Author(s)

Lekan Taofeek Popoola, Gutti Babagana, Alfred Akpoveta Susu

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Department of Chemical Engineering, University of Maiduguri, Maiduguri, Nigeria.

This paper presents a comprehensive review of various traditional systems of crude oil distillation column design, modeling, simulation, optimization and control methods. Artificial neural network (ANN), fuzzy logic (FL) and genetic algorithm (GA) framework were chosen as the best methodologies for design, optimization and control of crude oil distillation column. It was discovered that many past researchers used rigorous simulations which led to convergence problems that were time consuming. The use of dynamic mathematical models was also challenging as these models were also time dependent. The proposed methodologies use back-propagation algorithm to replace the convergence problem using error minimal method.

Artificial Neural Network; Crude Oil Distillation Column; Genetic Algorithm Framework; Sigmoidal Transfer Function; Back-Propagation Algorithm

L. Popoola, G. Babagana and A. Susu, "A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework," Advances in Chemical Engineering and Science, Vol. 3 No. 2, 2013, pp. 164-170. doi: 10.4236/aces.2013.32020.