section, where literature results are also presented. Particularly, SA has shown a strong capacity in topology searching in many works in the literature, while population based methods such as PSO, DE and HS demonstrate good results in the continuous space of the HEN synthesis problem. All of them are simpler to implement than deterministic methods.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2016

Self Cite

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

Section: Case Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…PSO is a meta-heuristic as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions [30,31]. Compared with the other evolutionary algorithm such as artificial fish swarm algorithm [32], genetic algorithm [33,34], artificial bee colony algorithm [35,36], and simulated annealing algorithm [37], the most important advantages of PSO are the few parameters needed to adjust and the easy implementation. IBPSO follows the action of chromosomes in genetic algorithm (GA) and sparsely calculates the eigenvectors of the input AHs.…”

mentioning

confidence: 99%

Monitoring of OSNR Using an Improved Binary Particle Swarm Optimization and Deep Neural Network in Coherent Optical Systems

Sun

Wei

et al. 2019

Photonics

View full text Add to dashboard Cite

A novel technique is proposed to implement optical signal-to-noise ratio (OSNR) estimation by using an improved binary particle swarm optimization (IBPSO) and deep neural network (DNN) based on amplitude histograms (AHs) of signals obtained after constant modulus algorithm (CMA) equalization in an optical coherent system. For existing OSNR estimation models of DNN and AHs, sparse AHs with valid features of original data are selected by IBPSO algorithm to replace the original, and the sparse sets are used as input vector to train and test the particle swarm optimization (PSO) optimized DNN (PSO-DNN) network structure. Numerical simulations have been carried out in the OSNR ranges from 10 dB to 30 dB for 112 Gbps PM-RZ-QPSK and 112 Gbps PM-NRZ-16QAM signals, and results show that the proposed algorithm achieves a high OSNR estimation accuracy with the maximum estimation error is less than 0.5 dB. In addition, the simulation results with different data input into the deep neural network structure show that the mean OSNR estimation error is 0.29 dB and 0.39 dB under original data and 0.29 dB and 0.37 dB under sparse data for the two signals, respectively. In the future dynamic optical network, it is of more practical significance to reconstruct the original signal and analyze the data using sparse observation information in the face of multiple impairment and serious interference. The proposed technique has the potential to be applied for optical performance monitoring (OPM) and is helpful for better management of optical networks.Photonics 2019, 6, 111 2 of 17 network resources. This phenomenon improves the transparency of an optical system but increases the complexity of network management [1]. The capacity of optical networks has been growing steadily, and the network architectures are becoming more dynamic, complex, and transparent. Optical signals in high-speed optical fiber networks are vulnerable to various transmission impairments, which can change dynamically with time. Therefore, an appropriate monitoring mechanism must be established throughout the optical network to provide accurate and real-time information on the quality of transmission links and the health of optical signals. optical performance monitoring (OPM) technology detects the performance parameters of transmission links or optical network nodes in optical networks, and understands the network status and signal transmission status in real time, and therefore processes and ensures the normal network operation and signal transmission. It is of great significance to guarantee the performance of signal transmission and optimize the allocation and management of network resources. Therefore, in order to realize the next generation dynamic reconfigurable optical network, it is necessary to monitor the important parameters, isolate faults in time, and optimize the processing to ensure the transmission quality of the network [2,3].The optical signal-to-noise ratio (OSNR) is one of the key parameters of OPM. It can realize fault management of an optical ...

show abstract

A simultaneous approach for the synthesis of multiperiod heat exchanger network using particle swarm optimization

et al. 2017

View full text Add to dashboard Cite

In the present paper an approach for the synthesis of multiperiod heat exchanger networks (HEN) using particle swarm optimization (PSO) is proposed, wherein all variables are optimized simultaneously. The model uses a stage‐wise superstructure in which stream splitting is considered. The HEN is synthesized for each period individually and a timesharing scheme procedure is used to integrate all the structures of different periods automatically in a unique step. The HEN is able to operate in all the considered conditions and heat transfer devices can be used for different streams in different periods. Two case studies from the literature were used to test the developed model and results for the total annual costs were better than those found in the literature.

show abstract

Heat Exchanger Network Synthesis without stream splits using parallelized and simplified simulated Annealing and Particle Swarm Optimization

Cited by 90 publications

References 34 publications

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

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

Monitoring of OSNR Using an Improved Binary Particle Swarm Optimization and Deep Neural Network in Coherent Optical Systems

A simultaneous approach for the synthesis of multiperiod heat exchanger network using particle swarm optimization

Contact Info

Product

Resources

About