i-manager Publications

Volume 15 Issue 3 July - September 2025

Research Paper

Integration of Autonomous Robotic Material Handling for Enhanced Safety in Zimbabwean Platinum Mining: A Case Study of Mimosa Mine

Tafadzwa Mukodi* , Rindai P. Mahoso, Godfrey Tigere*

*-*** Harare Institute of Technology, Harare, Zimbabwe.

Mukodi, T., Mahoso, R. P., and Tigere, G. (2025). Integration of Autonomous Robotic Material Handling for Enhanced Safety in Zimbabwean Platinum Mining: A Case Study of Mimosa Mine. i-manager’s Journal on Mechanical Engineering, 15(3), 1-19.

Abstract

Despite significant investments in mechanization and infrastructure, platinum mining operations in Zimbabwe, especially at Mimosa Mine, continue to experience major safety issues associated with manual material handling. Frequent incidents such as winch rope entanglement, equipment collisions, and rockfalls during stope cleaning have resulted in fatalities, emphasizing the pressing need for enhanced safety interventions. While autonomous solutions like robotic loaders and sensor-based geofencing have been shown to reduce accidents by up to 50% in international contexts, these technologies have yet to be widely implemented in Zimbabwe's underground platinum mining sector. This research paper develops an autonomous robotic material handling system tailored for the underground environment at Mimosa Mine, aiming to address critical safety risks and improve operational efficiency in high-risk activities. By focusing on automation of hazardous tasks and aligning with sustainability objectives, the proposed system provides a scalable approach to reducing human exposure to dangerous conditions and enhancing productivity. The study's outcomes highlight the transformative potential of autonomous systems for mining safety in Zimbabwe, while also recognizing ongoing operational challenges, such as power instability and fragmented supply chains.

References

[1]. Annals of Global Health. (2022a). Addressing safety in underground mining: Challenges and solutions. Annals of Global Health, 88(1), 55–66.

[2]. Annals of Global Health. (2022b). Mining Safety and Occupational Risks in Developing Countries. Annals of Global Health.

[3]. BHP. (2024). Autonomous Drilling Operations: Lessons and Best Practices. Mining Tech Review.

[4]. Chitangu, T. (2023). Challenges and Opportunities in Zimbabwe's Platinum Mining Sector. Mining Journal of Zimbabwe.

[5]. Chitangu, T. (2024). Supply Chain Challenges Impacting Automation in Zimbabwe's Mining Sector. Mining Industry Review.

[6]. Chronicle. (2025). Conveyor Belt Fire Traps 178 Miners Underground at Mimosa Mine. Zimbabwe Daily Chronicle.

[7]. Deloitte. (2024a). AI-Driven Navigation and Sensor- Based Geofencing to Prevent Mining Accidents. Deloitte Mining Innovations.

[8]. Deloitte. (2024b). Autonomous Mining Technologies: Global Trends and Safety Impact. Deloitte Mining Insights.

[9]. Kalpakjian, S., & Schmid, S. (2018). Manufacturing Engineering and Technology (7th ed.). Pearson.

[10]. Lidar Systems Insights. (2025). Use of Lidar for 3D Mapping and Geofencing in Mining. Mining Technology Journal.

[11]. Lööw, P., Kangas, J., Wallenius, J., & Andersson, J. (2019). Autonomous systems and safety improvements in mining. International Journal of Mining Science, 15(2), 77–85.

[12]. LSE Consulting. (2025a). Cybersecurity and System Resilience in Autonomous Mining Operations. Mining Security Journal.

[13]. LSE Consulting. (2025b). Real-Time Environmental Monitoring and AI-Driven Predictive Analytics in Mining. LSE Mining Reports.

[14]. Mimosa Mine. (2023). Safety Performance Report 2023. Internal Report.

[15]. Mining Zimbabwe. (2024a). Annual Report on Mining Industry Development. Mining Zimbabwe Publications.

[16]. Mining Zimbabwe. (2024b). Power Instability and its Effect on Mining Automation Adoption. Mining Zimbabwe Publications.

[17]. Nexgen SIMS Project. (2024). Integration of Battery- Electric Loaders with 5G and AI In Mining. Agnico Eagle Reports.

[18]. Paswavaviri, T. (2024). Accident rate analysis in Zimbabwe's mining industry. Zimbabwe Safety Review, 10(1), 24–35.

