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Full-Text Articles in Engineering
Process Modeling And Optimization Strategies, Sandip K. Lahiri
Process Modeling And Optimization Strategies, Sandip K. Lahiri
Dr. Sandip Kumar Lahiri
This paper presents artificial intelligence-based process modeling and optimization strategies, namely, support vector regression – differential evolution (SVR-DE) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR-DE approach, a support vector regression model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Differential Evolution (DE) with a view to maximize the process performance. DE possesses certain unique advantages over the commonly used gradient-based deterministic optimization algorithms. The SVR-DE is a new strategy for chemical process modeling and optimization. The major advantage …
Process Modeling And Optimization Strategies Integrating Neural Networks And Differential Evolution, Nadeem Muhammed Khalfe
Process Modeling And Optimization Strategies Integrating Neural Networks And Differential Evolution, Nadeem Muhammed Khalfe
Nadeem Khalfe
This article presents an artificial intelligence-based process modeling and optimization strategy, namely artificial neural networks—differential evolution (ANN-DE) for modeling and optimizing catalytic industrial ethylene oxide (EO) reactors. In the ANN-DE approach, an artificial neural network model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using DEs with a view to maximizing the process performance. The DE possesses certain unique advantages over the commonly used gradient-based deterministic optimization algorithms. The ANN-DE is a new strategy for chemical process modeling and optimization. The major advantage of the strategy …
Synthesis Design Of Artificial Magnetic Metamaterials Using A Genetic Algorithm, Chien Hsun Chen, P. Y. Chen, H. Wang, J. H. Tsai, W. X. Ni
Synthesis Design Of Artificial Magnetic Metamaterials Using A Genetic Algorithm, Chien Hsun Chen, P. Y. Chen, H. Wang, J. H. Tsai, W. X. Ni
Chien Hsun Chen
In this article, we present a genetic algorithm (GA) as one branch of artificial intelligence (AI) for the optimization-design of the artificial magnetic metamaterial whose structure is automatically generated by computer through the filling element methodology. A representative design example, metamaterials with permeability of negative unity, is investigated and the optimized structures found by the GA are presented. It is also demonstrated that our approach is effective for the synthesis of functional magnetic and electric metamaterials with optimal structures. This GA-based optimization-design technique shows great versatility and applicability in the design of functional metamaterials.