Dear Colleagues,

Industrial operations are characterized by the continuous search for more efficient process operations, more reliable and robust equipment designs, maximization of benefits, and minimization of deleterious environmental and social impacts. Simultaneously, the digital revolution provides increasingly cheaper computation platforms, with increasingly larger storage, memory, and computational capabilities. The combination of these has encouraged the continuous development of new modeling and numerical tools that are expected to be applied in actual industrial sites throughout the world and in different process engineering fields, for both offline and online analyses. Typical examples include the use of complex phenomenological models for the design of actual industrial equipment and process flowsheets; implementation of empirical models based on machine learning procedures for process monitoring, fault identification, and diagnosis; and utilization of identification techniques for the construction of virtual sensors and process twins, for purposes of process monitoring and control.

Based on these, this Special Issue on “Industrial Applications of Modeling Tools” aims to present new mathematical and numerical tools that have been implemented to solve real industrial problems at different production stages and that have been validated with actual industrial data, from process design to process monitoring and control. Some typical topics and applications include:

• Use of detailed phenomenological models for the design of actual process equipment and process flowsheets;
• Implementation of novel numerical schemes for faster design of actual process equipment and process flowsheets;
• Use of phenomenological and empirical models for process scale-up and optimization;
• Development and implementation of data-driven models for process monitoring (soft sensors), control, fault detection, and fault diagnosis;
• Online and real time implementation of phenomenological models for process identification (digital twins) and control;
• Novel numerical procedures for enhancement of numerical performance in actual industrial real-time applications;
• Use of empirical and phenomenological models for just-in-time maintenance.

Prof. Dr. Jose Carlos Pinto
Dr. Tahyná Barbalho Fontoura
Dr. Tiago Lemos
Prof. Dr. André Domingues Quelhas
Guest Editors

Manuscript Submission Information

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• mathematical modeling
• process simulation and control
• process monitoring
• process intelligence and machine learning
• industrial applications