Abstract
The online measurement of the aluminum−silicon ratio of red mud in the dissolution stage of the Bayer alumina production process is difficult to achieve. The offline assay method has a high cost and strong time delay. Soft sensors are an effective and economical method to solve such problems. In this paper, a hybrid model (TPRF model) based on a tree-structured Parzen estimator (TPE) optimized random forest (RF) algorithm is proposed to measure the Al−Si ratio of red mud. The probability distribution of the hyperparameters of the random forest model is estimated by combining the TPE optimization algorithm with the random forest algorithm. According to this probability distribution, the hyperparameters of the random forest algorithm are adjusted in the parameter search space to obtain the best combination of hyperparameters. We established a TPRF soft sensing model based on the optimal combination of hyperparameters. The results show that the best performance of the TPRF model is a mean absolute percentage error (MAPE) of 0.0015, a root-mean-square error (RMSE) of 0.00378, a mean absolute error (MAE) of 0.00162, and a goodness of fit (R2) of 0.9893. The goodness of fit improved by 93.2% compared to the linear model, 39.1% compared to the SVR model, about 21.2% compared to the GRU model, and 5.5% compared to the RF model. This level of performance is demonstrated to be better than traditional soft sensors.