J. J. Macías-Hernández, P. Angelov and X. Zhou, “Soft Sensor for Predicting Crude Oil Distillation Side Streams Using Evolving Takagi-Sugeno Fuzzy Models. Results Outlined,” Proceedings of 2nd International Symposium on Evolving Fuzzy Systems, 7-9 September 2008, Lake District, IEEE Press, 2008, pp. 214-220.

has been cited by the following article:

• TITLE: 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

  AUTHORS: Lekan Taofeek Popoola, Gutti Babagana, Alfred Akpoveta Susu

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

  JOURNAL NAME: Advances in Chemical Engineering and Science, Vol.3 No.2, April 29, 2013

  ABSTRACT: 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.