Application of Process Neural Network Controller Based on Deep Learning in Chemical Process Control

Abstract

The chemical industry, as the core component of the industrial system, generally presents complex characteristics such as multivariable coupling, significant lag effects, and dynamic changes in working conditions in the production process, which puts strict requirements on control accuracy, stability, and adaptability. Traditional control methods are difficult to accurately capture the dynamic laws of the process when dealing with complex working conditions, and are prone to problems such as delayed control response and insufficient anti-interference ability, which restrict the improvement of production efficiency and the stability of product quality. Deep learning, with its powerful feature mining and complex relationship fitting capabilities, provides a new technological path for industrial control. Process neural networks are optimized for dynamic process modeling and can effectively process time series data, which is in line with the dynamic characteristics of chemical processes. The organic integration of the two can break through the limitations of traditional control technology and build a precise control system suitable for complex chemical scenarios. Based on this, this article focuses on the practical needs of chemical process control and designs a process neural network controller based on deep learning. Through architecture optimization, module collaboration, and algorithm upgrades, it improves the adaptability and control performance to complex working conditions. The research and application of this controller will provide technical support for the safe and stable operation of chemical production, efficient utilization of resources, and assist in the intelligent transformation and high-quality development of the industry.