Deep Learning based Soft Sensor for Bioprocess Application

Abstract

Soft Senors provides an alternative way for measuring the process variables which cannot be measured online. Deep learning training techniques has become popular for designing Soft sensors for complex nonlinear systems because of accuracy and robustness. The work presented in this paper was to design a Soft Sensor to estimate bioprocess variables like lactose and ethanol concentrations in the bioreactor using deep neural networks(DNN).Here an unsupervised learning has been used to pre-train the network and supervised learning was used for training the network. self-organizing Maps(SOM) was used for pre-training the network and error back propagation algorithm was used for training the network. The Design of soft sensor using such Combination of algorithms results in better performance in terms of estimated values and measurement error when compared with other methods estimation. Furthermore, the performance of the soft sensor has been observed with different hidden layers and it was concluded that with three hidden layers the soft sensor gives accurate results when compared to that of the single layer.