Due to the advantages of high prediction accuracy, least squares support vector machine
(LSSVM) has been widely utilized for soft sensor developments in industrial processes. The …

On complex batch industrial processes, soft sensor modeling plays a key role in process
control and monitoring. Considering the nonlinearity, time-varying, and repetitive nature of …

Soft sensing of quality/key variables is critical to the control and optimization of industrial
processes. One of the main drawbacks of data-driven soft sensors is to deal with the …

In industrial production, soft sensors play very important roles in ensuring product quality
and production safety. Traditionally, global modeling methods, which use historical data to …

In the paper, a multi-layer perceptron (MLP) based soft sensor for SO 2 emission in
desulfurization process of thermal power plants is proposed. Firstly, the production process …

Soft sensors have gained wide popularity in the industrial processes for online quality
prediction in the recent years. In the case of online deployment, it is important to incorporate …

Soft sensor technique has become a promising solution to enable real-time estimations of
difficult-to-measure quality variables in industrial processes. However, traditional soft sensor …

Traditional single model based soft sensors may have poor performance on quality
prediction for batch processes because of the strong nonlinearity, multiple-phase, and time …

In nonlinear industrial processes, some product qualities or key variables are usually difficult
to measure online automatically due to the lack of sensors. In this work, a novel data-driven …

Soft sensors based on deep learning approaches are growing in popularity due to their
ability to extract high-level features from training, improving soft sensors' performance. In the …