Chemical Process Data Science for Industry 4.0

Digitization, robotics, and artificial intelligence are among the key aspects in the ongoing fourth industrial revolution. In this era of big data and powerful computers, the hype around data analytics is palpable and inescapable. While numerous industries have capitalized on analytics for business intelligence (i.e., targeted sales, marketing, etc.), the process industry has not realized the same level of utilization, in part because the fundamental science that governs the processing industry cannot be ignored. Process data science is typically utilized to improve operational efficiency via, e.g., fault detection, process monitoring, and inferential sensors developed using multivariate latent variable techniques, such as principal component analysis. In this talk, an overview of process data science is discussed along with extensions to other applications. For example, it can elucidate key process variables in root cause analyses, leading to a better understanding of the process and, e.g., an improvement in asset utilization. For these projects to be successful, the first-principles cannot be ignored and their incorporation into process models is vital, which is shown in the development of a hybrid fundamental/data-driven reactor model.