{"title":"Melt viscosity control in polymer extrusion using nonlinear model predictive control with neural state space modelling and soft sensor feedback","authors":"Yasith S. Perera , Jie Li , Chamil Abeykoon","doi":"10.1016/j.jprocont.2025.103556","DOIUrl":null,"url":null,"abstract":"<div><div>Melt viscosity is a key quality indicator in polymer extrusion processes, as it directly influences the mechanical properties, dimensional stability, and surface finish of the final product. However, real-time melt viscosity monitoring and control remain a major challenge in industrial polymer extrusion, due to the limitations of physical viscosity monitoring techniques, such as disturbances to the melt flow, reduced throughputs, and measurement delays. To address this issue, this study proposes a nonlinear model predictive control framework that enables direct, real-time control of melt viscosity using non-invasive feedback from a deep neural network-based soft sensor. A neural state-space model is trained on real experimental data to learn the underlying process dynamics and serves as the internal model of the controller. The soft sensor provides melt viscosity estimates based on readily available process variables (i.e., screw speed and barrel temperatures). These estimates are used by an extended Kalman filter with state augmentation to correct the internal state predictions. The proposed control system was rigorously evaluated via simulation across a variety of setpoint changes and disturbance scenarios. Results show that the controller maintains the melt viscosity within <span><math><mo>±</mo></math></span>2 % of the setpoint, irrespective of the initial conditions used, with settling times below 20 s. Under step and ramp disturbances applied to the output variable, screw speed, and barrel temperatures, the controller exhibited strong disturbance rejection capabilities. Notably, under step disturbances of <span><math><mo>±</mo></math></span> 100 Pa⋅s acting on the melt viscosity output, the controller quickly restored the viscosity to the setpoint with settling times under 18 s. The real-time closed-loop melt viscosity control framework proposed in this study should be invaluable for advancing process monitoring and control of polymer extrusion processes.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103556"},"PeriodicalIF":3.9000,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Process Control","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959152425001842","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Melt viscosity is a key quality indicator in polymer extrusion processes, as it directly influences the mechanical properties, dimensional stability, and surface finish of the final product. However, real-time melt viscosity monitoring and control remain a major challenge in industrial polymer extrusion, due to the limitations of physical viscosity monitoring techniques, such as disturbances to the melt flow, reduced throughputs, and measurement delays. To address this issue, this study proposes a nonlinear model predictive control framework that enables direct, real-time control of melt viscosity using non-invasive feedback from a deep neural network-based soft sensor. A neural state-space model is trained on real experimental data to learn the underlying process dynamics and serves as the internal model of the controller. The soft sensor provides melt viscosity estimates based on readily available process variables (i.e., screw speed and barrel temperatures). These estimates are used by an extended Kalman filter with state augmentation to correct the internal state predictions. The proposed control system was rigorously evaluated via simulation across a variety of setpoint changes and disturbance scenarios. Results show that the controller maintains the melt viscosity within 2 % of the setpoint, irrespective of the initial conditions used, with settling times below 20 s. Under step and ramp disturbances applied to the output variable, screw speed, and barrel temperatures, the controller exhibited strong disturbance rejection capabilities. Notably, under step disturbances of 100 Pa⋅s acting on the melt viscosity output, the controller quickly restored the viscosity to the setpoint with settling times under 18 s. The real-time closed-loop melt viscosity control framework proposed in this study should be invaluable for advancing process monitoring and control of polymer extrusion processes.
期刊介绍:
This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others.
Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques.
Topics covered include:
• Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods
Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.