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 …

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 …

Data-driven pilot optimization for electrochemical CO mass production

K Kim, WH Lee, J Na, YJ Hwang, HS Oh… - Journal of Materials …, 2020 - pubs.rsc.org
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 …

Synergistic optimization framework for the process synthesis and design of biorefineries

NI Vollmer, R Al, KV Gernaey, G Sin - Frontiers of Chemical Science and …, 2022 - Springer
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 …

The impact of surrogate models on the multi-objective optimization of Pump-As-Turbine (PAT)

S Ntiri Asomani, J Yuan, L Wang, D Appiah… - Energies, 2020 - mdpi.com
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 …

Learning and optimization with Bayesian hybrid models

EA Eugene, X Gao, AW Dowling - 2020 American Control …, 2020 - ieeexplore.ieee.org
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 …

GPdoemd: A Python package for design of experiments for model discrimination

S Olofsson, L Hebing, S Niedenführ… - Computers & Chemical …, 2019 - Elsevier
Abstract Model discrimination identifies a mathematical model that usefully explains and
predicts a given system's behaviour. Researchers will often have several models, ie …

The Monte Carlo driven and machine learning enhanced process simulator

MN Jones, J Frutiger, NG Ince, G Sin - Computers & Chemical Engineering, 2019 - Elsevier
This study presents a methodology with tools integration to apply advanced uncertainty
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 …

A prediction-optimization framework for site-wide process optimization

D Subramanian, P Murali, N Zhou, X Ma… - … Congress on Internet …, 2019 - ieeexplore.ieee.org
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 …