Self-Optimization of Reactive Sections for High Quality Production in Petroleum Refining Industry

Abstract

Hardware based sensing frameworks such as cooperative fuel research (CFR) engines are conventionally used to monitor research octane number (RON) in the petroleum refining industry. In this work, machine learning techniques are employed to predict the RON of two petroleum refining processes: (1) integrated naphtha reforming and isomerization process and (2) fluid catalytic cracking process. A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of ± 5\% in process conditions such as temperature, pressures, flow rates, etc. Generated data was used to train support vector machines, gaussian process regression, artificial neural networks, regression trees, and ensemble trees. Hyperparameter tuning was performed to enhance the prediction capabilities of gaussian process regression (GPR), artificial neural network (ANN), support vector machines (SVM), ensemble tree (ET) and regression tree (RT) models. Performance analysis of machine learning models indicates that in case of integrated naphtha reforming and isomerization process GPR, ANN, and SVM have R 2 values of 0.99, 0.978, and 0.979 and RMSE values of 0.108, 0.262, and 0.258, respectively. GPR, ANN, and SVM performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables. ET and RT had an R2 value of 0.94 and 0.89. In case of fluid catalytic cracking GPR, SVM, and ANN have R 2 values of 0.97, 0.969, and 0.963 and RMSE values of 0.3908, 0.3934, and 0.4333, respectively. ET and RT had an R2 value of 0.941 and 0.88. The GPR model was used as a surrogate model for fitness function evaluations in two optimization frameworks based on the genetic algorithm and particle swarm method. Particle swarm optimization performed marginally better than genetic algorithm. The proposed methodology of surrogatebased optimization will provide a platform for plant-level implementation to realize the concept of industry 4.0 in the refinery.