All Outputs (3)

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.

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.