[19]. PubMed Central. (2022a). Health and safety hazards in underground mining environments. Journal of Occupational Health, 64(3), 243–255.

[20]. PubMed Central. (2022b). Monitoring Real-Time Risks with Automated Mining Systems. Occupational Health and Safety Journal.

[21]. PubMed Central. (2023). Impact of Long Work Hours and PPE Insufficiency on Mining Injuries. Journal of Occupational Safety.

[22]. Reed, S. (1991). The application of Taguchi methods to human-machine interface designs. Quality Engineering, 3(4), 461–471.

[23]. Rio Tinto. (2023). Autohaul Autonomous Haulage System: Implementation and Outcomes. Mining Journal Australia.

[24]. SAMRASS Database. (2022). Statistics on Winch Rope Entanglement and Related Accidents. Southern African Mining Safety.

[25]. SAMRASS Database. (2023). Mining Accident Records for Zimbabwe. Southern African Mining Safety Repository.

[26]. Takeshita, K., & Hosokawa, M. (2007). Robust design of sensor systems using Taguchi methods. Sensors and Actuators A: Physical, 135(1), 214–220.

[27]. Trade.gov. (2025). Advances in Autonomous Mining: Technology and Safety Benefits. U.S. Department of Commerce.

[28]. Trade.gov. (2025). Environmental Hazard Detection and Automated Response in Mining. U.S. Trade Reports.

[29]. YuGong Framework. (2025). Digital Twin Technology for Mining Optimization. Global Mining Innovations.

[30]. Zimbabwe Colliery Supplier Support Program. (2023). Capacity Building Efforts in Mining Automation. Zimbabwe Mining Reports.

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Research Paper

Design and Motion Analysis of 14 Links Mechanism for One Degree of Freedom Robotic Legs and Prosthetics: Redefining Mobility

Azaka Onyemazuwa Andrew* , Okeke Chisom Agatha, Ezenwafor Izuchukwu Sixtus*, Eze Godwin Chukwuemerie**, Odeh Calistus Princewill*, Osazuwa Orobosa Kingsley****

*-****** Department of Mechanical Engineering, Nnamdi Azikiwe University, Awka, Anambra, Nigeria.

Andrew, A. O., Agatha, O. C., Sixtus, E. I., Chukwuemerie, E. G., Princewill, O. C., and Kingsley, O. O. (2025). Design and Motion Analysis of 14 Links Mechanism for One Degree of Freedom Robotic Legs and Prosthetics: Redefining Mobility. i-manager’s Journal on Mechanical Engineering, 15(3), 20-32.

Abstract

This study presents the design and motion analysis of a 14-link, single degree of freedom (DOF) mechanism for robotic legs and prosthetics. Through systematic number synthesis based on link mobility equations, 23 valid link combinations were generated that satisfy the condition of one DOF. Among these, the configuration consisting of 8 binary, 4 ternary, and 2 pentagonal links was selected because it is the only combination that incorporates pentagonal links, enabling a structurally rich, multi-loop architecture capable of coordinating complex hip, knee and ankle motion from a single actuator. The presence of two pentagonal links, each with five connection points, allows for strategic placement of joints and transmission elements, facilitating synchronized flexion and extension that closely mimic human gait kinematics. A 3D CAD model was developed in SOLIDWORKS and subjected to motion simulation, yielding a functional range of motion with 120° flexion and 37.6° extension. The mechanism achieves smooth articulation with a maximum angular velocity of 33.42 deg/s and acceleration of 33.04 deg/s², driven by a constant velocity rotor (300 deg/s). Simulink-based control system analysis confirmed stability, with an open-loop response exhibiting 10% overshoot and a 2-second settling time. A transfer function was derived from simulated input-output data, and system stability was further verified through pole-zero mapping, frequency response, and root locus analysis. By leveraging topological complexity rather than actuation redundancy, this work demonstrates that a single DOF mechanism can achieve biomimetic motion through intelligent link arrangement. The selected 14-link configuration provides a foundation for future integration of force analysis, lightweight materials, and experimental validation, offering a promising pathway toward adaptive, energy- efficient robotic leg systems.

References

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[4]. Mikolajczyk, T., Mikołajewska, E., Al-Shuka, H. F., Malinowski, T., Kłodowski, A., Pimenov, D. Y., & Macko, M. (2022). Recent advances in bipedal walking robots: Review of gait, drive, sensors and control systems. Sensors, 22(12), 4440.

