Publications

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

Toward autocorrection of chemical process flowsheets using large language models

Generative artificial intelligence in chemical engineering

Generative artificial intelligence

Machine learning in process systems engineering: Challenges and opportunities

Machine learning in process systems engineering

Data-driven Product-Process Optimization of N-isopropylacrylamide Microgel Flow-Synthesis

Data-driven Product-Process Optimization

A review and perspective on hybrid modeling methodologies

Hybrid modeling

Empirical assessment of ChatGPT’s answering capabilities in natural science and engineering

ChatGPT

Deep reinforcement learning for process design: Review and perspective

Deep reinforcement learning

An Educational Workshop for Effective PSE Course Development

Data augmentation for machine learning of chemical process flowsheets

Transfer learning for process design with reinforcement learning

Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning

Molecular design

Graph machine learning for design of high-octane fuels

Molecular design

Toward automatic generation of control structures for process flow diagrams with large language models

Piping and Instrumentation Diagrams (P&IDs)

Digitization of chemical process flow diagrams using deep convolutional neural networks

Flowsheet digitization

Geometry optimization of a continuous millireactor via CFD and Bayesian optimization

Geometry optimization

Learning from flowsheets: A generative transformer model for autocompletion of flowsheets

Flowsheets autocompletion

Flowsheet generation through hierarchical reinforcement learning and graph neural networks

Reinforcement learning for process design

SFILES 2.0: an extended text-based flowsheet representation

SFILES 2.0

Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids

Porperty prediction

Efficient Bayesian Uncertainty Estimation for nnU-Net

Flowsheet Recognition using Deep Convolutional Neural Networks

HybridML: Open source platform for hybrid modeling

A tool for hybrid modeling.

Physical pooling functions in graph neural networks for molecular property prediction

Physical pooling functions

Multi-objective Bayesian optimisation of a two-step synthesis of p-cymene from crude sulphate turpentine

Pushing nanomaterials up to the kilogram scale – An accelerated approach for synthesizing antimicrobial ZnO with high shear reactors, machine learning and high-throughput analysis

Efficient hybrid multiobjective optimization of pressure swing adsorption

Machine learning in chemical engineering: A perspective

Discussion of perspecitves for future interdisciplinary research and transformation of chemical engineering by identifying challenges and formulation problems for machine learning.

Insight to gene expression from promoter libraries with the machine learning workflow Exp2Ipynb

Chemical data intelligence for sustainable chemistry

Designing production-optimal alternative fuels for conventional, flexible-fuel, and ultra-high efficiency engines

Deterministic global optimization with Gaussian processes embedded

Obey validity limits of data-driven models through topological data analysis and one-class classification

Globally optimal working fluid mixture composition for geothermal power cycles

The potential of hybrid mechanistic/data‐driven approaches for reduced dynamic modeling: application to distillation columns

Deterministic global superstructure-based optimization of an organic Rankine cycle

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

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

Graph neural networks for prediction of fuel ignition quality

Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning

Nonlinear scheduling with time‐variable electricity prices using sensitivity‐based truncations of wavelet transforms

Deterministic global nonlinear model predictive control with neural networks embedded

Working fluid selection for organic rankine cycles via deterministic global optimization of design and operation

Simultaneous rational design of ion separation membranes and processes

Automated self-optimisation of multi-step reaction and separation processes using machine learning

Impact of accurate working fluid properties on the globally optimal design of an organic Rankine cycle

Deterministic global process optimization: Flash calculations via artificial neural networks

Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis

Deterministic global optimization with artificial neural networks embedded

Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks

Model-based bidding strategies on the primary balancing market for energy-intense processes

Rational design of ion separation membranes

Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives

Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes

Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm

Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm

The concept of selectivity control by simultaneous distribution of the oxygen feed and wall temperature in a microstructured reactor

A multiobjective optimization including results of life cycle assessment in developing biorenewables-based processes

Techno-economic optimization of a green-field post-combustion CO2 capture process using superstructure and rate-based models