An Intelligent Control Method for Alcohol Precipitation Processes Based on Graph Neural Networks

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 nonlinear and inter-variable coupling characteristics, 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.