[5]. Park, K., Ahn, H. J., Lee, K. H., & Lee, C. H. (2020). Development and performance verification of a motorized prosthetic leg for stair walking. Applied Bionics and Biomechanics, 2020(1), 8872362.

[6]. Price, M. A., Beckerle, P., & Sup, F. C. (2019). Design optimization in lower limb prostheses: A review. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(8), 1574-1588.

[7]. Saharan, L. K. (2017). iGrab Hand Orthosis: Design and Development Using Twisted and Coiled Polymer Muscles (Doctoral dissertation, The University of Texas at Dallas).

[8]. Silva, A. B., Murcia, M., Mohseni, O., Takahashi, R., Forner-Cordero, A., Seyfarth, A., & Sharbafi, M. A. (2024). Design of low-cost modular bio-inspired electric–pneumatic actuator (EPA)-driven legged robots. Biomimetics, 9(3), 164.

[9]. Su, H. J., Zhou, L., & Zhang, Y. (2013). Mobility analysis and type synthesis with screw theory: From rigid body linkages to compliant mechanisms. In Advances in Mechanisms, Robotics and Design Education and Research (pp. 67-81). Springer International Publishing.

[10]. Sun, Y., Zong, C., Pancheri, F., Chen, T., & Lueth, T. C. (2023). Design of topology optimized compliant legs for bio-inspired quadruped robots. Scientific Reports, 13(1), 4875.

[11]. Xavier, M. S., Tawk, C. D., Fleming, A., Zolfagharian, A., Pinskier, J., Howard, D., & Bodaghi, M. (2022). Soft pneumatic actuators: A review of design, fabrication, modeling, sensing, control and applications. IEEE Access, 10, 59442-59485.

[12]. Yang, Y., Semini, C., Tsagarakis, N. G., Guglielmino, E., & Caldwell, D. G. (2009). Leg mechanisms for hydraulically actuated robots. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 4669-4675). IEEE.

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Research Paper

Heat Transfer Enhancement in Heat Exchangers with Thermal Boundary Layer: A Numerical Study

Rajesh Davala* , V Ramachandra Raju**

* Department of Mechanical Engineering, Jawaharlal Nehru Technological University-GV College of Engineering Vizianagaram, Andhra Pradesh, India.

** Department of Mechanical Engineering, University College of Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.

Davala, R., and Raju, V. R. (2025). Heat Transfer Enhancement in Heat Exchangers with Thermal Boundary Layer: A Numerical Study. i-manager’s Journal on Mechanical Engineering, 15(3), 33-49.

Abstract

Enhancing the thermal performance of heat exchangers while minimizing flow resistance is critical for efficient energy systems. This study focuses on the effect of varying blade counts (2, 4, 6, and 8 blades) and Reynolds numbers (6000, 8000, 10,000, 12,000, and 14,000) on the thermal performance factor (TPF), friction factor, and heat transfer coefficient in thermal boundary layer heat exchangers. Numerical analyses were conducted to generate comprehensive datasets capturing the influence of blade-induced swirl and turbulence intensification across the specified Reynolds number range. An Artificial Neural Network (ANN) was developed and trained using these datasets to predict thermal and hydraulic performance with high accuracy. The model was used to optimize design parameters, identifying configurations that maximize heat transfer enhancement while controlling frictional penalties. Results indicate that increasing blade count significantly improves the heat transfer coefficient and TPF, especially at higher Reynolds numbers, but also leads to a corresponding increase in friction factor. The ANN demonstrated excellent predictive capability, offering a reliable tool for design optimization and performance prediction in thermal boundary layer heat exchangers with high Reynolds numbers and high blade counts. This work provides valuable insights into the interplay between blade geometry and flow conditions and demonstrates the effectiveness of AI-assisted modeling for the next generation of compact, high-performance heat exchangers.

References

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[2]. Bai, L., Sun, J., Zhang, P., & Khan, Z. A. (2021). Friction behavior of a textured surface against several materials under dry and lubricated conditions. Materials, 14(18), 5228.

[3]. Balla, H. H. (2017). Enhancement of heat transfer in six-start spirally corrugated tubes. Case Studies in Thermal Engineering, 9, 79-89.

