Artificial intelligence approach in crude distillation unit operation
1 Department of Chemical Engineering, University of Port Harcourt, PMB 5323 Port Harcourt, Rivers State, Nigeria.
2 Department of Petroleum Engineering, Niger Delta University,PMB 071, Wilberforce Island, Bayelsa State, Nigeria.
Research Article
Global Journal of Engineering and Technology Advances, 2021, 09(02), 075–082.
Article DOI: 10.30574/gjeta.2021.9.2.0155
Publication history:
Received on 18 October 2021; revised on 22 November 2021; accepted on 24 November 2021
Abstract:
This study incorporates the use of Artificial Intelligence in the monitoring of atmospheric distillation unit of large scale refining operation using Google AutoML tables, Jupyter, and Python software. The process involved training, evaluation, improvement, and deployment of the models based on the input data. The predicted yield (vol %) for the models were: Auto ML model: liquefied petroleum gas (LPG) - 1.41 , straight run gasoline (SRG)– 4.96, straight run naphtha (SRN) – 17.87, straight run kerosene (SRK) – 14.5, light diesel oil (LDO) – 26.47, heavy diesel oil (HDO) – 2.7, and atmospheric residue (AR) –30.03; Jupyter Model: LPG – (0.93), SRG – (4.69), SRN – (17.24), SRK – (14.39), LDO – (26.43), HDO – (2.7), and AR – (30.18); and Python Model:LPG – (1.66) , SRG – (7.58), SRN – (11.68), SRK – (14.92), LDO – (24.77), HDO – (4.59), and AR – (24.59). The coefficient of determination (R2) values of 0.99981, 0.99943, and 0.93078 and Standard Error values of 0.240918, 0.419291, 3.536064, were obtained for the 3 models, respectively. All the software gave good predictions of the actual yield, although the Google Auto ML Table gave the best prediction. The training of the model is fundamental to its performance and precision.
Keywords:
Artificial intelligence; Crude distillation unit; Mathematical modeling; Machine learning; Refining; Processing
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