Abstract
Instantaneous measurements of process variables are usually not representative of the process effects as a whole when defining the condition of an output sample mainly in case of laboratory analysis. Moreover, process data have considerable dispersion. This leads to uncertainty in input–output time alignment and in variable relationship. This work employs a trend data-based approach to overcome the negative effects of these uncertainties in both tasks variable selection commonly supported by correlation analysis and model identification. Two real case studies using a clinker rotary kiln from a cement plant and a chemical recovery boiler from a pulp mill were used for illustration purposes. More reliable data-driven system representation enhances the comprehension of the underlying system phenomena supporting a more rational basis for decision making.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Engineering Applications of Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
