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This study is motivated by a growing popularity of artificial neural network for process modeling and regression problems. However, many ANN regression application studies are performed by expert' users who have a good understanding of ANN methodology. Since the quality of ANN models depends on a proper setting of ANN architecture and ANN meta-parameters, the main issue for practitioners trying to apply ANN regression is how to set these parameter values (to ensure good generalization performance) for a given data set. Non-expert users face a difficulty in finding an optimum ANN architecture and are often confused about how to choose the ANN meta parameters. The present paper addresses this issue and develops a new hybrid procedure to find the optimum ANN architecture and tunes the ANN parameters, thus relieving the non expert' users. This method incorporates hybrid artificial neural network and differential evolution technique (ANN-DE) for efficient tuning of ANN meta parameters. The algorithm has been applied for modeling of a commercial ethylene oxide (EO) reactor. The model developed through this hybrid algorithm can predict the EO reactor output with only 0.4% error.

Modeling of Commercial Ethylene Oxide Reactor: A Hybrid Approach by Artificial Neural Network & Differential Evolution