Philip Keymer
Impact in
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- Algal biology and biofuel production
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- Water Quality Monitoring and Analysis
Papers in
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- Fault Detection and Control Systems 2
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- Neural Networks and Applications 1
- Data Stream Mining Techniques 1
- Machine Learning and Data Classification 1
- Co-authors
- Paul Lant (4 shared papers)Steven Pratt (4 shared papers)Ian Ruffell (1 shared paper)Andrew Ward (2 shared papers)Rachel Cardell‐Oliver (2 shared papers)Tim French (2 shared papers)Christian Kazadi Mbamba (2 shared papers)Damien J. Batstone (2 shared papers)
- Journals
- Water Research (1 paper)Biotechnology and Bioengineering (1 paper)IEEE Access (1 paper)Computers & Chemical Engineering (1 paper)Bioresource Technology (1 paper)
- Partner nations
- AustraliaDenmarkUnited Kingdom
In The Last Decade
Philip Keymer
7 papers receiving 277 citations
Philip Keymer's Hit Papers
Peers
Comparison fields: 5 of 55
- Renewable Energy, Sustainability and the Environment 104
- Industrial and Manufacturing Engineering 48
- Water Science and Technology 74
- Building and Construction 54
- Pollution 30
Countries citing papers authored by Philip Keymer
This map shows the geographic impact of Philip Keymer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Philip Keymer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philip Keymer more than expected).
Fields of papers citing papers by Philip Keymer
This network shows the impact of papers produced by Philip Keymer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Philip Keymer. The network helps show where Philip Keymer may publish in the future.
Co-authors
The 10 scholars most cited alongside Philip Keymer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 120 | |
| 2 | Deep learning in wastewater treatment: a critical review Hit paper breakdown → | 2023 | 114 |
| 3 | 2022 | 21 | |
| 4 | 2013 | 11 | |
| 5 | The Potential for generating algae derived biofuels using coal seam gas water as the growth media | 2009 | 6 |
| 6 | 2013 | 5 | |
| 7 | 2025 | 5 |
About Philip Keymer
Philip Keymer is a scholar working on Control and Systems Engineering, Artificial Intelligence, Environmental Engineering, Electrical and Electronic Engineering and Renewable Energy, Sustainability and the Environment, having authored 7 papers that have together received 282 indexed citations. Recurring topics across this work include Algal biology and biofuel production (2 papers), Fault Detection and Control Systems (2 papers), Fuel Cells and Related Materials (1 paper), Neural Networks and Applications (1 paper), Marine and coastal ecosystems (1 paper), Data Stream Mining Techniques (1 paper), Machine Learning and Data Classification (1 paper) and Ocean Acidification Effects and Responses (1 paper). The work is most often cited by research in Renewable Energy, Sustainability and the Environment (104 citations), Industrial and Manufacturing Engineering (48 citations), Water Science and Technology (74 citations), Building and Construction (54 citations) and Pollution (30 citations). Philip Keymer has collaborated with scholars based in Australia, Denmark and United Kingdom. Frequent co-authors include Paul Lant, Steven Pratt, Ian Ruffell, Andrew Ward, Rachel Cardell‐Oliver, Tim French, Christian Kazadi Mbamba, Damien J. Batstone, Jason Dwyer and Jürg Keller. Their work appears in journals such as Water Research, Biotechnology and Bioengineering, IEEE Access, Computers & Chemical Engineering and Bioresource Technology.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.