## 5 Results and Discussion  
### Analysis of the Forecasting Results of the Oxygen Demand  
The collected field data were divided into 12 groups for forecasting and verification. Each group of data contained 165 data points, of which the first 150 data points were used as input data for forecasting, and the last 15 data points as verification data for the forecast results. This is equivalent to forecasting the oxygen demand for the next half an hour based on 5 h of historical data. In addition, some of the field data are provided in Table 3 for a better understanding of the input and output of the models. First, the ARIMA model forecast was performed on the data, and ARIMA (2,1,5) was obtained as the optimal model for time series prediction. Second, the GABP model forecast was performed on the data, in which the GA optimization algorithm was set as follows: The genetic algebra was 100, the population size was 50, the crossover probability was 0.4, the mutation probability was 0.3, the number of nodes in the hidden layer of the BP neural network was 18, the activation function of the hidden layer was a tansig function, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & \begin{tabular}{c} **Demand** \\ **(m\({}^{3}\cdot\)h\({}^{-1}\))** \\ \end{tabular} & **No.** & \begin{tabular}{c} **Demand** \\ **(m\({}^{3}\cdot\)h\({}^{-1}\))** \\ \end{tabular} & **No.** & \
\begin{tabular}{c} **Demand** \\ **(m\({}^{3}\cdot\)h\({}^{-1}\))** \\ \end{tabular} & **No.** &
\begin{tabular}{c} **Demand** \\ **(m\({}^{3}\cdot\)h\({}^{-1}\))** \\ \end{tabular} \\ \hline
1 & 112,215 & 11 & 141,537 & 21 & 101,184 & 31 & 151,993 \\
2 & 132,765 & 12 & 145,090 & 22 & 100,658 & 32 & 192,764 \\
3 & 138,769 & 13 & 140,438 & 23 & 78,983 & 33 & 165,735 \\
4 & 139,979 & 14 & 142,175 & 24 & 79,322 & 34 & 174,441 \\
5 & 146,382 & 15 & 141,378 & 25 & 86,324 & 35 & 199,188 \\
6 & 183,921 & 16 & 131,942 & 26 & 84,529 & 36 & 198,568 \\
7 & 203,641 & 17 & 122,900 & 27 & 120,073 & 37 & 176,028 \\
8 & 172,899 & 18 & 82,587 & 28 & 158,378 & 38 & 147,128 \\
9 & 158,281 & 19 & 115,577 & 29 & 160,183 & 39 & 144,668 \\
10 & 155,339 & 20 & 99,487 & 30 & 148,698 & 40 & 149,441 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Some of the field data of the oxygen demand. the excitation function of the output layer was a purelin function, the learning rate was 0.05, the target error precision was 0.01, and the number of network iteration steps was 5000. The forecasting was performed using MATLAB 9.6 (R2019a) software, and the results of the ARIMA and GABP models are shown in Figures 3 and 4, respectively. When comparing Figures 3 and 4, it can be seen that both the ARIMA and GABP models can forecast the changing trend of the oxygen demand, but the GABP model performed better than the ARIMA model in terms of coincidence with the field data. To quantificationally compare the forecast accuracy of the two models, the mean absolute percentage error (MAPE) of each of the 12 sets of data was calculated and is shown in Figure 5. The calculation results show that the average MAPE values of the 12 sets of data of the ARIMA and GABP models were 23.8% and 20.2%, respectively. Therefore, the prediction accuracy of the GABP model is better than that of the ARIMA model. Figure 4: Oxygen demand: GABP model forecasting vs. field data. Figure 5: Forecasting error: ARIMA model vs. GABP model. Figure 3: Oxygen demand: ARIMA model forecasting vs. field data. As shown in Figure 6, the scheduling results of the oxygen output of the ASUs in each scheduling period declined, and the slope of the decline was the load change rate of the ASUs, which shows that under the current case conditions, oxygen was in a state of oversupply. The scheduling results of the oxygen output in Figure 6 are mostly lower than those of the field data. If on-site workers can perform load adjustments of the ASUs according to the scheduling results, the oxygen output can be reduced.