Musamba Banza | Environmental Science | Best Researcher Award
Postdoc fellow | UNISA | South Africa
Dr. Musamba Jean Claude Banza is a distinguished researcher and expert in machine learning and material development, with a strong commitment to advancing sustainable technologies. His research spans multiple domains, including machine learning applications in computer vision, natural language processing, and predictive analytics, alongside material science innovations aimed at addressing environmental challenges. Dr. Banza has contributed to the development of advanced materials like biodegradable packaging, polymeric hydrogels for environmental remediation, and nanocomposites for wastewater treatment. He is passionate about exploring cutting-edge technologies to solve complex real-world problems and has a track record of delivering impactful solutions. Dr. Banza holds a Ph.D. in Chemical Engineering from Vaal University of Technology and has published extensively in international journals. His work is recognized globally, having earned him several awards for excellence in research and innovation.
Profile
Education
Dr. Musamba Jean Claude Banza completed his Doctor of Philosophy (Ph.D.) in Chemical Engineering at Vaal University of Technology in 2022. His academic journey began with a Bachelor of Technology in Chemical Engineering (2014), followed by a Master of Technology in Chemical Engineering (2019), also from Vaal University of Technology. He further honed his expertise in the field with a National Diploma in Chemical Engineering (2016). Throughout his studies, Dr. Banza consistently demonstrated excellence, paving the way for his future groundbreaking work in machine learning and material development. His education provided him with a solid foundation in chemical engineering principles, materials science, and data analysis, which he has successfully applied in both academic research and industrial projects. His studies in sustainable materials and environmental science form the backbone of his innovative solutions in addressing pressing global challenges.
Experience
Dr. Musamba Jean Claude Banza has extensive professional experience in both academia and industry. He currently holds a Postdoctoral Fellowship at the University of South Africa (2025-2027) in the Department of Environmental Sciences. Prior to this, he worked as a Postdoctoral Fellow at Vaal University of Technology from 2023 to 2024, contributing to research in the Department of Chemical and Metallurgical Engineering. Between 2019 and 2022, Dr. Banza served as a part-time lecturer and supervised numerous Bachelor’s and Master’s students. His industry experience includes working at Ruashi Mining (Metorex and Jinchuan Group) from 2012 to 2014, where he optimized copper and cobalt leaching circuits and conducted mass balance analysis and water balance studies. His diverse career has allowed him to bridge the gap between research and practical applications, demonstrating his ability to apply theoretical knowledge to real-world challenges in both academia and the industrial sector.
Research Interests
Dr. Musamba Jean Claude Banza’s research focuses on the intersection of machine learning, material development, and environmental science. He has made significant strides in applying machine learning techniques, such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS), to solve complex challenges in various fields, including water treatment and environmental remediation. His work includes the development of advanced materials, such as biodegradable packaging, nanocomposites, and polymeric hydrogels designed to remove organic dyes, heavy metals, and rare earth elements from wastewater. Dr. Banza’s research also emphasizes the optimization of adsorption processes using novel materials for sustainable solutions in hydrometallurgical effluent treatment. By combining his expertise in material science with advanced machine learning algorithms, Dr. Banza is creating groundbreaking solutions that address both environmental and industrial challenges. His focus on sustainability and performance has led to practical applications that contribute to cleaner, safer environments.
Awards
Dr. Musamba Jean Claude Banza has received several prestigious awards for his exceptional contributions to research and innovation. In 2021, he was recognized as a Young Researcher by the International Academic Awards for his pioneering work in material science and machine learning. He was again honored in 2024 when he was selected as the Best Researcher by the International Academic Awards, acknowledging his significant impact on environmental remediation technologies and sustainable materials. Additionally, Dr. Banza earned a certificate from Elsevier for his article’s contribution to the United Nations Sustainable Development Goals, showcasing the global relevance of his research. These accolades underscore his dedication to addressing critical environmental issues through innovative scientific solutions. Dr. Banza’s awards demonstrate his consistent excellence in both academic and applied research, marking him as a leader in his field.
