信息与控制2026，Vol.55Issue(4)：665-677,13.DOI:10.13976/j.cnki.xk.2025.0431
基于图神经网络的醇沉过程智能控制方法
Intelligent Control Method for Alcohol Precipitation Processes Based on Graph Neural Networks
摘要
Abstract
The alcohol precipitation process is widely used in biopharmaceuticals and bioengineering,especially in the extraction of biomacromolecules such as proteins and polysaccharides.In view of the fact that traditional optimization methods are difficult to deal with its nonlinearities and inter-variable coupling,a multi-agent deep deterministic policy gradient(MADDPG)algorithm combining prioritized experience replay(PER)and graph convolutional network(GCN)is proposed.The algorithm introduces the GCN structure in the value network to capture the collaborative relationship between agents,and prioritizes the sampling of key experiences through the PER mechanism to accelerate policy learning and improve training efficiency.Experimental results show that the proposed algorithm has faster convergence speed and better policy performance in the alcohol precipitation process control task than some existing methods,verifying its advantages in complex multi-parameter optimization.关键词
醇沉/多智能体系统/优先经验回放/图卷积网络/深度强化学习Key words
alcohol precipitation/multi-agent system/prioritized experience replay/graph convolutional network/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
郭鸿斌,王艳,刘相,王团结,戴翠红..基于图神经网络的醇沉过程智能控制方法[J].信息与控制,2026,55(4):665-677,13.基金项目
长三角科技创新共同体联合攻关计划项目(2023CSJGG1700) （2023CSJGG1700）