[4]. Dizaji, H. S., Jafarmadar, S., & Mobadersani, F. (2015). Experimental studies on heat transfer and pressure drop characteristics for new arrangements of corrugated tubes in a double pipe heat exchanger. International Journal of Thermal Sciences, 96, 211-220.

[5]. Gorman, J. M., Krautbauer, K. R., & Sparrow, E. M. (2016). Thermal and fluid flow first-principles numerical design of an enhanced double pipe heat exchanger. Applied Thermal Engineering, 107, 194-206.

[6]. Han, H. Z., Li, B. X., Wu, H., & Shao, W. (2015). Multi-objective shape optimization of double pipe heat exchanger with inner corrugated tube using RSM method. International Journal of Thermal Sciences, 90, 173-186.

[7]. Hashemian, M., Jafarmadar, S., Nasiri, J., & Dizaji, H. S. (2017). Enhancement of heat transfer rate with structural modification of double pipe heat exchanger by changing cylindrical form of tubes into conical form. Applied Thermal Engineering, 118, 408-417.

[8]. Hübner, S., Eck, M., Stiller, C., & Seitz, M. (2016). Techno-economic heat transfer optimization of large scale latent heat energy storage systems in solar thermal power plants. Applied Thermal Engineering, 98, 483-491.

[9]. Jin, Z. J., Chen, F. Q., Gao, Z. X., Gao, X. F., & Qian, J. Y. (2017). Effects of pitch and corrugation depth on heat transfer characteristics in six-start spirally corrugated tube. International Journal of Heat and Mass Transfer, 108, 1011-1025.

[10]. Jin, Z. J., Liu, B. Z., Chen, F. Q., Gao, Z. X., Gao, X. F., & Qian, J. Y. (2016). CFD analysis on flow resistance characteristics of six-start spirally corrugated tube. International Journal of Heat and Mass Transfer, 103, 1198-1207.

[11]. Li, C., Wang, Z., Li, B., Peng, Z. R., & Fu, Q. (2019). Investigating the relationship between air pollution variation and urban form. Building and Environment, 147, 559-568.

[12]. Mousavi, S. V., Sheikholeslami, M., & Gerdroodbary,M. B. (2016). The Influence of magnetic field on heat transfer of magnetic nanofluid in a sinusoidal double pipe heat exchanger. Chemical Engineering Research and Design, 113, 112-124.

[13]. Peng, H., Zhang, J., Zhou, J., Shi, G., Wang, J., & Cai, X. (2023). Delta-type serial shunt soft normally-open points with wide power flow regulation range in distributed network. IEEE Transactions on Industrial Electronics, 71(7), 6501-6511.

[14]. Pourahmad, S., & Pesteei, S. M. (2016). Effectiveness-NTU analyses in a double tube heat exchanger equipped with wavy strip considering various angles. Energy Conversion and Management, 123, 462- 469.

[15]. Qi, C., Wan, Y. L., Li, C. Y., Han, D. T., & Rao, Z. H. (2017). Experimental and numerical research on the flow and heat transfer characteristics of TiO2-water nanofluids in a corrugated tube. International Journal of Heat and Mass Transfer, 115, 1072-1084.

[16]. Rishi, A. M., Kandlikar, S. G., & Gupta, A. (2019). Improved wettability of graphene nanoplatelets ( GNP)/ copper porous coatings for dramatic improvements in pool boiling heat transfer. International Journal of Heat and Mass Transfer, 132, 462-472.

[17]. Shirvan, K. M., Mamourian, M., Mirzakhanlari, S., & Ellahi, R. (2017). Numerical investigation of heat exchanger effectiveness in a double pipe heat exchanger filled with nanofluid: A sensitivity analysis by response surface methodology. Powder Technology, 313, 99-111.

[18]. Sun, M., & Zeng, M. (2018). Investigation on turbulent flow and heat transfer characteristics and technical economy of corrugated tube. Applied Thermal Engineering, 129, 1-11.

[19]. Verma, T. N., Nashine, P., Singh, D. V., Singh, T. S., & Panwar, D. (2017). ANN: Prediction of an experimental heat transfer analysis of concentric tube heat exchanger with corrugated inner tubes. Applied Thermal Engineering, 120, 219-227.

