Design of inferential sensors in the process industry: A review of Bayesian methods
In many industrial plants, development and implementation of advanced monitoring and
control techniques require real-time measurement of process quality variables. However, on …
control techniques require real-time measurement of process quality variables. However, on …
Big data analytics in chemical engineering
Big data analytics is the journey to turn data into insights for more informed business and
operational decisions. As the chemical engineering community is collecting more data …
operational decisions. As the chemical engineering community is collecting more data …
A survey on deep learning for data-driven soft sensors
Q Sun, Z Ge - IEEE Transactions on Industrial Informatics, 2021 - ieeexplore.ieee.org
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 …
prediction, and many other important applications. With the development of hardware and …
Artificial intelligence technologies in bioprocess: opportunities and challenges
Y Cheng, X Bi, Y Xu, Y Liu, J Li, G Du, X Lv, L Liu - Bioresource Technology, 2023 - Elsevier
Bioprocess control and optimization are crucial for tap** the metabolic potential of
microorganisms, and which have made great progress in the past decades. Combination of …
microorganisms, and which have made great progress in the past decades. Combination of …
Challenges in the development of soft sensors for bioprocesses: A critical review
Among the greatest challenges in soft sensor development for bioprocesses are variable
process lengths, multiple process phases, and erroneous model inputs due to sensor faults …
process lengths, multiple process phases, and erroneous model inputs due to sensor faults …
Just-in-time based soft sensors for process industries: A status report and recommendations
Soft sensors are mathematical models employed to estimate hard-to-measure variables from
available easy-to-measure variables. These sensors are typically developed using either …
available easy-to-measure variables. These sensors are typically developed using either …
Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models
W Shao, X Tian - Chemical Engineering Research and Design, 2015 - Elsevier
This paper proposes an adaptive soft sensing method based on selective ensemble of local
partial least squares models, referring to as the SELPLS, for quality prediction of nonlinear …
partial least squares models, referring to as the SELPLS, for quality prediction of nonlinear …
Electrospun nanofiber membrane diameter prediction using a combined response surface methodology and machine learning approach
MN Pervez, WS Yeo, MMR Mishu, ME Talukder… - Scientific Reports, 2023 - nature.com
Despite the widespread interest in electrospinning technology, very few simulation studies
have been conducted. Thus, the current research produced a system for providing a …
have been conducted. Thus, the current research produced a system for providing a …
The role of big data in industrial (bio) chemical process operations
With the emergence of Industry 4.0 and Big Data initiatives, there is a renewed interest in
leveraging the vast amounts of data collected in (bio) chemical processes to improve their …
leveraging the vast amounts of data collected in (bio) chemical processes to improve their …
A comparative study of deep and shallow predictive techniques for hot metal temperature prediction in blast furnace ironmaking
To realize stable operation of the ironmaking process, it is important to predict hot metal
temperature (HMT) in a blast furnace. Recently, deep learning is emerging as a highly active …
temperature (HMT) in a blast furnace. Recently, deep learning is emerging as a highly active …