Chemical Engineering

Chemical engineering is focused on designing, optimizing, and controlling chemical processes, with current research heavily emphasizing the integration of artificial intelligence and machine learning to improve efficiency and sustainability. This involves developing advanced models, such as those based on group contribution methods (e.g., UNIFAC 2.0), deep reinforcement learning, and graph neural networks, to predict thermodynamic properties, design optimal processes, and create accurate soft sensors for real-time process monitoring and control. These advancements are crucial for addressing challenges in areas like renewable energy integration and improving the accuracy and reliability of chemical processes across various industries.

Papers

July 25, 2024

Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0
Nicolas Hayer, Thorsten Wendel, Stephan Mandt, Hans Hasse, Fabian Jirasek
Machine Learning Client Contribution Thermodynamic Integration Pairwise Interaction Activity Coefficient Chemical Engineering

August 15, 2023

Deep reinforcement learning for process design: Review and perspective
Qinghe Gao, Artur M. Schweidtmann
Reinforcement Learning Deep Reinforcement Learning Narrative Review Visual Perspective Process Design Chemical Engineering

January 3, 2023

Machine Learning Approach to Polymerization Reaction Engineering: Determining Monomers Reactivity Ratios
Tung Nguyen, Mona Bavarian
Machine Learning Approach Molecular Structure Chemical Engineering Reactivity Ratio

August 8, 2022

Soft Sensors and Process Control using AI and Dynamic Simulation
Shumpei Kubosawa, Takashi Onishi, Yoshimasa Tsuruoka
Artificial Intelligence Soft Sensor Industrial Process Dynamic Simulation Process Control Chemical Engineering

July 25, 2022

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
Laura Stops, Roel Leenhouts, Qinghe Gao, Artur M. Schweidtmann
Neural Network Graph Drawing Reinforcement Learning Algorithm Reinforcement Learning Agent Hierarchical Reinforcement Learning Actor Critic Analysis by Synthesis Chemical Engineering Chemical Process Flowsheets

May 10, 2022

Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques
Marco Pegoraro
High Uncertainty Anticipation Probabilistic Model Process Mining Event Data Data Uncertainty Chemical Engineering

January 25, 2022

Maximizing information from chemical engineering data sets: Applications to machine learning
Alexander Thebelt, Johannes Wiebe, Jan Kronqvist, Calvin Tsay, Ruth Misener
Machine Learning Financial Application Full Information Chemical Engineering

Chemical Engineering

Papers

Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0

Deep reinforcement learning for process design: Review and perspective

Machine Learning Approach to Polymerization Reaction Engineering: Determining Monomers Reactivity Ratios

Soft Sensors and Process Control using AI and Dynamic Simulation

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks

Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques

Maximizing information from chemical engineering data sets: Applications to machine learning