Open AccessOptimization of Extraction Process Based on Neural Network
Author(s) -
Jing Sun,
Qiong Chen
Publication year - 2022
Publication title -
asian journal of chemical sciences
Language(s) - English
Resource type - Journals
ISSN - 2456-7795
DOI - 10.9734/ajocs/2022/v11i219117
Subject(s) - artificial neural network , process (computing) , extraction (chemistry) , computer science , process engineering , artificial intelligence , process optimization , engineering , chromatography , chemistry , environmental engineering , operating system
Liquid-liquid extraction is a chemical unit operation that utilizes the difference in solubility or distribution ratio of target components in two immiscible solvents to achieve separation, extraction or purification. There are many factors that affect the extraction efficiency, and it is difficult to quickly optimize the process using traditional methods. Artificial neural network is a system structure composed of multiple artificial neuron models, with functions such as self-learning, associative storage and fault tolerance. It can be used for optimization or control of multi-variable complex systems, and has been successfully applied to the extraction process of various products. optimization. This paper discusses the basic situation of artificial neural network, and analyzes the research progress of extraction process optimization based on neural network.