Publications
Blast Furnace Slag for SO2 Capture: Optimization and Prediction Using Response Surface Methodology and Artificial Neural Network
Authors: Kohitlhetse, I., Evans, S.K., Banza, M., Makomere, R.
Journal: Chemical Industry and Chemical Engineering Quarterly
Year: 2024
Volume: 30(4), pp. 349–357
Citations: 0
Modeling of Biomethane Production from Ultrasonic Pretreated Fruit and Vegetable Waste via Anaerobic Digestion
Authors: Matobole, K., Seodigeng, T., Banza, M., Rutto, H.
Journal: Journal of Environmental Science and Health – Part A Toxic/Hazardous Substances and Environmental Engineering
Year: 2024
Volume: 59(10), pp. 513–522
Citations: 0
Batch and Continuous Fixed Bed Adsorption of Copper (II) from Acid Mine Drainage (AMD) Using Green and Recyclable Adsorbent from Cellulose Microcrystals (CMCs)
Authors: Banza, M., Seodigeng, T., Linda, S., Owona, S., Musampa, P.
Journal: Journal of Environmental Science and Health – Part A Toxic/Hazardous Substances and Environmental Engineering
Year: 2024
Volume: 59(9), pp. 488–498
Citations: 0
Preparation, Characterization, and Application of Polymeric Ultra-Permeable Biodegradable Ferromagnetic Nanocomposite Adsorbent for Removal of Cr(VI) from Synthetic Wastewater: Kinetics, Isotherms, and Thermodynamics
Authors: Suter, E., Rutto, H., Makomere, R., Kiambi, S., Omwoyo, W.
Journal: Frontiers in Environmental Chemistry
Year: 2024
Volume: 5, Article: 1451262
Citations: 1
The Impact of Ozone Treatment on the Removal Effectiveness of Various Refractory Compounds in Wastewater from Petroleum Refineries
Authors: Khoza, N., Seodigeng, T., Banza, M., Ochieng, A.
Journal: Journal of Environmental Science and Health – Part A Toxic/Hazardous Substances and Environmental Engineering
Year: 2024
Volume: 59(4), pp. 189–199
Citations: 0
Energy Storage Through a Regenerative Hydrogen Fuel Cell in a Hybrid System of Renewable Energy for Power Generation
Authors: Nkwambe, M.S., Sutherland, T., Seodigeng, T., Banza, M.
Journal: Chemical Engineering Transactions
Year: 2024
Volume: 108, pp. 73–78
Citations: 2
Mass Transfer Modelling of Sclerocarya Birrea Kernels in Supercritical Carbon Dioxide
Authors: Reddy, T., Seodigeng, T., Banza, M., Rutto, H.
Journal: Chemical Engineering Transactions
Year: 2024
Volume: 108, pp. 25–30
Citations: 0
Application of Artificial Neural Network and Shrinking Core Model for Copper (II) and Lead (II) Leaching from Contaminated Soil Using Ethylenediaminetetraacetic Acid
Authors: Banza, M., Rutto, H., Seodigeng, T.
Journal: Soil and Sediment Contamination
Year: 2024
Volume: 33(1), pp. 43–63
Citations: 5
Comparison Study of ANFIS, ANN, and RSM and Mechanistic Modeling for Chromium(VI) Removal Using Modified Cellulose Nanocrystals–Sodium Alginate (CNC–Alg)
Authors: Banza, M., Seodigeng, T., Rutto, H.
Journal: Arabian Journal for Science and Engineering
Year: 2023
Volume: 48(12), pp. 16067–16085
Citations: 16
The Use of Artificial Neural Network (ANN) in Dry Flue Gas Desulphurization Modeling: Levenberg–Marquardt (LM) and Bayesian Regularization (BR) Algorithm Comparison
Authors: Makomere, R., Rutto, H., Koech, L., Banza, M.
Journal: Canadian Journal of Chemical Engineering
Year: 2023
Volume: 101(6), pp. 3273–3286
Citations: 15
Conclusion