Soft Sensors (SSs) are inferential dynamical models employed in industries to perform
prediction of process hard-to-measure variables based on their relation with easily …

For deep learning-based soft sensors, the lack of interpretability and the consequent
unreliability has become one of the most important problems. In this article, a neural network …

As an effective means of sensing difficult-to-measure process variables in real time, soft
sensors are widely used but have a few significant limitations. Modeling errors between the …

When addressing product quality standards in manufacturing lines, a critical issue is the
identification of the parameters that define the quality of the final product and their tracking …

Without sufficient labeled data, the construction of accurate soft-sensor models for
multigrade chemical processes is challenging. To alleviate the dilemma, a transductive …

Developing a reliable quality inference model in batch processes remains a challenge. The
performance of the model tends to decline due to batch-to-batch variations. Additionally, the …

This paper introduces a novel soft sensor modeling method based on BDA-IPSO-LSSVM
designed to address the issue of model failure caused by varying fermentation data …

Modeling of improved amine scrubbers using artificial neural networks (ANNs) were carried
out in this study. Instead of training models from scratch with the case-by-case method, the …

This investigation utilized binary-coded, parallel-connected on-off valves that can achieve
high flow rates with fewer valves while addressing flow peak challenges. By considering …

Batch processing is a widely utilized technique in the manufacturing of high-value products.
Traditional methods for quality assessment in batch processes often lead to productivity and …