[20]. Wang, M., Liu, Y., Zhang, H., Wu, Y., & Pan, L. (2022). Thermal conductivities of Ti3C2Tx MXenes and their interfacial thermal performance in MXene/epoxy composites–a molecular dynamics simulation. International Journal of Heat and Mass Transfer, 194, 123027.

[21]. Wang, W., Zhang, Y., Li, B., & Li, Y. (2018). Numerical investigation of tube-side fully developed turbulent flow and heat transfer in outward corrugated tubes. International Journal of Heat and Mass Transfer, 116, 115-126.

[22]. Wang, W., Zhang, Y., Li, B., Han, H., & Gao, X. (2017). Influence of geometrical parameters on turbulent flow and heat transfer characteristics in outward helically corrugated tubes. Energy Conversion and Management, 136, 294-306.

[23]. Zhang, D., Tao, H., Xu, Y., & Sun, Z. (2018). Numerical investigation on flow and heat transfer characteristics of corrugated tubes with non-uniform corrugation in turbulent flow. Chinese Journal of Chemical Engineering, 26(3), 437-444.

[24]. Zhao, C. Y., Yao, Z. L., Qi, D., Ji, W. T., Li, A. G., & Tao, W. Q. (2022). Numerical investigation of tube bundle arrangement effect on falling film fluid flow and heat transfer. Applied Thermal Engineering, 201, 117828.

[25]. Zhou, Y., Xu, L., Xi, L., Ran, H., Gao, J., & Li, Y. (2024). Numerical analysis of enhanced heat transfer and nanofluid flow mechanisms in fan groove and pyramid truss microchannels. International Journal of Heat and Fluid Flow, 109, 109559.

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Review Paper

Advances in Real-Time Data-Driven Scheduling and Optimisation in Dairy Manufacturing: Insights from Smart Yoghurt Production at Kefalos Cheese Pvt Ltd in Zimbabwe

Panashe S. Marivadzi*

Harare Institute of Technology, Harare, Zimbabwe.

Marivadzi, P. S. (2025). Advances in Real-Time Data-Driven Scheduling and Optimisation in Dairy Manufacturing: Insights from Smart Yoghurt Production at Kefalos Cheese Pvt Ltd in Zimbabwe. i-manager’s Journal on Mechanical Engineering, 15(3), 50-55.

Abstract

This review paper synthesizes recent research and practical developments in real-time data-driven scheduling and optimisation within the dairy manufacturing sector, with a particular focus on smart yoghurt production processes. Emphasis is placed on critical operational parameters such as machine utilisation, changeover times, demand variability, and batch size management and their influence on key production performance indicators. The integration of Industry 4.0 technologies, including IoT sensor networks, OPC UA data acquisition protocols, and advanced simulation- optimisation frameworks built in MATLAB/Simulink, is examined as enabling pillars of adaptive scheduling. Practical case studies from dairy manufacturers worldwide share quantified improvements in production efficiency and schedule robustness. Attention is given to challenges and opportunities for implementing such data-driven scheduling techniques in emerging economies, highlighting the case of Kefalos Cheese Pvt Ltd in Zimbabwe. Gaps for future research and technology adoption are outlined to guide continued advancements in smart dairy manufacturing systems.

References

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[2]. Amorim, P., Belo-Filho, M. A. F., Toledo, F. M. B. D., Almeder, C., & Almada-Lobo, B. (2013). Lot sizing versus batching in the production and distribution planning of perishable goods. International Journal of Production Economics, 146(1), 208-218.

[3]. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787- 2805.

[4]. Baker, K. (1983). Sequencing and scheduling: An introduction to the mathematics of the job-shop. Informs, 13 (3), 94-96.

[5]. Buzacott, J. A., & Shanthikumar, J. G. (2016). Stochastic Models of Manufacturing Systems. Prentice Hall.

[6]. Da Xu, L., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on Industrial Informatics, 10(4), 2233-2243.

[7]. Elbasheer, M., Longo, F., Mirabelli, G., & Solina, V. (2024). Flexible symbiosis for simulation optimization in production scheduling: A design strategy for adaptive decision support in industry 5.0. Journal of Manufacturing and Materials Processing, 8(6), 275.

[8]. Fu, L., Pan, R., Gu, T., Cheng, X., & Zhang, H. (2024). IoT-enabled supply chain management from a customer perspective: Challenges and opportunities. IEEE Internet of Things Journal, 12 (2), 1579 - 1589.

