Dr. Sunday Adedigba completed his PhD in Process Engineering at Memorial University of Newfoundland, Canada. He has received an M.Sc. in chemical Engineering at University of Science Malaysia (USM), B.Eng. in Chemical Engineering at Federal University of Technology Minna, Nigeria and Higher National Diploma in Chemical Engineering at Federal Polytechnic Bida, Nigeria.

Research interests

Accident modelling and Dynamic Risk Assessment

Publications

1. Adedigba, S.A., Khan, F., Yang, M. (2016). Dynamic safety analysis of process systems using nonlinear and non-sequential accident model. Chemical Engineering Research and Design, 111, 169-183.
2. Adedigba, S.A., Khan, F.,Yang, M. (2016). Process accident model considering dependency among contributory factors. Process Safety and Environmental Protection, 102, 633-647.
3. Adedigba, S.A., Khan, F. & Yang, M., (2017). Dynamic Failure Analysis of Process Systems Using Principal Component Analysis and Bayesian Network. Industrial & Engineering Chemistry Research, 56(8), pp.2094–2106.
4. Adedigba, S.A., Khan, F. & Yang, M., (2017). Dynamic failure analysis of process systems using neural networks. Process Safety and Environmental Protection, 111, pp.529–543.
5. Adedigba, S A., Khan, F, Yang, M. (2018). An Integrated Approach for Dynamic Economic Risk Assessment of Process Systems. Process Safety and Environment Protection,106,312-323
6. Adedigba, S.A., Oloruntobi, O. Khan, F. & Butt, S. (2018). Data-Driven Dynamic Risk Analysis of Offshore Drilling Operations. Journal of Petroleum Science and Engineering 165:444–52.
7. Oloruntobi, O., Adedigba, S., Khan, F., Chunduru, R., Butt, S.,(2018) Overpressure Prediction Using the Hydro-Rotary Specific Energy Concept, Journal of Natural Gas Science & Engineering, 55,243-253.
8. Onalo, D., Adedigba, S., Khan, F., James, L., Butt, S.,(2018) Data driven model for sonic well log prediction, Journal of Petroleum Science and Engineering,170,1022-1037
9. Onalo, D., Oloruntobi, O., Adedigba, S., Khan, F., James, L., Butt, S (2018). Static Young's modulus model prediction for formation evaluation, Journal of Petroleum Science and Engineering 171, pp.394–402.
10. D. Onalo, O. Oloruntobi, S. Adedigba, F. Khan, L. James, and S. Butt., Dynamic data driven sonic well log model for formation evaluation, J. Pet. Sci. Eng., vol. 175, pp. 1049–1062, Apr. 2019.

Contact Info

aas140@mun.ca