Artificial intelligence approach in crude distillation unit operation

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.