[9]. Jain, A. S., & Meeran, S. (1999). Deterministic job-shop scheduling: Past, present and future. European Journal of Operational Research, 113(2), 390-434.

[10]. Jouzdani, J., Fathian, M., Makui, A., & Heydari, M. (2020). Robust design and planning for a multi-mode multi-product supply network: A dairy industry case study. Operational Research, 20(3), 1811-1840.

[11]. Kamble, S., Gunasekaran, A., & Dhone, N. C. (2020). Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies. International Journal of Production Research, 58(5), 1319-1337.

[12]. Kletti, J. (Ed.). (2007). Manufacturing Execution Systems-MES. Springer Berlin Heidelberg.

[13]. Kumar, A., Mangla, S. K., & Kumar, P. (2025). A decision framework for supplier selection and order allocation for environmentally-sustainable perishable food supply chains. Annals of Operations Research, 346(2), 1153-1185.

[14]. Kusiak, A. (2017). Smart manufacturing must embrace big data. Nature, 544(7648), 23-25.

[15]. Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber- physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23.

[16]. O'donovan, P., Leahy, K., Bruton, K., & O'Sullivan, D. T. (2015). Big data in manufacturing: A systematic mapping study. Journal of Big Data, 2(1), 20.

[17]. Pinedo, M. L. (2016). Design and implementation of scheduling systems: More advanced concepts. In Scheduling: Theory, Algorithms, and Systems (pp. 485-508). Springer International Publishing.

[18]. Pinto, A. R. F., & Nagano, M. S. (2019). An approach for the solution to order batching and sequencing in picking systems. Production Engineering, 13(3), 325-341.

[19]. Ramadan, M., Salah, B., Othman, M., & Ayubali, A. A. (2020). Industry 4.0-based real-time scheduling and dispatching in lean manufacturing systems. Sustainability, 12(6), 2272.

[20]. Rossit, D. A., Tohmé, F., & Frutos, M. (2019). A data- driven scheduling approach to smart manufacturing. Journal of Industrial Information Integration, 15, 69-79.

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Review Paper

Artificial Intelligence in the Energy Sector: Enhancing Boiler Performance with Advanced Thermal Spray Coatings

Harshit Garg* , Keshav Gulati, Divyanshu Mittal*

*-*** Department of Computer Science and Engineering, CGC College of Engineering, Punjab, India.

Garg, H., Gulati, K., and Mittal, D. (2025). Artificial Intelligence in the Energy Sector: Enhancing Boiler Performance with Advanced Thermal Spray Coatings. i-manager’s Journal on Mechanical Engineering, 15(3), 56-61.

Abstract

The Energy Sector is experiencing a critical change with the integration of AI and progressed materials. This paper investigates the part of AI in upgrading the execution of evaporator businesses, especially in thermal power plants. Different thermal spray coatings utilized in evaporator applications, such as NiCrAlY and CoNiCrAlY, are examined for their benefits in enhancing erosion resistance, wear resistance, and overall effectiveness. The potential of AI in optimizing kettle operations, foreseeing support needs, and reducing energy utilization is also analyzed. By integrating AI with advanced thermal spray coatings in the energy sector, we can achieve higher efficiency, lower emissions, and greater sustainability.

References

[1]. Aruna, S. T., & Mukasyan, A. S. (2008). Combustion synthesis and nanomaterials. Current Opinion in Solid State and Materials Science, 12(3-4), 44-50.

[2]. Bello, S., Wada, I., Ige, O., Chianumba, E., & Adebayo, S. (2024). AI-driven predictive maintenance and optimization of renewable energy systems for enhanced operational efficiency and longevity. International Journal of Science and Research Archive, 13(1), 2823-2837.

[3]. Hossain, E., Khan, I., Un-Noor, F., Sikander, S. S., & Sunny, M. S. H. (2019). Application of big data and machine learning in smart grid, and associated security concerns: A review. IEEE Access, 7, 13960-13988.

[4]. Kumar, S., Handa, A., Chawla, V., Grover, N. K., & Kumar, R. (2021). Performance of thermal-sprayed coatings to combat hot corrosion of coal-fired boiler tube and effect of process parameters and post-coating heat treatment on coating performance: A review. Surface Engineering, 37(7), 833-860.

