Abstract

Developing analytical solutions for the process industry is a judicious exercise of exploring the available information sources, handling constraints, and overcoming limitations of different natures. These solutions should be flexible, robust, and operationalizable, and must be able to cope with the fundamental characteristics of the systems and data they generate. Their development often requires a pragmatic, problem-oriented perspective, which can yield different proposals compared to those derived with a method-centric focus. Industrial Process Analytics aims to provide the proper context, principles, and methods for developing holistic approaches by bringing together expert knowledge, first principles, and data induction. This work presents the different levels of industrial challenges that must be considered (related to the nature of systems and data collection specifics), as well as the macro-organization of analytical goals, referred to as the Industrial Process Analytics Ladder, that provides a coarser view of the plethora of problems that can be addressed. Existing and emerging challenges pertaining to each step of the Industrial Process Analytics Ladder are briefly referred and some solutions proposed to address them are presented.