Low-density polyethylene (LDPE) is a valued commodity plastic with versatile applications. However, controlling the LDPE polymerization reactor is a challenging task due to its nonlinear behavior and multivariable process and operates in a board operating region. This work aims to develop and explore the neural Wiener MPC (NWMPC) application in controlling the LDPE tubular reactor process. The motivation for NWMPC development originates from its advantage in terms of lower development effort, time, resource, and computational costs compared to nonlinear MPC (NMPC) using the first principle model (FPM). In order to develop the LDPE tubular reactor model, the Aspen Plus and Aspen Dynamic software are used to simulate the process in steady-state and dynamic form. The neural Wiener (NW) model identification is performed using state space and neural network model identification using Matlab software. The identification result shows the ability of the NW model to identify the nonlinear LDPE tubular reactor process successfully. Furthermore, the NWMPC has outperformed the state space MPC (SSMPC) in controlling grade transition, feed pressure loss, feed impurity disturbance, and heat of polymerization change during the online closed-loop performance tests. These results signify the ability of NWMPC to handle practical LDPE tubular reactor control scenarios.

中文翻译：

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使用神经维纳模型预测控制的低密度聚乙烯管式反应器控制

低密度聚乙烯 (LDPE) 是一种用途广泛的有价值的商品塑料。然而，控制 LDPE 聚合反应器是一项具有挑战性的任务，因为它具有非线性行为和多变量过程，并且在板操作区域中运行。这项工作旨在开发和探索神经维纳 MPC (NWMPC) 在控制 LDPE 管式反应器过程中的应用。与使用第一原理模型 (FPM) 的非线性 MPC (NMPC) 相比，NWMPC 开发的动机源于其在更低的开发工作量、时间、资源和计算成本方面的优势。为了开发 LDPE 管式反应器模型，使用 Aspen Plus 和 Aspen Dynamic 软件以稳态和动态形式模拟过程。神经维纳 (NW) 模型识别使用状态空间和神经网络模型识别使用 Matlab 软件进行。辨识结果表明，NW 模型能够成功辨识非线性 LDPE 管式反应器过程。此外，在在线闭环性能测试期间，NWMPC 在控制等级转变、进料压力损失、进料杂质扰动和聚合热变化方面的表现优于状态空间 MPC (SSMPC)。这些结果表明 NWMPC 处理实际 LDPE 管式反应器控制场景的能力。在在线闭环性能测试期间，NWMPC 在控制等级转变、进料压力损失、进料杂质扰动和聚合热变化方面优于状态空间 MPC (SSMPC)。这些结果表明 NWMPC 处理实际 LDPE 管式反应器控制场景的能力。在在线闭环性能测试期间，NWMPC 在控制等级转变、进料压力损失、进料杂质扰动和聚合热变化方面优于状态空间 MPC (SSMPC)。这些结果表明 NWMPC 处理实际 LDPE 管式反应器控制场景的能力。