Development of a Modular Automation Framework for Data-Driven Modeling and Optimization of Coating Formulations

Chemistry 4.0 is the new era of chemical process industry, where digitalization, modularization, sustainability, and circular economy play key roles. A growing interest in the use of process data with the aim of gaining a better understanding of the production process and optimizing products can be observed. In chemical industry, data-driven models are used when the production process is too complex to be described by chemical laws. 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 product optimization. Another key element to realize a modular automation framework is standardization, e.g., the Module Type Package (MTP). In this paper, a modular automation framework to optimize coating formulations with data-driven modeling in industrial environment is presented. Therefore, the presented framework automates the acquisition of data via modular and standardized interfaces in a process control system (PCS), data preparation for data-driven modeling, model training, and deployment of the model to propose new coating formulations. The framework shown in this paper is applied to optimize the scratch resistance of a coating using Gaussian processes (GPs) as data-driven modeling method. For the novel automation of machine learning driven design of experiments (DoE), the Bayesian optimization is used.