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Utilizing big data for batch process modeling and control
Computers & Chemical Engineering ( IF 4.3 ) Pub Date : 2018-09-18 , DOI: 10.1016/j.compchemeng.2018.09.013
Abhinav Garg , Prashant Mhaskar

This manuscript illustrates the use of big data for modeling and control of batch processes. A modeling and control framework is presented that utilizes data variety (temperature or concentration measurements along with size distribution) to achieve newer control objectives. For an illustrative crystallization process, an approach is proposed consisting of a subspace state-space model augmented with a linear quality model, able to model and predict, and therefore control the particle size distribution (PSD). The identified model is deployed in a linear model predictive control (MPC) with explicit model validity constraints. The paper presents two formulations: a) one that minimizes the volume of fines in the product by leveraging the variety of measurements and b) the other that directly controls the shape of the particle size distribution in the product. The former case is compared to traditional control practice while the latter’s superior ability to achieve desired PSD shape is demonstrated.



中文翻译:

利用大数据进行批处理建模和控制

该手稿说明了如何使用大数据对批处理进行建模和控制。提出了一个建模和控制框架,该框架利用数据的多样性(温度或浓度测量值以及大小分布)来实现更新的控制目标。对于说明性的结晶过程,提出了一种由子空间状态空间模型组成的方法,该子空间状态空间模型用线性质量模型进行了增强,能够对模型进行建模和预测,从而控制粒度分布(PSD)。所识别的模型将部署在具有显式模型有效性约束的线性模型预测控制(MPC)中。本文提出了两种表达方式:a)一种通过利用各种测量来最大程度地减少产品中的细粉的体积,b)另一种直接控制产品中粒度分布的形状。将前一种情况与传统的控制方法进行比较,而后者则具有实现所需PSD形状的出色能力。

更新日期:2018-09-18
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