# Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network
## 1 Introduction  
Because of the complex reactor production process, it is difficult to establish an accurate mathematical model for most real industrial control systems, and the reactor temperature is difficult to control due to the reactor's complex structure, physical exothermic reaction, external environment interference, and other unstable factors. The temperature of the reactor is challenging to control because the temperature of the reactor has two reaction processes: a reaction heating stage and heat release. The temperature properties of the two reactions are opposed. If excess heat is not removed during the reaction process, the temperature in the reactor will become too high, affecting product quality and manufacturing safety. If you remove too much heat, the product will respond too slowly, and it will not be qualified. As can be seen, precise reactor temperature control is closely related to product quality and production safety. Furthermore, the enterprise's reactor capacity is generally large, resulting in a thick reactor wall, heat release, and heat absorption of the reaction process, and with the progress of the reaction process, the irregular change of the heat conducting medium is also very sensitive to external influence. The reaction kettle is a nonlinear, large hysteresis-controlled object for these systems. The temperature control of reactors has always been the emphasis and challenge of chemical enterprises in recent years. This subject has great research value because the temperature of the reactor affects not only the quality of pharmaceuticals but also the life safety of workers in enterprises. The reaction kettle in these setups is a nonlinear, massive hysteresis-regulated entity. To manage the temperature of the reactor, complex control procedures are required due to its temperature control characteristics, which include a high lag, delay, and nonlinearity. Researchon how to increase the temperature control effect of reactors is increasingly becoming a topic of increasing difficulty and interest due to the increasing production quality needs of modern businesses. In recent years, numerous researchers have suggested numerous predictive control algorithms based on nonlinear models, such as the Wiener model [1; 2], the neural network model [3], and the support vector machine model [4; 5]. Based on these models applied to the temperature management of reactors, favorable results have been obtained. Currently, there are numerous studies on the prediction of neural networks. [PERSON] et al. [6] used the back propagation neural network based on the particle swarm optimization algorithm to study the prediction of the high-speed grinding temperature of titanium matrix composites, and verified through experiments that the PSO-BP method has more advantages over other methods in predicting grinding temperature in terms of convergence rate, fitting accuracy, and prediction accuracy. [PERSON] et al. [7] proposed a multi-variable prediction method for short-term power loads based on chaos theory and a radial basis function neural network and found through experimental data that this method significantly improved the accuracy of prediction. [PERSON] et al. [8] proposed a method to optimize the BP neural network by using a chaotic particle swarm optimization algorithm to improve the prediction accuracy of PM\({}_{2.5}\) concentration in the atmosphere. The results show that the CPSO-BP neural network has better prediction performance than the traditional BP neural network. [PERSON] [9] proposed an improved radial basis function neural network model based on CPSO to solve the nonlinear control problem. The results showed that the model had obvious advantages in drug saving, small overshooting, and shortened adjustment time, thus achieving the lowest cost effect. [PERSON] et al. [10] proposed a model based on the improved Sparrow algorithm to optimize the RBF-BP neural network.