Alexander Bowler
University of Leeds
Research Expertise
About
Publications
A review of in-line and on-line measurement techniques to monitor industrial mixing processes
Chemical Engineering Research and Design / Jan 01, 2020
Bowler, A. L., Bakalis, S., & Watson, N. J. (2020). A review of in-line and on-line measurement techniques to monitor industrial mixing processes. Chemical Engineering Research and Design, 153, 463–495. https://doi.org/10.1016/j.cherd.2019.10.045
Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning
Sensors / Mar 25, 2020
Bowler, A. L., Bakalis, S., & Watson, N. J. (2020). Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning. Sensors, 20(7), 1813. https://doi.org/10.3390/s20071813
A review of ultrasonic sensing and machine learning methods to monitor industrial processes
Ultrasonics / Aug 01, 2022
Bowler, A. L., Pound, M. P., & Watson, N. J. (2022). A review of ultrasonic sensing and machine learning methods to monitor industrial processes. Ultrasonics, 124, 106776. https://doi.org/10.1016/j.ultras.2022.106776
Near-infrared spectroscopy and machine learning for classification of food powders during a continuous process
Journal of Food Engineering / Mar 01, 2023
Ozturk, S., Bowler, A., Rady, A., & Watson, N. J. (2023). Near-infrared spectroscopy and machine learning for classification of food powders during a continuous process. Journal of Food Engineering, 341, 111339. https://doi.org/10.1016/j.jfoodeng.2022.111339
Intelligent Sensors for Sustainable Food and Drink Manufacturing
Frontiers in Sustainable Food Systems / Nov 05, 2021
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. https://doi.org/10.3389/fsufs.2021.642786
Optimised mode selection in electromagnetic sensors for real time, continuous and in-situ monitoring of water cut in multi-phase flow systems
Sensors and Actuators B: Chemical / Nov 01, 2019
Yuan, C., Bowler, A., Davies, J. G., Hewakandamby, B., & Dimitrakis, G. (2019). Optimised mode selection in electromagnetic sensors for real time, continuous and in-situ monitoring of water cut in multi-phase flow systems. Sensors and Actuators B: Chemical, 298, 126886. https://doi.org/10.1016/j.snb.2019.126886
Transfer learning for process monitoring using reflection-mode ultrasonic sensing
Ultrasonics / Aug 01, 2021
Bowler, A. L., & Watson, N. J. (2021). Transfer learning for process monitoring using reflection-mode ultrasonic sensing. Ultrasonics, 115, 106468. https://doi.org/10.1016/j.ultras.2021.106468
Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning
Fermentation / Mar 04, 2021
Bowler, A., Escrig, J., Pound, M., & Watson, N. (2021). Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning. Fermentation, 7(1), 34. https://doi.org/10.3390/fermentation7010034
Domain Adaptation and Federated Learning for Ultrasonic Monitoring of Beer Fermentation
Fermentation / Nov 01, 2021
Bowler, A. L., Pound, M. P., & Watson, N. J. (2021). Domain Adaptation and Federated Learning for Ultrasonic Monitoring of Beer Fermentation. Fermentation, 7(4), 253. https://doi.org/10.3390/fermentation7040253
A surrogate model for the economic evaluation of renewable hydrogen production from biomass feedstocks via supercritical water gasification
International Journal of Hydrogen Energy / Jan 01, 2024
Rodgers, S., Bowler, A., Wells, L., Lee, C. S., Hayes, M., Poulston, S., Lester, E., Meng, F., McKechnie, J., & Conradie, A. (2024). A surrogate model for the economic evaluation of renewable hydrogen production from biomass feedstocks via supercritical water gasification. International Journal of Hydrogen Energy, 49, 277–294. https://doi.org/10.1016/j.ijhydene.2023.08.016
Domain Adaptation for In-Line Allergen Classification of Agri-Food Powders Using Near-Infrared Spectroscopy
Sensors / Sep 24, 2022
Bowler, A. L., Ozturk, S., Rady, A., & Watson, N. (2022). Domain Adaptation for In-Line Allergen Classification of Agri-Food Powders Using Near-Infrared Spectroscopy. Sensors, 22(19), 7239. https://doi.org/10.3390/s22197239
Convolutional feature extraction for process monitoring using ultrasonic sensors
Computers & Chemical Engineering / Dec 01, 2021
Bowler, A., Pound, M., & Watson, N. (2021). Convolutional feature extraction for process monitoring using ultrasonic sensors. Computers & Chemical Engineering, 155, 107508. https://doi.org/10.1016/j.compchemeng.2021.107508
Probabilistic commodity price projections for unbiased techno-economic analyses
Engineering Applications of Artificial Intelligence / Jun 01, 2023
Rodgers, S., Bowler, A., Meng, F., Poulston, S., McKechnie, J., & Conradie, A. (2023). Probabilistic commodity price projections for unbiased techno-economic analyses. Engineering Applications of Artificial Intelligence, 122, 106065. https://doi.org/10.1016/j.engappai.2023.106065
Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements
Food Control / May 01, 2023
Bowler, A., Ozturk, S., di Bari, V., Glover, Z. J., & Watson, N. J. (2023). Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements. Food Control, 147, 109622. https://doi.org/10.1016/j.foodcont.2023.109622
Development of an open-source carbon footprint calculator of the UK craft brewing value chain
Journal of Cleaner Production / Jan 01, 2024
Bowler, A. L., Rodgers, S., Meng, F., McKechnie, J., Cook, D. J., & Watson, N. J. (2024). Development of an open-source carbon footprint calculator of the UK craft brewing value chain. Journal of Cleaner Production, 435, 140181. https://doi.org/10.1016/j.jclepro.2023.140181
Bayesian and ultrasonic sensor aided multi-objective optimisation for sustainable clean-in-place processes
Food and Bioproducts Processing / Sep 01, 2023
Bowler, A. L., Rodgers, S., Cook, D. J., & Watson, N. J. (2023). Bayesian and ultrasonic sensor aided multi-objective optimisation for sustainable clean-in-place processes. Food and Bioproducts Processing, 141, 23–35. https://doi.org/10.1016/j.fbp.2023.06.010
Education
Ph.D., Chemical Engineering / March, 2022
Experience
University of Leeds
Research Fellow in Artificial Intelligence for Sustainable Food / December, 2023 — Present
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