[5]. Mirzaee, M., Mohebbi, T., & Rezakhani, D. (2023). A comprehensive review of the corrosion and erosion resistant coating on the fireside in power plant boilers. Farayandno, 18(82), 73-93.

[6]. Ukoba, K., Olatunji, K. O., Adeoye, E., Jen, T. C., & Madyira, D. M. (2024). Optimizing renewable energy systems through artificial intelligence: Review and future prospects. Energy & Environment, 35(7), 3833-3879.

[7]. Wijewardane, S. (2015). Thermal spray coatings in renewable energy applications. In Future Development of Thermal Spray Coatings (pp. 241-257). Woodhead Publishing.

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i-manager's Journal on Mechanical Engineering (JME)

Current Issue Vol. 15 Issue 3

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Volume 15 Issue 3 July - September 2025

Integration of Autonomous Robotic Material Handling for Enhanced Safety in Zimbabwean Platinum Mining: A Case Study of Mimosa Mine

Tafadzwa Mukodi* , Rindai P. Mahoso**, Godfrey Tigere***

*-*** Harare Institute of Technology, Harare, Zimbabwe.

Mukodi, T., Mahoso, R. P., and Tigere, G. (2025). Integration of Autonomous Robotic Material Handling for Enhanced Safety in Zimbabwean Platinum Mining: A Case Study of Mimosa Mine. i-manager’s Journal on Mechanical Engineering, 15(3), 1-19.

Abstract

References

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Design and Motion Analysis of 14 Links Mechanism for One Degree of Freedom Robotic Legs and Prosthetics: Redefining Mobility

Azaka Onyemazuwa Andrew* , Okeke Chisom Agatha**, Ezenwafor Izuchukwu Sixtus***, Eze Godwin Chukwuemerie****, Odeh Calistus Princewill*****, Osazuwa Orobosa Kingsley******

*-****** Department of Mechanical Engineering, Nnamdi Azikiwe University, Awka, Anambra, Nigeria.

Abstract

References

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Heat Transfer Enhancement in Heat Exchangers with Thermal Boundary Layer: A Numerical Study

Rajesh Davala* , V Ramachandra Raju**

* Department of Mechanical Engineering, Jawaharlal Nehru Technological University-GV College of Engineering Vizianagaram, Andhra Pradesh, India. ** Department of Mechanical Engineering, University College of Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.

Davala, R., and Raju, V. R. (2025). Heat Transfer Enhancement in Heat Exchangers with Thermal Boundary Layer: A Numerical Study. i-manager’s Journal on Mechanical Engineering, 15(3), 33-49.

Abstract

References

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Advances in Real-Time Data-Driven Scheduling and Optimisation in Dairy Manufacturing: Insights from Smart Yoghurt Production at Kefalos Cheese Pvt Ltd in Zimbabwe

Panashe S. Marivadzi*

Harare Institute of Technology, Harare, Zimbabwe.

Marivadzi, P. S. (2025). Advances in Real-Time Data-Driven Scheduling and Optimisation in Dairy Manufacturing: Insights from Smart Yoghurt Production at Kefalos Cheese Pvt Ltd in Zimbabwe. i-manager’s Journal on Mechanical Engineering, 15(3), 50-55.

Abstract

References

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Artificial Intelligence in the Energy Sector: Enhancing Boiler Performance with Advanced Thermal Spray Coatings

Harshit Garg* , Keshav Gulati**, Divyanshu Mittal***

*-*** Department of Computer Science and Engineering, CGC College of Engineering, Punjab, India.

Garg, H., Gulati, K., and Mittal, D. (2025). Artificial Intelligence in the Energy Sector: Enhancing Boiler Performance with Advanced Thermal Spray Coatings. i-manager’s Journal on Mechanical Engineering, 15(3), 56-61.

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Tafadzwa Mukodi* , Rindai P. Mahoso, Godfrey Tigere*

Azaka Onyemazuwa Andrew* , Okeke Chisom Agatha, Ezenwafor Izuchukwu Sixtus*, Eze Godwin Chukwuemerie**, Odeh Calistus Princewill*, Osazuwa Orobosa Kingsley****

* Department of Mechanical Engineering, Jawaharlal Nehru Technological University-GV College of Engineering Vizianagaram, Andhra Pradesh, India.

** Department of Mechanical Engineering, University College of Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.

Harshit Garg* , Keshav Gulati, Divyanshu Mittal*