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Physics-informed neural networks and time-series transformer for modeling of chemical reactors

Multiscale modeling of catalytical chemical reactors typically results in solving a system of partial differential equations (PDEs) or ordinary differential equations (ODEs). Despite significant progress, the numerical solution of such PDE or ODE …

Toward autocorrection of chemical process flowsheets using large language models

The process engineering domain widely uses Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (P&IDs) to represent process flows and equipment configurations. However, the P&IDs and PFDs, hereafter called flowsheets, can contain …

An Educational Workshop for Effective PSE Course Development

An educational workshop for developing Process Systems Engineering (PSE) courses will be held during ESCAPE-33, following the model workshop that was run during the CAPE Forum 2022 held at the University of Twente, in the Netherlands. This 3-hour …

Data augmentation for machine learning of chemical process flowsheets

Flowsheets are the most important building blocks to define and communicate the structure of chemical processes. Gaining access to large data sets of machine-readable chemical flowsheets could significantly enhance process synthesis through …

Transfer learning for process design with reinforcement learning

Process design is a creative task that is currently performed manually by engineers. Artificial intelligence provides new potential to facilitate process design. Specifically, reinforcement learning (RL) has shown some success in automating process …

Efficient Bayesian Uncertainty Estimation for nnU-Net

The self-configuring nnU-Net has achieved leading performance in a large range of medical image segmentation challenges. It is widely considered as the model of choice and a strong baseline for medical image segmentation. However, despite its …

Flowsheet Recognition using Deep Convolutional Neural Networks

Flowsheets are the most important building blocks to define and communicate the structure of chemical processes. Gaining access to large data sets of machine-readable chemical flowsheets could significantly enhance process synthesis through …

Hybrid mechanistic data-driven modeling for the deterministic global optimization of a transcritical organic Rankine cycle

Global optimization is desirable for the design of chemical and energy processes as design decisions have a significant influence on the economics. A relevant challenge for global flowsheet optimization is the incorporation of accurate thermodynamic …

Modelling circular structures in reaction networks: Petri nets and reaction network flux analysis

Optimal reaction pathways for the conversion of renewable feedstocks are often examined by reaction network flux analysis. An alternative modelling approach for reaction networks is a Petri net. These explicitly take the reaction sequence into …

Deterministic global nonlinear model predictive control with neural networks embedded

Nonlinear model predictive control requires the solution of nonlinear programs with potentially multiple local solutions. Here, deterministic global optimization can guarantee to find a global optimum. However, its application is currently severely …