A5046617955- Process Optimization and Integration
- Phase Equilibria and Thermodynamics
- Advanced Control Systems Optimization
- Chemical Thermodynamics and Molecular Structure
- Thermodynamic properties of mixtures
- Crystallization and Solubility Studies
- Chemical and Physical Properties in Aqueous Solutions
- Catalytic Processes in Materials Science
- Industrial Gas Emission Control
- Carbon Dioxide Capture Technologies
- Ionic liquids properties and applications
- Analytical Chemistry and Chromatography
- Extraction and Separation Processes
- Manufacturing Process and Optimization
- Microbial Metabolic Engineering and Bioproduction
- Membrane Separation and Gas Transport
- Chemistry and Chemical Engineering
- Computational Drug Discovery Methods
- Membrane Separation Technologies
- Machine Learning in Materials Science
- Fault Detection and Control Systems
- Thermal and Kinetic Analysis
- Advanced Thermodynamics and Statistical Mechanics
- Probabilistic and Robust Engineering Design
- Thermodynamic and Exergetic Analyses of Power and Cooling Systems
Technical University of Denmark
2016-2025
Alfa Laval (Sweden)
2018
Institute for Computer Aided Design
2004-2013
AstraZeneca (United Kingdom)
2008
University of Birmingham
2007
University of Virginia
2003
Quantitative structure-property relationships (QSPRs) are important tools to facilitate and accelerate the discovery of compounds with desired properties. While many QSPRs have been developed, they associated various shortcomings such as a lack generalizability modest accuracy. Albeit machine-learning deep-learning techniques integrated into models, another shortcoming has emerged in form transparency interpretability models. In this work, two interpretable graph neural network (GNN) models...
A rigorous methodology is developed that addresses numerical and statistical issues when developing group contribution (GC) based property models such as regression methods, optimization algorithms, performance statistics, outlier treatment, parameter identifiability, uncertainty of the prediction. The evaluated through development a GC method for prediction heat combustion (ΔHco) pure components. results showed robust lead to best statistics estimation. bootstrap found be valid alternative...
Solvent selection is one of the major concerns in early development many chemicals-based products from pharmaceutical, agrochemicals, food, and specialty chemicals industries. Because nature active product, most important solvent property solubility complex solids. Predictive models for estimation solid different organic solvents, especially suitable solvent-selection procedures, are reviewed. Also, schemes that can be employed and/or calculation through limited available experimental data...
In nonaqueous enzymology, control of enzyme hydration is commonly approached by fixing the thermodynamic water activity medium. this work, we present a strategy for evaluating in molecular dynamics simulations proteins water/organic solvent mixtures. The method relies on determining content bulk phase and uses combination Kirkwood–Buff theory free energy calculations to determine corresponding coefficients. We apply study Candida antarctica lipase B pure organic solvents methanol, tert-butyl...
Scientific projects frequently involve measurements of thermophysical, thermochemical, and other related properties chemical compounds materials. These measured property data have significant potential value for the scientific community, but incomplete inaccurate reporting often hampers their utilization. The present IUPAC Technical Report summarizes needs engineers researchers as consumers these shows how publishing practices can improve information transfer. In Report, general principles...
The ability to evaluate pure compound properties of various molecular species is an important prerequisite for process simulation in general and particular computer-aided design (CAMD). Current techniques rely on group-contribution methods, which suffer from many drawbacks mainly the absence contributions specific groups. To overcome this challenge, work, we extended range interpretable graph neural network models describing a wide component properties. new model library contains 30...
This study utilizes a new mobile pilot plant to capture CO2 from real biogas with 30 wt% monoethanolamine (MEA). The is described in detail and validated by data reconciliation showing that the produces reliable steady state data. It demonstrated can upgrade 32 kg per hour capturing 20 hour. Further, achieve below 2.5 mol% methane product at specific reboiler duty of 3.74 GJ ton CO2. absorber column was found be operated close pinch conditions as temperatures reached above 90 °C giving...
This study investigates the potential of a water-lean 30 wt% monoethanolamine (MEA) solvent for biogas upgrading. The consists MEA, 15 monoethyleneglycol (MEG), and 55 water at zero loading. is investigated using mobile pilot scale amine scrubbing unit, with capacity 1 ton CO2 per day, option to utilize rich recycle (RSR) configuration. Without RSR configuration, obtains low loading 0.46 mol/mol resulting in specific reboiler duty (SRD) 3.92 MJ kg CO2, which 6 % higher compared aqueous MEA....
Accurate prediction of thermophysical properties is important in chemical engineering, where group-contribution models (GCM) have been used extensively. Traditional GC-based tend to use all available data for parameter estimation, preventing a fair comparison with machine learning (ML) methods that require separate training, validation, and testing data. In this study, we introduce new splitting algorithm which optimally partitions molecular datasets by ensuring comprehensive group...
Abstract Deep learning and graph‐based models have gained popularity in various life science applications such as property modeling, achieving state‐of‐the‐art performance. However, the quantification of prediction uncertainty these is less studied not applied low dataset size regime, which characterizes many chemical engineering‐related molecular properties. In this work, we apply two to model critical‐ temperature, pressure, volume three techniques (the bootstrap, ensemble, dropout)...
This study was aimed at evaluating different binary solvent mixtures for efficient industrial monoacylglycerol (MAG) production by enzymatic glycerolysis. Of all investigated cases, the mixture of tert-butanol:tert-pentanol (TB:TP) 80:20 vol % most suitable organic medium continuous glycerolysis, ensuring high MAG formation in a short time, reasonable price, and easy handling during distillation/condensation processing. A minimum dosage 44-54 wt reaction necessary to achieve yields 47-56 %,...
Process intensification in distillation systems has received much attention during past decades, with the aim of increasing both energy and separation efficiency. Various techniques, such as internal heat-integrated distillation, membrane rotating packed bed, dividing-wall columns reactive were studied reported literature. All these techniques employ conventional continuous counter-current contact vapor liquid phases. Cyclic technology is based on an alternative operating mode using separate...