Bioprocess systems analysis, modeling, estimation, and control
The production of monoclonal antibody (mAb) therapeutics, a rapidly growing multi-billion-
dollar enterprise in the biopharmaceutical industry, faces major challenges in achieving …
dollar enterprise in the biopharmaceutical industry, faces major challenges in achieving …
[KÖNYV][B] Soft sensors for monitoring and control of industrial processes
Soft Sensors for Monitoring and Control of Industrial Processes | SpringerLink Skip to main
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RNN-and LSTM-based soft sensors transferability for an industrial process
The design and application of Soft Sensors (SSs) in the process industry is a growing
research field, which needs to mediate problems of model accuracy with data availability …
research field, which needs to mediate problems of model accuracy with data availability …
Stacked enhanced auto-encoder for data-driven soft sensing of quality variable
X Yuan, S Qi, Y Wang - IEEE Transactions on Instrumentation …, 2020 - ieeexplore.ieee.org
Data-driven soft sensors have been widely used in industrial processes. Traditional soft
sensors are mostly shallow networks, which cannot easily describe the complicated process …
sensors are mostly shallow networks, which cannot easily describe the complicated process …
Soft sensors based on deep neural networks for applications in security and safety
Here, this article reports about the design of a soft sensor (SS) able to monitor the hazardous
gases in industrial plants. The SS is designed to estimate the gas concentrations by means …
gases in industrial plants. The SS is designed to estimate the gas concentrations by means …
Obey validity limits of data-driven models through topological data analysis and one-class classification
Data-driven models are becoming increasingly popular in engineering, on their own or in
combination with mechanistic models. Commonly, the trained models are subsequently …
combination with mechanistic models. Commonly, the trained models are subsequently …
Stacked isomorphic autoencoder based soft analyzer and its application to sulfur recovery unit
Deep learning is an important and effective tool for process soft sensor modeling in
industrial artificial intelligence. Traditional deep learning methods like stacked autoencoder …
industrial artificial intelligence. Traditional deep learning methods like stacked autoencoder …
Data-driven soft sensor approach for online quality prediction using state dependent parameter models
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 …
uses state dependent parameter (SDP) models to improve product quality monitoring. The …
Gated broad learning system based on deep cascaded for soft sensor modeling of industrial process
M Mou, X Zhao - IEEE Transactions on Instrumentation and …, 2022 - ieeexplore.ieee.org
With the advancement of computer and sensor technology, soft sensors have been more
and more extensively used in industrial processes. Soft sensors based on deep learning …
and more extensively used in industrial processes. Soft sensors based on deep learning …
Development of a new soft sensor method using independent component analysis and partial least squares
H Kaneko, M Arakawa, K Funatsu - AIChE Journal, 2009 - Wiley Online Library
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 …
online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to …