Simulation and Optimization of Ethane Recovery Unit Using Evolutionary Algorithms and Artificial Neural Networks

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Pakravesh, Hallas | 2009

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 39100 (06)
University: Sharif University of Technology
Department: Chemical and Petroleum Engineering
Advisor(s): Rashtchian, Davood
Abstract:
Nowadays evolutionary algorithms help developers to solve many problems in applied science and engineering aspects. So the main goal of this thesis is to apply and verify the ability of evolutionary algorithms and artificial neural networks in optimization of Ethane recovery unit. Algorithms applied in this study are Ant Colony Algorithm, Artificial Immune Systems Algorithm, Incremental Evolutionary Algorithm, Chaotic Based Algorithm, Variable Population Size Genetic Algorithm, Frog Leaping Algorithm, Frog Leaping with Bacterial Optimization Approach and Different types of Particle Swarm Optimization Algorithm. Optimization methods based on local search are also applied in order to compare with evolutionary ones. Applying so called optimization algorithms and artificial neural networks for Ethane recovery unit, improves purity of Ethane in the product. Among different types of evolutionary optimization algorithms, Frog Leaping with Bacterial Optimization Approach and Incremental Evolutionary Algorithm have the best results while methods based on local search come to worst results
Keywords:
Evolutionary Algorithm ; Optimization ; Artificial Neural Network ; Ethane Recovery Unit

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