# Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network
## 4 Conclusions  
* The control object in this study was the reactor of a chemical enterprise in Hengdian, Zhejiang, China. Because reactor temperature control is inaccurate, it is always a difficult issue for businesses to overcome. A chaotic particle algorithm CPSO is suggested in this paper to optimize the RBF-BP neural network model of the reactor temperature prediction control method. In this study, the CPSO optimization algorithm was used to correct the RBF-BP model's weights and thresholds, and the impact of initial weight and threshold uncertainties on the training efficiency of the RBF-BP combined neural network was investigated. Other prediction models were used in this study for prediction comparison at the same time. Among several prediction datasets, the CPSO-RBF-BP model outperformed the BP and RBF-BP models in terms of prediction accuracy. The CPSO-RBF-BP variant is extremely accurate.
* The simulation findings indicate that the proposed prediction method outperforms the BP and RBF-BP neural network prediction models in all aspects. The CPSO-RBF-BP mixed neural network model has a root mean square error of 17.3%, an average absolute error of 11.4%, and a fitting value of 99.791%. The CPSO-RBF-BP model  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample** & **BP** & \multicolumn{4}{c}{**RBF-BP**} & \multicolumn{4}{c}{**CPSO-RBF-BP**} \\
**Number** & \multicolumn{2}{c}{**Actual/Predicted/Error**} & \multicolumn{2}{c}{**Actual/Predicted/Error**} & \multicolumn{2}{c}{**Actual/Predicted/Error**} \\ \hline
20 & 7.51 & 7.83 & 0.32 & 7.65 & 7.62 & \(-\)0.03 & 21.95 & 21.90 & 0.05 \\
40 & 7.31 & 7.48 & 0.17 & 25.21 & 25.24 & 0.03 & 26.06 & 26.08 & 0.02 \\
60 & 18.74 & 18.85 & 0.11 & 18.91 & 18.92 & 0.01 & 7.51 & 7.50 & 0.01 \\
80 & 24.63 & 23.45 & \(-\)1.18 & 24.63 & 23.22 & \(-\)1.41 & 24.63 & 24.62 & 0.01 \\
100 & 18.42 & 15.35 & \(-\)3.07 & 21.95 & 22.62 & 0.67 & 25.57 & 25.53 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different predictive neural networks.  
Figure 13: CPSO-RBF-BP neural network fitted curve.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Neural Network** & **RMSE** & **R2** & **MAE** \\ \hline BP & 2.391 & 0.68779 & 0.628 \\ RBF-BP & 0.507 & 0.90245 & 0.433 \\ CPSO-RBF-BP & 0.263 & 0.99791 & 0.114 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of four neural network evaluation metrics.  
used in this article outperforms the BP and RBF-BP neural network models in terms of control performance. The RBF-BP neural network model used in this paper has good nonlinear mapping ability, which accounts for better control performance. The optimal and suitable control variables can be found thanks to the chaotic particle swarm optimization CPSO algorithm's good convergence and optimization ability. The optimum control variable for the reactor temperature can be used. The simulation results demonstrate the effectiveness of the proposed predictive control technique.
3. At the moment, only a subset of the data described in this article has been collected, which is all data from normal operation, and data from various factors affecting the temperature of the reactor is missing. Later, the operation data will be supplemented, and the prediction model for various temperature factors will be constructed to make the model universal, and the model will be applied to real production at the same time.  
Conceptualization, X.T. and Z.X.; methodology, X.T.; software, X.T.; validation, X.T. and B.X.; formal analysis, X.T. and Z.X.; investigation, X.T.; resources, X.T.; data curation, X.T.; writing--original draft preparation, X.T.; writing--review and editing, Z.X. and B.X.; visualization, X.T.; supervision, B.X.; project administration, B.X.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.