Power optimization in mill plant design: Theoretical analysis and AggFlow simulation
Aouda Awad Arfoa1, Ashraf Alsafasfeh2, Alaa Al-Qutimat1, Abdullah Eial Awwad1, Amani Assolie 3, Reyad Al Dwairi2, Nabeel T. Alshabatat4
1Department of Electrical Engineering, Tafila Technical University, Taifla, Jordan
2Department of Natural Resources and Chemical Engineering, Tafila Technical University, Taifla, Jordan
3Department of Civil Engineering, Ajloun National University, Ajloun, Jordan
4Department of Mechanical Engineering, Tafila Technical University, Taifla, Jordan
Min. miner. depos. 2025, 19(1):56-64
https://doi.org/10.33271/mining19.01.056
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      ABSTRACT
      Purpose. This study aims to optimize the power consumption of a mill plant by combining theoretical analysis and process simulation using AggFlow software, since power optimization is a crucial factor in enhancing the efficiency and sustainability of industrial milling operations.
      Methods. The mill plant, comprising a primary jaw crusher, a secondary double roll crusher, a ball mill, multiple screens, and conveyors, was modeled in AggFlow. A field survey collected specifications of equipment components, and product samples were used for simulation. Theoretical power consumption was calculated and compared with actual field data. Various operational scenarios were simulated to identify opportunities for power consumption reduction.
      Findings. The optimized settings, including precise adjustments of gap width, rotational speed and belt speed, resulted in measurable power savings of 17.65% for jaw crusher, 7.69% for roll crushers, 13.33% for ball mill, and 20% for conveyor belts, with a total power consumption reduction of 14.29%.
      Originality. This study highlights the effective use of AggFlow software for power optimization in industrial milling processes, providing a new approach to reducing energy consumption in mill plants.
      Practical implications. The results provide practical insights for industries aiming to enhance energy efficiency in milling operations. The successful reduction in power consumption demonstrates the potential for integrating process simulation tools like AggFlow into sustainable plant management strategies.
      Keywords: mill plant design, power optimization, energy efficiency, AggFlow simulation, sustainable mining
      REFERENCES
- Jahid, A., Hossain, M.S., Monju, M.K.H., Rahman, M.F., & Hossain, M.F. (2020). Techno-economic and energy efficiency analysis of optimal power supply solutions for green cellular base stations. IEEE Access, 8, 43776-43795. https://doi.org/10.1109/ACCESS.2020.2973130
- Xu, Y., Yan, C., Liu, H., Wang, J., Yang, Z., & Jiang, Y. (2020). Smart energy systems: A critical review on design and operation optimization. Sustainable Cities and Society, 62, 102369. https://doi.org/10.1016/j.scs.2020.102369
- Xu, Y., Tian, S., Wang, Q., Yuan, X., Ma, Q., Liu, M., & Liu, C. (2021). Optimization path of energy-economy system from the perspective of minimum industrial structure adjustment. Energy, 237, 121650. https://doi.org/10.1016/j.energy.2021.121650
- Nota, G., Nota, F.D., Peluso, D., & Toro Lazo, A. (2020). Energy efficiency in Industry 4.0: The case of batch production processes. Sustainability, 12(16), 6631. https://doi.org/10.3390/su12166631
- Abdelaoui, F.Z.E., Jabri, A., & Barkany, A.E. (2023). Optimization techniques for energy efficiency in machining processes – A review. The International Journal of Advanced Manufacturing Technology, 125(7), 2967-3001. https://doi.org/10.1007/s00170-023-10927-y
- Alsafasfeh, A. (2024). Modeling and evaluating mill plant production using AggFlow software: Case study in the South of Jordan. Mining of Mineral Deposits, 18(1), 119-124. https://doi.org/10.33271/mining18.01.119
- Segura, I.P., Ranjbar, N., Damø, A.J., Jensen, L.S., Canut, M., & Jensen, P.A. (2023). A review: Alkali-activated cement and concrete production technologies available in the industry. Heliyon, 9(5), 15718. https://doi.org/10.1016/j.heliyon.2023.e15718
