Application of a grey-box modelling approach for the online monitoring of batch production in the chemical industry

Abstract

Model-based solutions for monitoring and control of chemical batch processes have been of interest in research for many decades. However, unlike in continuous processes, in which model-based tools such as Model Predictive Control (MPC) have become a standard in the industry, the reported use of models for batch processes, either for monitoring or control, is rather scarce. This limited use is attributed partly to the inherent complexity of the batch processes (e. g., dynamic, nonlinear, multipurpose) and partly to the lack of appropriate commercial tools in the past. In recent years, algorithms and commercial tools for model-based monitoring and control of batch processes have become more mature and in the era of Industry 4.0 and digitalization they are slowly but steadily gaining more interest in real-word batch applications. This contribution provides a practical example in this application field. Specifically, the use of a grey-box modeling approach, in which a multiway Projection to Latent Structure (PLS) model is combined with a first-principles model, to monitor the evolution of a batch polymerization process and predict in real-time the final batch quality is reported. The modeling approach is described, and the experimental results obtained from an industrial batch laboratory reactor are presented.

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

“…Carneiro [33], Wang [34] 2021-2023 Prediction of steel properties Zou [35], Feng [36], Liu [37], Wang [38], Qian [39] 2021-2023 Prediction of molten steel composition Wang [40] 2022 Energy efficiency Lee [41] 2021 Motor equipment load Huang [42], Yu [43] 2022-2023 Modeling and prediction of inventory change Zhou [44], Esche [45], Zhu [46], Li [47], Bouaswaig [48],…”

Efficient Process for Batch Analysis

“…Two key quality key performance indicators (KPI) need to be ensured: the residual unreacted monomer needs to be below a certain threshold and the average polymer chain length needs to be within a certain range. In [13], the data-driven model is complemented with a rigorous model to handle process nonlinearities and the real-time alignment of the batch data. MPLS is used as the core diagnostic method.…”

“…In [13], an industrial laboratory reactor for polymerization is being studied. Two key quality key performance indicators (KPI) need to be ensured: the residual unreacted monomer needs to be below a certain threshold and the average polymer chain length needs to be within a certain range.…”

Gray‐Box Modeling of the Molecular Weight Distribution in a Batch Polymerization Reactor

“…The model is intended for application in nonlinear model predictive control of the reactor temperature; therefore, the only quality-related output variable is the monomer conversion. Bouaswaig et al report a gray-box approach for data-driven soft sensing of quality parameters in a radical polymerization reaction using multiway partial least squares regression [6]. Here, the white-box component is a fully parametrized, rigorous process model, therefore the described approach is not readily applicable to cases where a kinetic model is not available.…”