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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
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projects
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Chemical Product Design
Developping calssical procedures for integrated product and process design
publications
Analysis and evaluation of periodic separations using COPS trays
Published in Chemical Engineering Transactions, 2018
In this paper we evaluate the performance of cyclically operated perforated sheet (COPS) of periodic stripping of a dilute aqueous ammonia solutions with air.
Recommended citation: Nielsen A., Alvarez E.C., Carlsen N., Azizi H., Jorgensen S.B., Abildskov J., 2018, Analysis and evaluation of periodic separations using cops trays, Chemical Engineering Transactions, 69, 733-738 https://doi.org/10.3303/CET1869123s
Comparison of group-contribution and machine learning-based property prediction models with uncertainty quantification
Published in Computer Aided Chemical Engineering, 2021
In this paper, we benchmark and compare three models and techniques for uncertainty quantification applied to a group-contribution model for the lower flammability limit.
Recommended citation: Aouichaoui, A. R., Al, R., Abildskov, J., & Sin, G. (2021). Comparison of group-contribution and machine learning-based property prediction models with uncertainty quantification. In Computer Aided Chemical Engineering (Vol. 50, pp. 755-760). Elsevier. https://doi.org/10.1016/B978-0-323-88506-5.50118-2
Uncertainty estimation in deep learning‐based property models: Graph neural networks applied to the critical properties
Published in AIChE journal, 2022
In this paper we investigate three model agnostic approaches to uncertainty estimation of graph neural networks: ensemble, bootstrap and monte carlo dropout. these techniques were benchmarked on two GNN models and three pure component properties: the critical temperature, pressure and volume.
Recommended citation: Aouichaoui, A. R., Mansouri, S. S., Abildskov, J., & Sin, G. (2022). Uncertainty estimation in deep learning‐based property models: Graph neural networks applied to the critical properties. AIChE Journal, 68(6), e17696. https://doi.org/10.1002/aic.17696
Combining Group-Contribution concept and graph neural networks toward interpretable molecular property models
Published in Journal of Chemical Information and Modeling, 2022
In this paper we develop two new interpretable graph neural networks by integrating groups knwn from group-contribution models as nodes and adding the attention mechanism to highlight important groups.
Recommended citation: Aouichaoui, A. R., Fan, F., Mansouri, S. S., Abildskov, J., & Sin, G. (2023). Combining Group-Contribution concept and graph neural networks toward interpretable molecular property models. Journal of Chemical Information and Modeling, 63(3), 725-744. https://doi.org/10.1021/acs.jcim.2c01091
DeepGSA: Plant Data-Driven Global Sensitivity Analysis using Deep Learning
Published in Computer Aided Chemical Engineering, 2022
In this paper, we developped a Matlab tool for global sensitivity analysis for deep neural netwrks (DNN).
Recommended citation: Aouichaoui, A. R., Al, R., & Sin, G. (2022). DeepGSA: Plant Data-Driven Global Sensitivity Analysis using Deep Learning. In Computer Aided Chemical Engineering (Vol. 49, pp. 1759-1764). Elsevier. https://doi.org/10.1016/B978-0-323-85159-6.50293-1
Molecular representations in deep-learning models for chemical property prediction
Published in Computer Aided Chemical Engineering, 2022
In this paper we compare three different techniques for modelling the enthalpy of formation: group-contribution, QSPR and graph neural networks.
Recommended citation: Aouichaoui, A. R., Fan, F., Mansouri, S. S., & Sin, J. A. G. (2022). Molecular representations in deep-learning models for chemical property prediction. In Computer Aided Chemical Engineering (Vol. 49, pp. 1591-1596). Elsevier. https://doi.org/10.1016/B978-0-323-85159-6.50265-7
On The Interpretability of Graph Neural Networks in QSPR Modeling
Published in Computer Aided Chemical Engineering, 2022
In this paper we explore a new geaph neural network that uses groups as nodes and combine it with the attention mechanism to highlight important substructures.
