Process Systems Engineering Overview

Principles

Definition

Process systems engineering is an academic discipline that applies mathematical modeling, computational tools, and systems methodologies to the design, analysis, optimization, and control of complex processes across various scales and domains. This field integrates fundamental principles from engineering and science to achieve efficient, sustainable, and safe process operations in industrial, biological, and environmental systems.

Ontological type

Core Methods

Industrial Applications

Process Control

Key developments

Key Figures

Hybrid Mechanistic Modeling era

John F. MacGregor [1], with affiliations at McMaster University [3] and Université Claude Bernard Lyon 1 [4], was a leading figure in this era. His key contributions include monitoring batch processes using multiway PCA [7], process monitoring and diagnosis by multiblock PLS methods [8], and multivariate SPC Methods for Process and Product Monitoring [9], advancing the hybrid mechanistic-data modeling paradigm for robust fault detection. Costas Kiparissides [2], associated with Queen's University [5] and University of Waterloo [6], contributed to this era's data-driven process-monitoring paradigm. His work, exemplified by the 1994 paper Process monitoring and diagnosis by multiblock PLS methods [8], helped extend multiblock data fusion approaches for robust process monitoring and diagnosis within hybrid mechanistic modeling.

Integrated Design and Scheduling era

Wil M. P. van der Aalst[1] is a central figure in process mining in this era, with associations at The University of Queensland[3] and Utrecht University[4]. van der Aalst[1]'s key contributions include Rediscovering workflow models from event-based data using little thumb[7] and Replaying history on process models for conformance checking and performance analysis[6], which advanced automated discovery and conformance testing of process models—crucial for integrating design, control, and scheduling under uncertainty in this era. Boudewijn F. van Dongen[2] is another prominent contributor in this era, associated with Eindhoven University of Technology[5]. van Dongen[2] co-authored Replaying history on process models for conformance checking and performance analysis[6], a work that advanced conformance checking and performance analysis in process mining and supported coordinated decisions across synthesis, operation, and scheduling horizons.

Modular Hybrid Process Design era

Marcus Baumann [1] is a scholar whose work in the Modular Hybrid Process Design era (2017–2023) intersects with process systems engineering and pharmaceutical chemistry, with affiliations including University College Dublin [3] and Durham University [4]. His key contributions in this era center on integrating continuous flow concepts into pharmaceutical manufacturing, as articulated in A Perspective on Continuous Flow Chemistry in the Pharmaceutical Industry [7], which highlights how modular, flow-based processing can enhance robustness and scalability in hybrid design frameworks. Jay H. Lee [2] is a prominent figure whose work during this era spans international collaboration with Daejeon University [5] and the University of Southern California [6], reflecting a transcontinental reach. His key contribution, captured in Machine learning: Overview of the recent progresses and implications for the process systems engineering field [8], provides a comprehensive survey of machine-learning advances and their implications for surrogate modeling and uncertainty-aware optimization in modular hybrid process design.