## 3 Optimization Models
### Crested Porcupine Optimizer (CPO) Optimization
Table 6 lists the optimal parameter penalty coefficients C and the kernel function coefficients gamma. Figures 2 and 3 show the test results with NOx conversion efficiency and NH\({}_{3}\) slip as SVM model outputs, respectively. For model accuracy, the coefficient of determination R\({}^{2}\) and RMSE were used to assess the model accuracy, as shown in Tables 7 and 8. For the prediction of NOx conversion efficiency, it can be seen that the CPO optimization algorithm as well as the PSO optimization algorithm are able to make accurate predictions of NOx conversion efficiency under the three operating conditions. The coefficient of determination R\({}^{2}\) of the SVM model optimized by the CPO optimization algorithm is above 0.98 and RMSE for the three conditions is 0.76, while the coefficient of determination R\({}^{2}\) of the SVM model optimized by the PSO optimization algorithm is about 0.96 and the RMSE for the three conditions is 2.05. It can be seen that the SVM model optimized by the CPO optimization algorithm can predict the NOx conversion efficiency accurately as compared with that of the PSO optimization algorithm; the SVM model has higher prediction accuracy for NOx conversion efficiency. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **1200 rpm-75 N\(\cdot\)m** & **1800 rpm-300 N\(\cdot\)m** & **2400 rpm-450 N\(\cdot\)m** \\ \cline{2-6}  & **CPO** & **PSO** & **CPO** & **PSO** & **CPO** & **PSO** \\ \hline C & 486.24 & 106.9584 & 27.8258 & 68.046 & 780.19 & 971.1188 \\ gamma & 5.9741 & 0.6098 & 3.3745 & 0.2793 & 3.4645 & 0.7397 \\ \hline \end{tabular}
\end{table} 
Table 6: Optimal parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline
**SCR Inlet** & **Exhaust Flow** & **Inlet NOx (ppm)** & **Urea Injection** & **NOx Conversion** & **NH\({}_{3}\) Slip (ppm)** \\
**Temperature** (°C) & **(kg/h)** & **Inlet NOx (ppm)** & **(mg/s)** & **Efficiency (\%)** & **NH\({}_{3}\) Slip (ppm)** \\ \hline
315 & 344 & 1266 & 0 & 0.00 & 0.00 \\
315 & 344 & 1266 & 25 & 6.48 & 0.12 \\
315 & 344 & 1266 & 50 & 12.95 & 0.24 \\
315 & 344 & 1266 & 75 & 19.35 & 1  
Figure 3: NH\({}_{3}\) slip prediction results. Figure 2: NOx conversion efficiency prediction results. For the prediction of NH\({}_{3}\) slip, the CPO optimization algorithm is still able to accurately predict the NH\({}_{3}\) slip under the three operating conditions. On the other hand, the PSO optimization algorithm was able to predict the NH\({}_{3}\) slip more accurately only at 2400 rpm-450 N\(\cdot\)m, and the prediction of NH\({}_{3}\) slip for the other two conditions, especially at 1200 rpm-75 N\(\cdot\)m, had a large deviation. This phenomenon may be due to the fact that the established SVM model has two output parameters, and the PSO optimization algorithm is unable to find the optimal parameters that can take into account the two output parameters, so the prediction accuracy of the NOx conversion efficiency is first considered when optimizing the model using the PSO algorithm. Compared with the PSO optimization algorithm, the CPO optimization algorithm has the ability to extensively explore the search space (which enables it to avoid falling into local minima) and to protect population diversity based on the novel cyclic population reduction mechanism, which enables it to find the optimal parameter that can take into account the two output parameters within the specified parameter range, allowing it to accurately predict both output parameters. Therefore, the SVM model optimized by the CPO optimization algorithm can be used for the prediction of NOx conversion efficiency and NH\({}_{3}\) slip.