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All Outputs (10)

Novel comprehensive life cycle assessment (LCA) of sustainable flue gas carbon capture and utilization (CCU) for surfactant and fuel via Fischer-Tropsch synthesis (2025)
Journal Article
Sadhukhan, J., Fisher, O. J., Cummings, B., & Xuan, J. (2025). Novel comprehensive life cycle assessment (LCA) of sustainable flue gas carbon capture and utilization (CCU) for surfactant and fuel via Fischer-Tropsch synthesis. Journal of Co2 Utilization, 92, Article 103013. https://doi.org/10.1016/j.jcou.2024.103013

This novel study presents an effective comprehensive life cycle assessment (LCA) of a novel sustainable carbon dioxide capture and utilization (CCU) system to co-produce alcohol ethoxylate (AE7), a valuable surfactant (a high-value chemical component... Read More about Novel comprehensive life cycle assessment (LCA) of sustainable flue gas carbon capture and utilization (CCU) for surfactant and fuel via Fischer-Tropsch synthesis.

Techno-economic analysis and process simulation of alkoxylated surfactant production in a circular carbon economy framework (2024)
Journal Article
Fisher, O. J., Sadhukhan, J., Daniel, T., & Xuan, J. (2024). Techno-economic analysis and process simulation of alkoxylated surfactant production in a circular carbon economy framework. Digital Chemical Engineering, 13, Article 100199. https://doi.org/10.1016/j.dche.2024.100199

Successfully transitioning to a net-zero and circular carbon economy requires adopting innovative technologies and business models to capture CO2 and convert it into valuable chemicals and materials. Given the high economic costs and limited funding... Read More about Techno-economic analysis and process simulation of alkoxylated surfactant production in a circular carbon economy framework.

Dynamic optimisation of CO2 electrochemical reduction processes driven by intermittent renewable energy: Hybrid deep learning approach (2024)
Journal Article
Yee Tai, X., Xing, L., Zhang, Y., Fu, Q., Fisher, O., D.R. Christie, S., & Xuan, J. (2024). Dynamic optimisation of CO2 electrochemical reduction processes driven by intermittent renewable energy: Hybrid deep learning approach. Digital Chemical Engineering, 9, Article 100123. https://doi.org/10.1016/j.dche.2023.100123

The increasing demand for net zero solutions has prompted the exploration of electrochemical CO2 reduction reaction (eCO2RR) systems powered by renewable energy sources. Here, we present a comprehensive AI-enabled framework for the adaptive optimisat... Read More about Dynamic optimisation of CO2 electrochemical reduction processes driven by intermittent renewable energy: Hybrid deep learning approach.

Promoting equality, diversity and inclusion in research and funding: reflections from a digital manufacturing research network (2024)
Journal Article
Fisher, O. J., Fearnshaw, D., Watson, N. J., Green, P., Charnley, F., McFarlane, D., & Sharples, S. (2024). Promoting equality, diversity and inclusion in research and funding: reflections from a digital manufacturing research network. Research Integrity and Peer Review, 9(1), Article 5. https://doi.org/10.1186/s41073-024-00144-w

Background: Equal, diverse, and inclusive teams lead to higher productivity, creativity, and greater problem-solving ability resulting in more impactful research. However, there is a gap between equality, diversity, and inclusion (EDI) research and p... Read More about Promoting equality, diversity and inclusion in research and funding: reflections from a digital manufacturing research network.

Responsive CO2 capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply (2023)
Journal Article
Fisher, O. J., Xing, L., Tian, X., Tai, X. Y., & Xuan, J. (2024). Responsive CO2 capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply. Reaction Chemistry and Engineering, 9(2), 235-250. https://doi.org/10.1039/d3re00544e

The drive for efficiency improvements in CO2 capture technologies continues to grow, with increasing importance given to the need for flexible operation to adapt to the strong fluctuations in the CO2-rich flue gas flow rate and CO2 concentration. Usi... Read More about Responsive CO2 capture: predictive multi-objective optimisation for managing intermittent flue gas and renewable energy supply.

Digital transformation and the circular economy: Creating a competitive advantage from the transition towards Net Zero Manufacturing (2022)
Journal Article
Okorie, O., Russell, J., Cherrington, R., Fisher, O., & Charnley, F. (2023). Digital transformation and the circular economy: Creating a competitive advantage from the transition towards Net Zero Manufacturing. Resources, Conservation and Recycling, 189, Article 106756. https://doi.org/10.1016/j.resconrec.2022.106756


Manufacturers are exploring the extent to which digital technology applications can support their sustainability efforts by helping to convert abstract sustainability goals, such as those of net-zero emissions and circular economy... Read More about Digital transformation and the circular economy: Creating a competitive advantage from the transition towards Net Zero Manufacturing.

Data-driven modelling for resource recovery: Data volume, variability, and visualisation for an industrial bioprocess (2022)
Journal Article
Fisher, O., Watson, N. J., Porcu, L., Bacon, D., Rigley, M., & Gomes, R. L. (2022). Data-driven modelling for resource recovery: Data volume, variability, and visualisation for an industrial bioprocess. Biochemical Engineering Journal, 185, Article 108499. https://doi.org/10.1016/j.bej.2022.108499

Advances in industrial digital technologies have led to an increasing volume of data generated from industrial bioprocesses, which can be utilised within data-driven models (DDM). However, data volume and variability complications make developing mod... Read More about Data-driven modelling for resource recovery: Data volume, variability, and visualisation for an industrial bioprocess.

Intelligent Sensors for Sustainable Food and Drink Manufacturing (2021)
Journal Article
Watson, N. J., Bowler, A. L., Rady, A., Fisher, O. J., Simeone, A., Escrig, J., Woolley, E., & Adedeji, A. A. (2021). Intelligent Sensors for Sustainable Food and Drink Manufacturing. Frontiers in Sustainable Food Systems, 5, Article 642786. https://doi.org/10.3389/fsufs.2021.642786

Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight pro... Read More about Intelligent Sensors for Sustainable Food and Drink Manufacturing.

Multiple target data-driven models to enable sustainable process manufacturing: An industrial bioprocess case study (2021)
Journal Article
FISHER, O. J., WATSON, N. J., PORCU, L., BACON, D., RIGLEY, M., & GOMES, R. L. (2021). Multiple target data-driven models to enable sustainable process manufacturing: An industrial bioprocess case study. Journal of Cleaner Production, 296, Article 126242. https://doi.org/10.1016/j.jclepro.2021.126242

Process manufacturing industries constantly strive to make their processes increasingly sustainable from an environmental and economic perspective. A manufacturing system model is a powerful tool to holistically evaluate various manufacturing configu... Read More about Multiple target data-driven models to enable sustainable process manufacturing: An industrial bioprocess case study.