Melt viscosity control in polymer extrusion using nonlinear model predictive control with neural state space modelling and soft sensor feedback

IF 3.9 2区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Yasith S. Perera , Jie Li , Chamil Abeykoon
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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.
基于神经状态空间建模和软传感器反馈的非线性模型预测控制在聚合物挤出过程中的熔体粘度控制
熔体粘度是聚合物挤出过程中一个关键的质量指标,因为它直接影响到最终产品的机械性能、尺寸稳定性和表面光洁度。然而,由于物理粘度监测技术的局限性,如熔体流动的干扰、吞吐量的降低和测量延迟,熔体粘度的实时监测和控制仍然是工业聚合物挤出的主要挑战。为了解决这个问题,本研究提出了一种非线性模型预测控制框架,该框架可以使用基于深度神经网络的软传感器的非侵入性反馈直接实时控制熔体粘度。在实际实验数据上训练神经状态空间模型来学习潜在的过程动力学,并作为控制器的内部模型。软传感器根据现成的工艺变量(即螺杆速度和料筒温度)提供熔体粘度估计。这些估计被一个带状态增强的扩展卡尔曼滤波器用来修正内部状态预测。所提出的控制系统通过各种设定值变化和干扰情景的仿真进行了严格的评估。结果表明,无论使用何种初始条件,该控制器都能将熔体粘度保持在设定值的±2%以内,沉降时间低于20 s。在施加于输出变量、螺杆转速和筒体温度的阶跃和斜坡扰动下,控制器表现出较强的抗干扰能力。值得注意的是,在对熔体粘度输出施加±100 Pa·s阶跃扰动的情况下,控制器可以快速将粘度恢复到设定值,沉降时间小于18 s。本研究提出的熔体粘度实时闭环控制框架对于推进聚合物挤出过程的过程监测和控制具有重要意义。
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来源期刊
Journal of Process Control
Journal of Process Control 工程技术-工程:化工
CiteScore
7.00
自引率
11.90%
发文量
159
审稿时长
74 days
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.
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