Kwame Sarkodie
Kwame Nkrumah University of Science and Technology
- Senior Lecturer, Kwame Nkrumah University of Science and Technology, since 2024per ORCID
Publishing since 2014, latest 2026
Dr. Kwame Sarkodie is a dynamic and accomplished researcher in the field of flow assurance and multiphase flow metering, with a strong commitment to innovation at the intersection of energy systems, sensor technology, and artificial intelligence. He currently serves as a Senior Lecturer in the Department of Petroleum Engineering at Kwame Nkrumah University of Science and Technology (KNUST), Ghana. With a solid academic foundation, Dr. Sarkodie holds a PhD in Chemical Process… More on ORCID
- Mechanical Engineering
- Ocean Engineering
- Biomedical Engineering
- Mechanics of Materials
- Analytical Chemistry
- Civil and Structural Engineering
In their own words
- Flow Assurance
- Chemical Process Engineering
- Machine Learning
- Publications
- 422014–2026
- Citations
- 422to those papers
- h-index
- 10i10-index 13
- Field-weighted impact
- 1.101.00 is the world average
- World top 10%
- 6%2 of 31 scored papers
- Open access
- 55%17 of the 31 whose access is recorded
5 other works — preprints, datasets and the like — are listed but not counted.
Google Scholar shows 548 citations, h-index 11 and i10-index 15 (523 citations and h-index 10 since 2021), as of October 10, 2026. It also counts theses, books and web pages, so it runs higher.
By year
Papers, and the citations to them
Publications, left axisCitations to that year’s papers, right axis2026 still in progress
Positions
- Senior Lecturer, Petroleum EngineeringKwame Nkrumah University of Science and Technologysince 2024
- Lecturer, Petroleum EngineeringKwame Nkrumah University of Science and Technology2016–2024
- Assistant Lecturer of petroleum engineering, Petroleum engineeringKwame Nkrumah University of Science and Technology2012–2016
Education
- PhD Chemical Process and Energy engineering, Chemical and Energy EngineeringLondon South Bank University2016–2020
Publications
42 in all
- An interpretable model for real-time prediction of bottomhole flowing pressure in deviated wells: a hybrid GA-ANN approach
2026Journal of Engineering and Applied ScienceJournalOpen access1 citation10.1186/s44147-026-00879-x
- Transformer-based estimation of oil formation volume factor: a data-driven gray-box correlation with symbolic regression and transfer learning