Artificial intelligence approach in crude distillation unit operation

Obumneme Onyeka Okwonna Obumneme Onyeka Okwonna,Amalate Ann Jonathan Obuebite Amalate Ann Jonathan Obuebite

doi:10.30574/gjeta.2021.9.2.0155

Obumneme Onyeka Okwonna Obumneme Onyeka Okwonna, Amalate Ann Jonathan Obuebite Amalate Ann Jonathan Obuebite

Open Access

https://doi.org/10.30574/gjeta.2021.9.2.0155

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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.

Highlights

The oil and gas industry plays a crucial role in energy generation and is the mainstay and a major driver of most economies around the world
The need for process improvement in the refining process and its products implies the need for more sophisticated technologies and methods through the application of artificial intelligence (AI)
Much improvement has been made in applying auto machine learning (ML) in distillation as cited in these studies, the rigor of integrating neural networks and other optimization systems are still inbound.This study evaluates the application of Auto ML in the prediction of the output of a commercial distillation process, andto draw a comparison on the performance of different software

Summary

Introduction

The oil and gas industry plays a crucial role in energy generation and is the mainstay and a major driver of most economies around the world. Crude distillation unit, being the first stage of the refining process is a fundamental process in the value chain. It is a highly energy intensive process and demands high efficiency and the constant demand on the need to improve its operation through necessary due diligence [1]. It is imperative that AI can have major breakthroughs in the oil and gas industry, especially the downstream sector to harness its efficiency and effectiveness. This approach will involve data observation, preparation, planning, and model building [4]

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