Soft sensors have been widely used in industrial process control to improve the quality of
product and assure safety in production. The core of a soft sensor is to construct a soft …

The goal of this paper is to design and implementation of a new data-driven soft sensor that
uses state dependent parameter (SDP) models to improve product quality monitoring. The …

Most traditional soft sensors are built upon the labeled dataset that contains equal numbers
of input and output data samples. However, the output variables that correspond to quality …

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 …

Traditionally, data‐based soft sensors are constructed upon the labeled historical dataset
which contains equal numbers of input and output data samples. While it is easy to obtain …

Support Vector Machine (SVM) is a novel machine-learning method of soft sensor modeling
in fermentation process, which has the ability to approximate nonlinear process with …

A soft sensor predicts the values of some process variable y that is difficult to measure. To
maintain the predictive ability of a soft sensor model, adaptation mechanisms are applied to …

The temperature and silicon content of hot metal are essential parameters for the thermal
control of a blast furnace. However, the physical structure of the blast furnace prevents direct …

Soft sensors are used widely to estimate a process variable which is difficult to measure
online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to …

This paper proposes a semisupervised deep neural regression network with embedding
manifold (SSE-DNN) for soft sensor modeling that integrates manifold embedding into deep …