Senior Mining Engineer/Ph. D Student, Dangote Cement PLC/Federal University of Technology Akure, Nigeria
Adamolekun Lateef Bankole is a Mining Engineer with over a decade of experience at Dangote Cement Plc, Gboko Plant, Benue State, Nigeria. He holds a B.Eng. and M.Eng. in Surface Mining Engineering from the Federal University of Technology, Akure, where he is currently pursuing a PhD in the same field. Adamolekun has expertise in mine design, production scheduling, blasting optimization, and environmental safety compliance. His research focuses on applying machine learning techniques like artificial neural networks (ANN) to improve mining operations, geotechnical assessments, and predictive modeling. He has authored several publications in reputable journals and has contributed to advancements in mining cost control and operational efficiency. Adamolekun is also proficient in various technical tools, including MATLAB and survey equipment, and he holds certifications in blasting and health, safety, and environment (HSE). His work reflects a balance of practical industry experience and innovative research.
Profile:
Education
Adamolekun Lateef Bankole has a solid educational foundation in Mining Engineering, beginning with his National Diploma in Mineral Resources Engineering from the Federal Polytechnic, Ado-Ekiti, in 2006, where he graduated with Upper Credit. He advanced his studies at the Federal University of Technology, Akure (FUTA), earning a Bachelor of Engineering (B.Eng.) in Mining Engineering with Second Class Upper Division in 2011. Following this, Adamolekun pursued a Master of Engineering (M.Eng.) in Surface Mining Engineering at the same institution, completing the program in 2015 with another Second Class Upper Division. His academic journey is further elevated by his ongoing Ph.D. in Surface Mining Engineering at FUTA, which began in 2020. His advanced studies focus on developing innovative approaches in mining, and his educational background equips him with a strong mix of theoretical knowledge and practical skills in the field of mining and geotechnical engineering.
Professional Experience
Adamolekun Lateef Bankole is a seasoned Mining Engineer with over a decade of experience at Dangote Cement Plc, Gboko Plant, Benue State. Since joining the company in 2014, he has played a pivotal role in enhancing mining operations by implementing innovative haulage auditing techniques that reduced financial losses by 60%. His expertise spans mine dewatering, production scheduling, and the coordination of complex mining activities such as drilling, blasting, and overburden stripping. He is proficient in designing benches, overseeing road and ramp construction, and ensuring adherence to environmental and safety standards. Adamolekun’s leadership skills are demonstrated through his supervision of large-scale projects, including blasting ground control and crushing operations. His problem-solving capabilities extend to using queue models and operational research to optimize mining processes, making him a valuable asset to his team. His commitment to improving operational efficiency highlights his strong technical and managerial expertise in the mining industry.
Research Interests
Adamolekun Lateef Bankole’s research interests are centered on the fields of surface mining engineering, geotechnical assessments, and the application of machine learning in mining operations. He is particularly focused on optimizing mine design, drilling, and blasting techniques to improve operational efficiency and safety in the mining industry. His expertise extends to the development of artificial neural network (ANN) models and other soft computing techniques for predicting soil properties and enhancing the accuracy of engineering applications. Adamolekun is also interested in addressing haulage problems, mining cost control, and environmental compliance in mining operations. His research aims to integrate advanced data-driven methodologies with traditional mining practices to create innovative solutions that reduce financial losses, improve resource extraction efficiency, and ensure sustainable mining practices. He is committed to exploring cutting-edge technologies and enhancing the reliability of mining operations through computational modeling and optimization.
Honors and Award
Adamolekun Lateef Bankole has earned recognition for his contributions to the field of mining engineering through various honors and awards. His exemplary work at Dangote Cement Plc led to the successful implementation of innovative ROM haulage auditing techniques, significantly reducing financial losses, which garnered him accolades within the organization. In academia, his research publications in reputable journals have showcased his commitment to advancing knowledge in mining and geotechnical engineering. He is a registered Mining Engineer with the Council of Nigerian Mining Engineers and Geoscientists, reflecting his professional standing in the industry. Additionally, his active participation in professional societies, such as the Nigeria Mining and Geosciences Society, has positioned him as a respected figure among peers. While specific awards are not detailed, his ongoing research efforts and the impact of his work suggest that he is on a trajectory for further recognition in the field.
Research Skills
Adamolekun Lateef Bankole possesses a diverse array of research skills that enhance his contributions to the field of mining engineering. His expertise in machine learning techniques, including Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO-ANN), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), demonstrates his proficiency in applying advanced computational methods to solve complex engineering problems. Adamolekun’s strong background in data analysis is reflected in his research on geotechnical assessments and predictive modeling of lateritic soil strength, showcasing his ability to integrate theoretical knowledge with practical applications. His skills in production scheduling, blasting optimization, and environmental compliance further underline his comprehensive understanding of mining processes. Additionally, his familiarity with software tools like MATLAB and proficiency in documentation and report preparation support his research endeavors. Adamolekun’s commitment to innovative solutions and continuous learning positions him as a valuable contributor to the mining engineering research community.
- Title: Investigating the competency of some selected soft computing techniques for modeling of lateritic soil strength based on index properties
Authors: Lateef Bankole Adamolekun; Muyideen Alade Saliu; Abiodun Ismail Lawal; Ismail Adeniyi Okewale
Year: 2024
Journal: International Journal of Science and Research Archive
DOI: 10.30574/ijsra.2024.12.2.1199
ISSN: 2582-8185
- Title: Geotechnical Assessment of Selected Lateritic Soils in Southwest Nigeria for Road Construction and Development of Artificial Neural Network Mathematical Based Model for Prediction of the California Bearing Ratio
Authors: Lateef Bankole Adamolekun; Muyideen Alade Saliu; Abiodun Ismail Lawal; Ismail Adeniyi Okewale
Year: 2024
Journal: International Journal of Innovative Science and Research Technology (IJISRT)
DOI: 10.38124/ijisrt/ijisrt24jun753
ISSN: 2456-2165
- Title: Development of artificial neural network based mathematical models for predicting small scale quarry powder factor for efficient fragmentation coupled with uniformity index model
Authors: Taiwo Blessing Olamide; Fissha Yewuhalashet; Lateef Bankole Adamolekun; Ogunyemi Olaoluwa Bidemi; Oluwaseun Victor Famobuwa; Adediran Oluwatomisin Victoria
Year: 2023
Journal: Artificial Intelligence Review
DOI: 10.1007/s10462-023-10524-1
ISSN: 0269-2821, 1573-7462
- Title: Improvement of Asphalt Production Investment in Nigeria Through Profitability Assessment: A Case Study of Oyo State Nigeria Construction Company
Authors: Melodi Mbuyi Mata; Blessing Olamide Taiwo; Lateef Bankole Adamolekun
Year: 2022
Journal: International Journal of Engineering and Advanced Technology Studies
DOI: 10.37745/ijeats.13/vol10n2115
ISSN: 2053-5783, 2053-5791