Dynamical Soft Sensors from Scarce and Irregularly Sampled Outputs Using Sparse Optimization Techniques

Abstract

In process industries, quality variables such as concentrations and viscosity usually require offline laboratory analysis due to difficulties associated with online sensing and are often sampled slowly or irregularly compared to other variables such as temperatures and flow rates. Dynamical soft sensors, which relate the uniformly fast sampled variables to irregularly sampled quality variables, are crucial in control and process monitoring applications. Most identification approaches for soft sensing assume that all the variables are sampled regularly and uniformly. The existing auxiliary model (AM)-based approaches for dealing with irregular sampling suffer from drawbacks such as nonconvex optimization, lack of model parsimony and require prior information about the system dynamics. This work addresses a few of these issues by developing a flexible AM that can accommodate complex linear process dynamics without assuming any prior knowledge and compromising on model parsimony by using redundant Laguerre filters and casting the model learning in the sparse optimization framework. The developed AM is utilized to efficiently reconstruct the measurements at the base sampling interval, which further serves as a foreground for the traditional parametric model identification techniques or the expectation maximization algorithm to obtain optimal parameter estimates. The efficacy of the proposed AM-based soft sensor algorithm is demonstrated through synthetic as well as industrial simulation case studies. Finally, a few guidelines on effective sampling of quality variables to generate informative experiments for soft sensor development are also presented.

RegGAN: A Virtual Sample Generative Network for Developing Soft Sensors with Small Data

“…In modern complex chemical production processes, real-time monitoring has proven to be an exceedingly effective method for ensuring product safety and enhancing economic benefits. − Quality variables serve as essential indicators of the performance of the chemical production process and are crucial in maintaining process stability . However, due to the scarcity of economical and reliable online monitoring equipment, many quality variables in practical chemical production are acquired through offline laboratory analysis .…”

Editorial for the Festschrift Honoring the Legacy of Babatunde A. Ogunnaike (1956–2022)

“…6 Correlations between interfacial area and volumetric mass transfer in bubble columns are presented by Hazare et al 7 Special Issue: In Honor of Babatunde A. Ogunnaike Process control and operations are represented by the works of Santander et al, who discuss an integrated stochastic framework for simultaneous short-term scheduling and control for batch processes, 8 Lovelett et al, who review the control of processes with input multiplicity, 9 and Pinnamaraju et al, who employ sparse optimization to construct soft sensors from irregularly and infrequently sampled data. 10 Finally, randomness and the use of feedback control to address it are covered in the works of Pearson 11 and McAllister et al, 12 respectively.…”