Data Fusion: Integrating Heterogeneous Information Sources in the Chemical Processing Industry

ABSTRACT Industrial data have always played a pivotal role in the development, management, and optimization of processes and products. Over time, the intrinsic characteristics of data (modality, acquisition frequency, granularity, uncertainty, etc.) have evolved significantly and so have the computational methods and technology to process and integrate them, transforming these inputs into valuable insights. By integrating multiple heterogeneous data sources, data fusion provides an updated and improved status of the operations, enhancing information quality and leading to more comprehensive, accurate, and insightful analysis of the systems under examination and, therefore, better decision‐making. In this work, we present a review of the data fusion methodologies from the perspective of practitioners, including engineers and data analysts, and how it can be systematically organized into classes of methods dedicated to achieving specific goals. The terminology adopted in the field, not always used consistently and without ambiguity, is also discussed and clarified. Additionally, several case studies are provided to showcase some of the applications and potential advantages of adopting data fusion frameworks.