Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry
By Manabu Kano, Yoshiaki Nakagawa. Computers and Chemical Engineering, 2008
511 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Mechanical Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/33ad93bd96c5865f92932db3390be0d8b4515c5d
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In Control and Systems Engineering
In Mechanical Engineering
In Statistics and Probability
Citations in the corpus
- Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE (2018, background): “Hence, soft sensors have been developed to estimate the difficult-to-measure quality variables through those easy-to-measure ones by some predictive mathematical models, which can provide important real-time information that is necessary for process monitoring, optimization, and control [1]–[3].”
- Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network (2020, background): “THE difficulty of timely measurement of many key quality variables has been a major problem for process monitoring, control, and optimization in modern industry [1]–[4].”
- Canonical correlation analysis-based fault detection methods with application to alumina evaporation process (2016, unknown): “The reader is referred to (Russell, Chiang, & Braatz, 2000; Venkatasubramanian, Rengaswamy, Kavuri, & Yin, 2003; Kano & Nakagawa, 2008; Qin, 2012; Yin, Ding, Haghani, Hao, & Zhang, 2012; Ding, 2014) for a comprehensive literature study.”
- Data-Based Techniques Focused on Modern Industry: An Overview (2015, background): “deal with high dimension correlated data sets [2], [3].”