Development of a Framework for Data-Driven Modeling with Cloud Services in the Process Industry

Abstract

The chemical process industry is currently undergoing a transformation to Chemistry 4.0, where digitalization, modularization, sustainability, and the circular economy are coming into focus. A growing interest in the use of process data with the aim of gaining a better understanding of the production process and conserving resources can be observed. Data-driven modeling is used in chemical industry when the production process is too complex to be described by chemical laws. Gaining knowledge of the chemical relationships can lead to resource-conserving production. In this paper, a framework to optimize the process of data-driven modeling in an industrial environment is presented. For generating data-driven models of industrial processes, many manual and time-consuming steps have to be carried out. This leads to delay in information acquisition and process optimization. Therefore, the presented framework automates these steps to accelerate the process of data-driven modeling. The steps are to extract the data from a process control system (PCS), make the data available for data-driven modeling, train the model, and deploy the model for predicting the process. To achieve high availability of the data and generate data-driven models, cloud services are used. The framework of this paper is applied to a high-throughput formulation system (HTFS) for coatings. In this paper, Gaussian processes are used for data-driven modeling. The evaluation of the framework shows the usefulness in this domain, but also the flexibility and scalability of this framework.