Application of Artificial Neural Network (ANN) in the Optimization of Crude Oil Refinery Process: New Port-Harcourt Refinery

Main Article Content

M. N. Braimah

Abstract

Background: Optimizing the process conditions of the crude distillation unit is a main challenge for each refinery. Optimization increases profit by producing the required range of distillates at maximum yield and at minimum cost. To achieve an acceptable control of product quality an artificial neural network (ANN) can be used. ANNs are used for engineering purposes, such as pattern recognition, forecasting, and data compression. In the petroleum refinery industry, ANN has been used as controller in for the crude distillation unit. The aim of the current study was to use ANN to optimize and achieve control of product quality of crude distillation unit of an oil   refinery.

Materials: The research was carried out using the following materials; The design flowchart and the operating data of the crude distillation unit of the New Port Harcourt refinery, Simulation software (HYSYS 2006.5) and Matlab for the ANN.

Results: The ANN predicted the optimum operating conditions at which the atmospheric distillation unit (ADU) can operate with the least irreversibility and without changing the design and compromising the products quality. The corresponding exergy efficiency after optimization with ANN for the input variable combinations was 70.6% which was a great improvement because the exergy efficiency increased as compared to the base case of 51.9%.

Conclusion: Optimization using ANN, improved the efficiency of the ADU with the least irreversibility and without changing the design and compromising the products quality.

Keywords:
Atmospheric distillation unit, artificial neural network, exergy, optimization.

Article Details

How to Cite
Braimah, M. N. (2020). Application of Artificial Neural Network (ANN) in the Optimization of Crude Oil Refinery Process: New Port-Harcourt Refinery. Journal of Energy Research and Reviews, 5(4), 26-38. https://doi.org/10.9734/jenrr/2020/v5i430154
Section
Original Research Article

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