Inferential Model Predictive Control of Continuous Pulping under Grade Transition,Industrial & Engineering Chemistry Research

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Inferential Model Predictive Control of Continuous Pulping under Grade Transition
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2021-02-24 , DOI: 10.1021/acs.iecr.0c06216
Hyun-Kyu Choi _{1,

2} , Sang Hwan Son _{1,

2} , Joseph Sang-Il Kwon _{1,

2}

Affiliation

Even though continuous pulp processes have been studied for many years, the absence of a model that can accurately describe the evolution of fiber morphology has impeded the application of advanced control techniques. In this study, a multiscale model for continuous Kraft pulping processes, which can capture the spatiotemporal evolution of wood chips and cooking liquor, is developed by integrating a macroscopic model (i.e., Purdue model) with a microscopic model (i.e., kinetic Monte Carlo algorithm). Then, an approximate model is identified to circumvent the high computational requirement of the multiscale model and to handle the input time-delay, followed by designing a soft sensor to infer state variables and primary measurements. This allows the use of an inferential model predictive control strategy in a continuous pulp digester to regulate the blow-line pulp properties (i.e., Kappa number and cell wall thickness) and achieve optimal grade transitions.

中文翻译：

坡度过渡下连续制浆的推理模型预测控制

尽管已经研究了连续纸浆工艺多年，但缺少能够准确描述纤维形态演变的模型，这阻碍了先进控制技术的应用。在这项研究中，通过将宏观模型（即，Purdue模型）与微观模型（即，动力学蒙特卡洛算法）相集成，开发了一种连续牛皮纸制浆过程的多尺度模型，该模型可以捕获木片和蒸煮液的时空演变。）。然后，确定一个近似模型来规避多尺度模型的高计算要求并处理输入时延，然后设计一个软传感器来推断状态变量和主要测量值。

更新日期：2021-03-10

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