## 3 Results
### Model Comparison  
In order to prove the superiority of the ga-RF model, the predicted oxygen concentration data of the return airway corner were compared with that of the traditional RF and BPNN as well as that of the optimized BPNN model based on GA.  
First, the RF model with the default tree size of 500 and the number of leaves [1; 5] was used. The RF process is shown in Figure 13. In the training phase, MSE(OOB) value of leaves = [1; 5] is used as an unbiased estimation of the error of the test data set to test the generalization ability of the model. As shown in Figure 13, MSE(OOB) tends to be stable when the number of trees exceeds 100. MSE(OOB) = 0.091 is the minimum when leaf = 1 and trees = 417, which is basically consistent with RF parameters leaf = 1 and trees = 398 after GA optimization. This further verified the rationality of RF model parameters optimized by GA.  
Figure 12: Prediction data of oxygen concentration in GA-RF return-air corner. Figure 13: MSE(OOB) data under different numbers of leaves. In GA-BPNN, the initial population of GA was set randomly, assuming that the population size was 50 and the maximum iteration was 100. GA parameters were set as crossover probability 0.8 and variation probability 0.1. The GA convergence curve for optimizing the number of hidden neurons is shown in Figure 14. After about 40 iterations, the optimal solution is obtained. Based on the optimal solution of the objective function, the optimal number of hidden neurons corresponding to the optimal individual is nine. The topological structure of the return air corner oxygen prediction model is 6-9-1. That is, the network is a three-layer structure, in which the hidden layer neurons are nine, and the input and output neurons are six and one, respectively. The number of network weights and thresholds is determined according to the network structure, and the initial weights and thresholds of BPNN are optimized by GA. Each individual in GA population contains the initial weight and threshold value of a network. The objective function is used to calculate the individual fitness value. GA finds the initial weight and threshold value corresponding to the optimal fitness value through selection, crossover and mutation operation. The objective function is to minimize the training data RMSE. The initial population of GA was set randomly, assuming that the population size was 50 and the maximum iteration was 100. GA parameters were set as crossover probability 0.8 and variation probability 0.1. The GA convergence curve of the optimized initial thresholds and weights is shown in Figure 15. After about 40 iterations, the optimal solution is obtained. Based on the optimal solution of the objective function, the optimal initial weight and threshold corresponding to the optimal individual are obtained, and the optimal initial value is assigned to BPNN for training model. Figure 14: The minimum RMSE fitness fusion characteristic curve, based on GA optimized BPNN number of hidden neurons. Figure 15: Minimum RMSE fitness fusion characteristic curve based on GA optimized BPNN initial weights and thresholds. According to the above analysis, the internal parameters of GA-RF, RF, GA-BPNN and BPNN models are shown in Table 3.  
Based on the collected data samples, the training set and test set with the same proportion were divided. Ga-rf, RF, GA-BPNN and BPNN were used to predict the oxygen concentration in the corner of the return air. The predicted data were shown in Figure 16.  
When ntree = 500 and leaf = 1, the MSE(OOB) value of the RF model is the smallest. Therefore, the RF model with ntree = 500 and leaf = 1 is selected to compare the absolute percentage error with other models. When RF and BPNN optimized by GA are compared with RF and BPNN, the MAPE is significantly reduced. This indicates that GA has a certain optimization effect on the prediction accuracy of the model. The absolute error percentage of RF is slightly less than that of GA-BPNN.