Over the past twenty years, numerous research outcomes have been published, related to
the design and implementation of soft sensors. In modern industrial processes, various types …

Pressure-swing distillation (PSD) is widely used as an efficient method for separating
pressure-sensitive azeotropic mixtures in industrial processes. Remarkably, PSD can …

Soft sensors are widely constructed in process industry to realize process monitoring, quality
prediction, and many other important applications. With the development of hardware and …

Data-driven soft sensors have been widely utilized in industrial processes to estimate the
critical quality variables which are intractable to directly measure online through physical …

In industrial processes, inferential sensors have been extensively applied for prediction of
quality variables that are difficult to measure online directly by hard sensors. Deep learning …

Just-in-time learning (JITL) is a commonly used technique for industrial soft sensing of
nonlinear processes. However, traditional JITL approaches mainly focus on equal sample …

Probabilistic latent variable regression models have recently caught much attention in the
process industry, particularly for soft sensor applications. One of the main challenges for …

The principal component regression (PCR) based soft sensor modeling technique has been
widely used for process quality prediction in the last decades. While most industrial …

Wastewater treatment plants (WWTPs) form an industry whose main goal is to reduce
water's pollutant products, which are harmful to the environment at high concentrations. In …

In this paper, a novel variable selection method for neural network that can be applied to
describe nonlinear industrial processes is developed. The proposed method is an iterative …