Summary
Miao Guo is currently a Senior Lecturer at Department of Engineering, King's College London.
Miao Guo currently holds a membership of the EPSRC Early Career Forum in Manufacturing Research, and is a honorary lecturer at Imperial College London.
Prior to moving to King's College London in Nov 2019, Miao Guo was an EPSRC Research Fellow hosted at Department of Chemical Engineering, Imperial College London.
Miao's research experiences in Life Sciences and Chemical Engineering has enabled her to consolidate cross-disciplinary strengths at the interface of Engineering and Natural Sciences. Her lab focuses on interdisciplinary research and experimentally and computationally understands, develops and optimises bioprocesses and waste-to-resource technologies.
In collaboration with international academics and industrial pioneers, Miao has been leading the frontier research to develop new modelling methods and tools as well as new applications of PSE research e.g. waste-to-protein, biomanufacturing, digitalised toxicology. Her research interests include -
- Optimisation and mathematical programming
- Biorenewable process simulation and optimisation
- Hybrid modelling methods
- Biotechnology for wastewater and resource recovery
- Microbial protein and biomanufacturing
- Life cycle sustainability assessment
To uncover our latest research, please visit our group website and our video stream on YouTube
Publications
Journals
Guo M, Wu C, Chapman S, et al., 2023, Advances in biorenewables-resource-waste systems and modelling, Carbon Capture Science & Technology, Vol:9
Sun X, Durkin A, Guo M, 2023, Life cycle optimisation tool development for process systems and centralised supply chain design, Rsc Sustainability, Vol:1, Pages:2224-2240
Uma VS, Usmani Z, Sharma M, et al., 2023, Valorisation of algal biomass to value-added metabolites: emerging trends and opportunities, Phytochemistry Reviews, Vol:22, ISSN:1568-7767, Pages:1015-1040
Liu Y, Zuo Z, Li H, et al., 2023, <i>In</i><i>-situ</i> advanced oxidation of sediment iron for sulfide control in sewers, Water Research, Vol:240, ISSN:0043-1354
Kalian AD, Benfenati E, Osborne OJ, et al., 2023, Exploring Dimensionality Reduction Techniques for Deep Learning Driven QSAR Models of Mutagenicity, Toxics, Vol:11