## 3 Results and Discussion
### Weight Analysis of Influencing Factors  
From the above single-factor test, it can be seen that various factors have little impact on the desulfurization efficiency of the DBD combined wet scrubbing system, while high desulfurization efficiency is easy to achieve. The denitration efficiency is the key problem in this system. In order to quantitatively analyze the influence of various factors on the denitration efficiency and to optimize the performance of this system, orthogonal tests were designed to analyze the weights of various factors. Each factor and its level division are shown in Table 4.  
An orthogonal table (\(L_{18}(3^{7})\)) was selected to arrange the experiment. The six factors considered in this paper are placed in the first six columns of the table (column 7 is not required). Table 5 shows the specific arrangement and results of the orthogonal test. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**LevelFactor** & **A** & **B** & **C** & **D** & **E** & **F** & **Results** \\  & **(-)** & **(-)** & **(m\({}^{3}\)/(m\({}^{2}\).h))** & **(\%)** & **(W)** & **F (L/min)** \\ \hline
1 & D\({}_{\text{S}}\) 10 mm Pall ring & 40 & 1.8 & 0 & 10.9 & 3 \\
2 & D\({}_{\text{S}}\) 6 mm Pall ring & 80 & 5.4 & 0.5 & 19.5 & 5 \\
3 & D\({}_{\text{S}}\) 6 mm Dixon ring & 120 & 10.8 & 1 & 26.6 & 7 \\ \hline \end{tabular}  
* A = packing type; B = packing height; C = spray density; D = mass fraction of NaOH solution; E = discharge power in the DBD reactor; F = simulated flue gas flow rate.  
\end{table} 
Table 4: The factors and levels involved in the denitration efficiency. Figure 11: Effects of simulated flue gas flow rate on desulfurization and denitration efficiency levels (operating conditions: discharge power in DBD reactor = 19.5 W, spray density = 7.6 m\({}^{3}\)/(m\({}^{2}\).h), scrubbing solution = 1% NaOH solution, packing type = D\({}_{\text{S}}\) 6 mm Dixon ring, packing height = 80 cm). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**NO.** & **A** & **B** & **C** & **D** & **E** & **F** & **Results** \\ \hline
1 & 1 & 1 & 1 & 1 & 1 & 1 & 12.17\% \\
2 & 1 & 2 & 2 & 2 & 2 & 2 & 67.62\% \\
3 & 1 & 3 & 3 & 3 & 3 & 3 & 81.65\% \\
4 & 2 & 1 & 1 & 2 & 2 & 3 & 40.68\% \\
5 & 2 & 2 & 2 & 3 & 3 & 1 & 95.62\% \\
6 & 2 & 3 & 3 & 1 & 1 & 2 & 15.07\% \\
7 & 3 & 1 & 2 & 1 & 3 & 2 & 76.02\% \\
8 & 3 & 2 & 3 & 2 & 1 & 3 & 12.63\% \\
9 & 3 & 3 & 1 & 3 & 2 & 1 & 79.88\% \\
10 & 1 & 1 & 3 & 3 & 2 & 2 & 54.85\% \\
11 & 1 & 2 & 1 & 1 & 3 & 3 & 53.41\% \\
12 & 1 & 3 & 2 & 2 & 1 & 1 & 17.06\% \\
13 & 2 & 1 & 2 & 3 & 1 & 3 & 10.94\% \\ \hline \end{tabular}
\end{table} 
Table 5: Arrangement and results of the orthogonal test. The range and variance of each factor in the orthogonal test can reflect its influence on the experimental results. The greater the range and variance, the greater the influence of the factor on the results. Generally, variance is used to evaluate the weights of the factors. The larger the variance of a factor, the greater the weight. The weights of the factors can be calculated using the following formula [36; 37]:  
\[S_{A}=\frac{1}{\alpha}\sum_{i=1}^{m}K_{i}^{2}-\frac{1}{n}\left(\sum_{k=1}^{n}x _{k}\right)^{2}, \tag{19}\]  
\[K_{i}=\sum_{j}^{\alpha}x_{ij}, \tag{20}\]  
\[w_{A}=\frac{\sqrt{S_{A}}}{\sum\sqrt{S_{*}}}, \tag{21}\]  
where \(S_{A}\) is the variance of factor \(A\); \(\alpha\) is the number of tests at each factor level; \(m\) represents the levels of factors; \(n\) is the total number of tests; \(K_{i}\) is the sum of the i-th level results of the factor; \(x_{k}\) is the test result of number k; \(x_{ij}\) is the result of the i-th level and j-th test of the factor; \(w_{A}\) is the influence weight of factor \(A\); \(\sqrt{S_{A}}\) is the standard deviation of factor \(A\); \(\sum\sqrt{S_{*}}\) is the sum of the standard deviations of each factor.