Virtual Sensor for Sustainable Large-Scale Process Monitoring
Mohammad Reza Boskabadi, Mahesh Murugaiah, Torben Rank Nielsen, Abhishek Sivaram, Gürkan Sin, Seyed Soheil Mansouri
Abstract
Feed quality diversity, high production capacity, and often harsh process conditions lead to challenges in process monitoring and control. This study proposes a framework that advances soft (virtual) sensor applications in complex industrial settings, highlighting the potential of machine learning and process knowledge for process monitoring and digitalization by utilizing a plantwide process control system. The application is demonstrated through a soft sensor to predict Brix values as a quality indicator variable (QIV) in industrial batch crystallization processes. The effectiveness of the soft sensor is shown through a rigorous evaluation using statistical metrics. Additionally, the study explores temporal dependencies through autocorrelation function (ACF) analysis and long short-term memory (LSTM) modeling. The developed soft sensor is applied to a sugar manufacturing plant. It has been shown that it will decrease the mean residence time of sugar crystallization by 2.12%, which consequently leads to a reduction in energy consumption and CO 2 footprint, increasing production capacity, and finally, sustainable process development.