Utilizing big data for batch process modeling and control. (November 2018)

• Record Type: Journal Article
• Authors: Garg, Abhinav; Mhaskar, Prashant
• Is Part Of: Computers & electrical engineering. Volume 72(2018)
• Page Start: 237
• Page End: 247
• Publication Date: 2018-11
• Subjects: Batch process -- Subspace identification -- Model predictive control -- Big-data -- Data driven model predictive control; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.09.017
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Utilizes data variety in subspace identification and control of batch processes. The modeling approach is able to use data variety for batches of unequal lengths. A novel model predictive control (MPC) with explicit model validity constraints, and the ability to specify target PSD is proposed. The approach is demonstrated on batch crystallization process. Abstract: 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.