Recommended citation: Fan, F., Aouichaoui, A. R., & Sin, G. (2022). On The Interpretability of Graph Neural Networks in QSPR Modeling. In Computer Aided Chemical Engineering (Vol. 51, pp. 1393-1398). Elsevier. https://doi.org/10.1016/B978-0-323-95879-0.50233-2
Application of Outlier Treatment Towards Improved Property Prediction Models
Published in Computer Aided Chemical Engineering, 2022
In this paper we apply outlier treatment to improve a set of 18 group-contribution based property models.
Recommended citation: Aouichaoui, A. R., Mansouri, S. S., Abildskov, J., & Sin, G. (2022). Application of Outlier Treatment Towards Improved Property Prediction Models. In Computer Aided Chemical Engineering (Vol. 51, pp. 1357-1362). Elsevier. https://doi.org/10.1016/B978-0-323-95879-0.50227-7
Application of interpretable group-embedded graph neural networks for pure compound properties
Published in Computers & Chemical Engineering, 2023
In this paper we apply two new interpretable graph neural networks to 30 different pure componenent properties. This is so far the largest study of GNN on pure componenent poperties.
Recommended citation: Aouichaoui, A. R., Fan, F., Abildskov, J., & Sin, G. (2023). Application of interpretable group-embedded graph neural networks for pure compound properties. Computers & Chemical Engineering, 176, 108291. https://doi.org/10.1016/j.compchemeng.2023.108291
S-GNN: State-Dependent Graph Neural Networks for Functional Molecular Properties
Published in Computer Aided Chemical Engineering, 2023
In this paper we extended the applicability of graph neural networks to temperature dependent properties. This extension was tested on 13 different properties.
Recommended citation: Aouichaoui, A. R., Cogliati, A., Abildskov, J., & Sin, G. (2023). S-GNN: State-Dependent Graph Neural Networks for Functional Molecular Properties. In Computer Aided Chemical Engineering (Vol. 52, pp. 575-581). Elsevier. https://doi.org/10.1016/B978-0-443-15274-0.50091-3
Energy- and process real-time optimization through hybrid modeling - a case from Viking Malt A/S
Published in Computer Aided Chemical Engineering, 2024
In this paper we apply a hybrid modelling to monitor the germination process in the malting process.
Recommended citation: Jul-Rasmussen, P., Aouichaoui, A. R., Korzepa, M., Engelbrecht, K., Kongsgaard, K., & Huusom, J. K. (2024). Energy-and process real-time optimization through hybrid modeling-a case from Viking Malt A/S. In Computer Aided Chemical Engineering (Vol. 53, pp. 1645-1650). Elsevier. https://doi.org/10.1016/B978-0-443-28824-1.50275-1
Scoping and Identifying Data-Driven Optimization Prospects in the Danish Processing Industry
Published in Computer Aided Chemical Engineering, 2024
In this paper we present a new framework for developping ditital twins intended for process monitoring and optimization.
Recommended citation: Aouichaoui, A. R., Ernstsson, B. B., Jul-Rasmussen, P., Iversen, N. H., Vermue, L., & Huusom, J. K. (2024). Scoping and Identifying Data-Driven Optimization Prospects in the Danish Processing Industry. In Computer Aided Chemical Engineering (Vol. 53, pp. 451-456). Elsevier. https://doi.org/10.1016/B978-0-443-28824-1.50076-4
Towards Self-Consistent Graph Neural Networks for Predicting the Ideal Gas Heat Capacity, Enthalpy, and Entropy
Published in Computer Aided Chemical Engineering, 2024
In this paper we present one hybrid modelling configuration that combines graph neural networks with a fundamental equation for calculation the heat capacity of organic compounds.
Recommended citation: Aouichaoui, A. R., Müller, S., & Abildskov, J. (2024). Towards Self-Consistent Graph Neural Networks for Predicting the Ideal Gas Heat Capacity, Enthalpy, and Entropy. In Computer Aided Chemical Engineering (Vol. 53, pp. 2833-2838). Elsevier. https://doi.org/10.1016/B978-0-443-28824-1.50473-7
talks
Importance of Outlier Treatment for Property Prediction Models
Published:
I gave a talk about the work we produced here
Comparison of Group-Contribution and Machine Learning-based Property Prediction Models with Uncertainty Quantification
Published:
I gave a talk concerning the work we produced here.
