Employing Data Analytics and Evolutionary Algorithms for Optimizing DownstreamRefinery Process Parameters

Abstract

The downstream refinery process is a complex and critical component of the petroleum industry, requiring continuous optimization to improve efficiency, product quality, and profitability. This paper explores the application of data analytics and evolutionary algorithms to optimize key process parameters in the downstream refinery process. The study utilizes historical process data from various refinery units, such as crude distillation, fluid catalytic cracking, and hydrotreating, to develop predictive models and identify critical process variables. Advanced data analytics techniques, including machine learning algorithms and multivariate statistical analysis, are
employed to uncover hidden patterns and relationships among the process parameters. Furthermore, evolutionary algorithms, such as genetic algorithms and particle swarm optimization, are applied to optimize the identified critical process parameters. These algorithms simulate the principles of natural evolution to search for optimal solutions in a complex and multi-dimensional parameter space. The optimization objectives include maximizing product yield, minimizing energy consumption, and ensuring product quality compliance. This research highlights the potential of integrating data analytics and evolutionary algorithms for optimizing downstream refinery
processes. The findings contribute to the advancement of smart manufacturing and data-driven decision-making in the petroleum industry, enabling refineries to adapt to changing market demands and regulatory requirements while maintaining a competitive edge.