Towards the development of digital twins for the bio-manufacturing industry
CL Gargalo, SC de Las Heras, MN Jones… - Digital twins: Tools and …, 2021 - Springer
The bio-manufacturing industry, along with other process industries, now has the opportunity
to be engaged in the latest industrial revolution, also known as Industry 4.0. To successfully …
to be engaged in the latest industrial revolution, also known as Industry 4.0. To successfully …
Life cycle assessment of intensified processes towards circular economy: Omega-3 production from waste fish oil
R Monsiváis-Alonso, SS Mansouri… - … and Processing-Process …, 2020 - Elsevier
Fish oil is a nutritious product, mainly due its omega-3 polyunsaturated fatty acids (ω-3
PUFA) content that is attractive for its health benefits. Fish oil is discarded as a waste in …
PUFA) content that is attractive for its health benefits. Fish oil is discarded as a waste in …
Data-driven pilot optimization for electrochemical CO mass production
Electroreduction systems to convert CO2 into CO via Ag electrodes have been intensely
studied as a means of producing carbon-neutral fuels or chemical products. However …
studied as a means of producing carbon-neutral fuels or chemical products. However …
Synergistic optimization framework for the process synthesis and design of biorefineries
The conceptual process design of novel bioprocesses in biorefinery setups is an important
task, which remains yet challenging due to several limitations. We propose a novel …
task, which remains yet challenging due to several limitations. We propose a novel …
The impact of surrogate models on the multi-objective optimization of Pump-As-Turbine (PAT)
Pump-as-turbine (PAT) technology permits two operating states—as a pump or turbine,
depending on the demand. Nevertheless, designing the geometrical components to suit …
depending on the demand. Nevertheless, designing the geometrical components to suit …
Learning and optimization with Bayesian hybrid models
Bayesian hybrid models fuse physics-based insights with machine learning constructs to
correct for systematic bias. In this paper, we compare Bayesian hybrid models against …
correct for systematic bias. In this paper, we compare Bayesian hybrid models against …
GPdoemd: A Python package for design of experiments for model discrimination
Abstract Model discrimination identifies a mathematical model that usefully explains and
predicts a given system's behaviour. Researchers will often have several models, ie …
predicts a given system's behaviour. Researchers will often have several models, ie …
The Monte Carlo driven and machine learning enhanced process simulator
This study presents a methodology with tools integration to apply advanced uncertainty
propagation and sensitivity analysis in connection with commercial process simulation …
propagation and sensitivity analysis in connection with commercial process simulation …
[HTML][HTML] A study on dynamic active learning for meta-modelling of process simulations
PS Bartolomé, T Van Gerven - Engineering Applications of Artificial …, 2024 - Elsevier
Accurate simulations are of fundamental importance for process engineers, but most modern
simulation software is often inflexible, and cannot be easily integrated into new applications …
simulation software is often inflexible, and cannot be easily integrated into new applications …
A prediction-optimization framework for site-wide process optimization
We address the problem of site-wide operational optimization of production plants in the
context of Industry 4.0 with an emphasis on sensor data-driven approaches. A multi-plant …
context of Industry 4.0 with an emphasis on sensor data-driven approaches. A multi-plant …