Data Scarcity in Developing Property Prediction Models: Application of Multi-Task Transfer
Published:
I gave a talk on how to leverage multi-task and transfer learning in cases where the data availability is limited.
Benchmarking Uncertainty Quantification Methods for Property Prediction Models: Application to Group-Contribution Models
Published:
I gave a keynote talk about benchmarking various uncertainty estimation techniques applied to group-contribution models.
Multi-Task Property Prediction: Importance of the “Chemist-in-the-Loop” in Model
Published:
Abstract can be found here
Prediction of Nitrous Oxide Concentration in Wastewater Treatment Plants: Application and Benchmarking of Deep-Learning Models
Published:
Abstract can be found here
An Interpretable Graph Neural Network-based Property Prediction
Published:
I gave a talk about the work we produced here
Application of Outlier Treatment Towards Improved Property Prediction Models
Published:
I gave a talk about the work we produced here
Molecular Representations in Deep-Learning Models for Chemical Property Prediction
Published:
I gave a talk about the work we produced here
DeepGSA: Plant Data-Driven Global Sensitivity Analysis using Deep Learning
Published:
I gave a talk about the work we produced here
Integrating Chemistry Knowledge and Graph Neural Networks Towards Interpretable Molecular Property Models
Published:
I gave a talk about our working in combining chemistry knwoledge into graph neural networks.
Combining Functional Groups and Graph Neural Networks Towards Interpretable Molecular Property Models
Published:
The abstract of the talk can be found here
State-aware graph neural networks for temperature-dependent propertie
Published:
I gave a talk about the work we produced here
Towards Self-Consistent Graph Neural Networks for Predicting the Ideal Gas Heat Capacity, Enthalpy, and Entropy
Published:
I gave a talk about the work we produced here
teaching
Risk Assessment in Chemical and Biochemical Industry
Masters-level course, Technical University of Denmark, Department of Chemical and Biochemical Engineering, 2020
Teaching Assistant (TA) for the course. Main responsibilities:
- Delivering tutorials on HAZOP and QRA
- Developping tutorials and assignments
- Grading and providing feedback for assignments
- Deliver selected lectures
Chemical Process Control
Bachelor-level course, Technical University of Denmark, Department of Chemical and Biochemical Engineering, 2020
Teaching Assistant (TA) for the course. Main responsibilities:
- Delivering tutorials on process modelling and control
- Supervising pilot scale experiments to validate control strategies
- Grading and providing feedback for assignments
Uncertainty and sensitivity analysis of numerical models
Ph.D.-level course, Technical University of Denmark, Department of Chemical and Biochemical Engineering, 2021
Teaching Assistant (TA) for the course. Main responsibilities:
- Delivering tutorials on various techniques for uncertainty and sensitivity techniques
- Provide support in implementing the techniques in Matlab
- Developping tutorials and assignments
- Providing feedback for assignments
Process Design: Principles and Methods
Masters-level course, Technical University of Denmark, Department of Chemical and Biochemical Engineering, 2022
Teaching Assistant (TA) for the course. Main responsibilities:
- Delivering tutorials on process design
- Provide support in implementing various unit operations in Aveva Process simulation and PROII
- Developping tutorials and assignments
- Grading and providing feedback for assignments
Separation Processes
Masters-level course, Technical University of Denmark, Department of Chemical and Biochemical Engineering, 2023
Teaching Assistant (TA) for the course. Main responsibilities:
- Delivering tutorials on various techniques in advanced separation processes
- Developping tutorials and assignments
- Providing feedback for assignments
Automation and control of yeast fermentation
Ph.D.-level course, Technical University of Denmark, Department of Chemical and Biochemical Engineering, 2024
Teaching Assistant (TA) for the course. Main responsibilities:
- Delivering tutorials on various techniques for for modelling and control of yeast fermentation processes
- Applying advanced analytics such as Monte-Carlo simulation and sensitivity analysis
- Developing tutorials, course materials and assignments
- Developing the code base in Python
- Provide support in implementing the techniques in Python