Optimization Model for Reactor Operation Simulation Based on Deep Learning Technology

With the rapid development of the chemical industry, chemical reactors are core equipment, and their operation optimization is crucial to improving productivity and saving energy. Traditional operation strategies often fail to take into account the multi-objective requirements of complex reaction systems, resulting in waste of resources. This paper introduces deep reinforcement learning technology to build an intelligent operation strategy model. First, the background and existing problems in the chemical industry are detailed, and then the model construction, training methods and experimental design are explained. By combining simulation with actual experiments, the application effect of the strategy under different working conditions is comprehensively investigated. Experimental results show that the new strategy improves yield and reduces energy consumption, achieving the highest possible perfect prediction (accuracy of 1), providing strong support for the sustainable development of the chemical industry.