Design and Applications of Soft Sensors in Polymer Processing: A Review

Abstract

In manufacturing industry, process monitoring is a key to observe the product quality, operational health, safety and also for achieving good/satisfactory process control performance. In polymer processing, the level of control of the process operational quality is vastly dependent upon the performance of the process monitoring techniques as it is quite difficult to observe the inside of an extruder barrel (or processing chamber) during the process operation. Currently, a number of physical sensing devices/techniques are widely available for industrial applications for monitoring of parameters such as melt temperature, melt pressure, screw/processing speed, product dimensions, etc in real-time. However, there may be some limitations in using physical sensors in piratical process measurements (such as melt viscosity and melt temperature profile) due to several constraints such as their access requirements, disruptive effects to the melt flow, fragility, complexity, etc. Therefore, soft sensing techniques should be highly useful for improved process monitoring and modelling, and hence for advanced process control. In this work, a general description is presented on the state-of-the-art of soft sensor design. Then, a comprehensive review is made on the use of soft sensing techniques in polymer processing applications while identifying their capabilities and limitations. Eventually, the importance of developing of such soft sensing techniques (together with some sort of built-on intelligence) for the advancement of process monitoring is discussed while indicating some of the possible directions for future industry.

Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions

“…Although PCA and PLS have shown promising results in various applications, these methods have some limitations. PCA and PLS may not perform well with non-linear data [ 110 ]. Also, the use of PLS and PCA may, in some cases, reduce the access or the interpretability of the data, as information on which regions of the spectrum are responsible for the majority of the variation is obscured.…”

Industrial data science – a review of machine learning applications for chemical and process industries

“…For example, in ref. 76, the authors discuss the merits of monitoring melt viscosity, temperature profile, and flow index as indicators of product quality in the context of polymer processing. As a result, soft sensors may be constructed to infer these qualities from other available process measurements (such as screw speed, die melt temperature, feed rates, and pressures) either via first principles, data-driven or hybrid modeling approaches.…”

Built-in Sensor System for Monitoring Internal Shear Strain and Stress Distribution in Soft Materials

“…The majority of the proposed soft sensing techniques have been tested only with simulations and are not yet ready for practical use. The improvement of soft sensors from the simulation stage to practical use is a significant challenge because the actual process dynamics will be more complicated than the conditions considered in simulation experiments [20]. Therefore, to provide more precise explanation of the proposed sensor system, the sensing model should be fully considered and validated.…”