Research on Optimization for Methods Light Hydrocarbon Recovery Processes

Abstract

 This study provides a systematic review of the fundamental principles and optimization strategies for light hydrocarbon recovery, focusing on thermodynamics-based process simulation and intelligent optimization methods. The study explores the integration of HYSYS process simulation with deep learning techniques, leveraging neural networks, genetic algorithms, and other advanced approaches to optimize process parameters, enhance recovery efficiency, and reduce energy consumption. Deep learning enables accurate prediction of key process variables and, when combined with optimization algorithms, significantly improves system performance. The synergy between HYSYS modeling and intelligent optimization offers a novel framework for enhancing the efficiency and accuracy of light hydrocarbon recovery, contributing to the advancement of smart process optimization.