Data-Driven Soft Sensor Model Based on Deep Learning for Quality Prediction of Industrial Processes

Fermentation process is a time-varying, nonlinear and multivariable dynamic coupling system. Therefore, it is difficult to directly measure the key biological variables using traditional physical sensors during the process of fermentation, which makes the monitoring and real-time control impossible. To resolve this problem, a data-driven soft sensor modeling method based on deep neural network (DNN) is proposed in this paper. This method is suitable for large amount of data and it enjoys high efficiency and robustness. At the same time, an adaptive moment estimation (Adam) algorithm is used to optimize the hyper-parameters of the DNN model, which is a technique for efficient stochastic optimization that only requires first-order gradients with little memory requirement. The consistent correlation method is used to determine the auxiliary variables of the soft sensor model. The penicillin and l-lysine fermentation processes are taken as the research object, substrate concentration, cell concentration, and product concentration are selected as a target variable. The performance of established soft sensor model is evaluated through the indexes of mean square error (MSE), root-mean-square error (RMSE), and mean absolute error (MAE). The simulation results show that the prediction performance of the soft sensor model based on DNN-Adam is good and compared with model based on stochastic gradient descent (SGD) with momentum optimization algorithm. It is verified that the proposed method can make a more accurate real-time prediction of quality variables in the fermentation process, and it has higher prediction accuracy than DNN-SGD method. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.