- Krishna, R.S., Mishra, J., Meher, S., Das, S.K., Mustakim, S.M., & Singh, S.K. (2020). Industrial solid waste management through sustainable green technology: Case study insights from steel and mining industry in Keonjhar, India. Materials Today: Proceedings, 33, 5243-5249. https://doi.org/10.1016/j.matpr.2020.02.949
- Ek, C.S. (1986). Energy usage in mineral processing. Mineral Processing at a Crossroads, 133-155. https://doi.org/10.1007/978-94-009-4476-3_6
- Fuerstenau, D.W., & Abouzeid, A.Z. (2002). The energy efficiency of ball milling in comminution. International Journal of Mineral Processing, 67(1-4), 161-185. https://doi.org/10.1016/S0301-7516(02)00039-X
- Yu, B.Y., Yang, G., Lee, K., & Yoo, C. (2016). AggFlow: Scalable and efficient network address virtualization on software defined networking. Proceedings of the 2016 ACM Workshop on Cloud-Assisted Networking, 1-6. https://doi.org/10.1145/3010079.3012012
- Hasan, A.M., Tuhin, R.A., Ullah, M., Sakib, T.H., Thollander, P., & Trianni, A. (2021). A comprehensive investigation of energy management practices within energy intensive industries in Bangladesh. Energy, 232, 120932. https://doi.org/10.1016/j.energy.2021.120932
- Narciso, D.A., & Martins, F.G. (2020). Application of machine learning tools for energy efficiency in industry: A review. Energy Reports, 6, 1181-1199. https://doi.org/10.1016/j.egyr.2020.04.035
- McLellan, B.C., Corder, G.D., Giurco, D., & Green, S. (2009). Incorporating sustainable development in the design of mineral processing operations – Review and analysis of current approaches. Journal of Cleaner Production, 17(16), 1414-1425. https://doi.org/10.1016/j.jclepro.2009.06.003
- Bond, F.C. (1952). The third theory of comminution. Transactions of the American Institute of Mining and Metallurgical Engineers, 193, 484-494.
- Salazar, J.L., Valdés-González, H., & Cubillos, F. (2010). Advanced simulation for semi-autogenous mill systems: A simplified models approach. Dynamic Modelling, 145-156.
- Bond, F.C. (1961). Crushing and grinding calculations Part II. British Chemical Engineering, 6, 543
- Liu, X., & Li, S. (2024). A crushing index for granular soils based on comminution energy consumption theory. Powder Technology, 434, 119380. https://doi.org/10.1016/j.powtec.2024.119380
- Vinogradov, Y.I., Khokhlov, S.V., Zigangirov, R.R., Miftakhov, A.A., & Suvorov, Y.I. (2024). Optimization of specific energy consumption for rock crushing by explosion at deposits with complex geological structure. Journal of Mining Institute, 266, 231-245.
- Zhang, C., Wang, P., Liu, X., Wang, E., Jiang, Q., & Liu, M. (2024). Energy evolution and coal crushing mechanisms involved in coal and gas outburst. Natural Resources Research, 33(1), 455-470. https://doi.org/10.1007/s11053-023-10285-2
- Chimwani, N. (2024). Optimization of the mechanical comminution – The crushing stage. Recovery of Values from Low‐Grade and Complex Minerals: Development of Sustainable Processes, 1-40. https://doi.org/10.1002/9781119896890.ch1
- Alsafasfeh, A., Alawabdeh, M., Alfuqara, D., Gougazeh, M., & Amai-reh, M.N. (2022). Oil shale ash as a substitutional green component in cement production. Advances in Science and Technology Research Journal, 16(4), 157-162. https://doi.org/10.12913/22998624/152464
- Napier-Munn, T.J., Morrell, S., Morrison, R.D., & Kojovic, T. (1996). Mineral comminution circuits: Their operation and optimisation. Vo-lume 2. Indooroopilly, Australia: Julius Kruttschnitt Mineral Research Centre, University of Queensland, 413 p.
- Bennett, T. (2015). Modelling of crushing operations in the aggregates industry. PhD Thesis. Birmingham, United Kingdom: University of Birmingham, 105 p.
- Schnatz, R. (2004). Optimization of continuous ball mills used for finish-grinding of cement by varying the L/D ratio, ball charge filling ratio, ball size and residence time. International Journal of Mineral Processing, 74, S55-S63. https://doi.org/10.1016/j.minpro.2004.07.024
- Zhou, Z., Rajan, K., Labbé, N., & Wang, S. (2024). Significantly reducing energy consumption during nanolignin production via high-solid content grinding. Industrial Crops and Products, 211, 118209. https://doi.org/10.1016/j.indcrop.2024.118209
- Morrell, S. (2004). A new autogenous and semi-autogenous mill model for scale-up, design and optimisation. Minerals Engineering, 17(3), 437-445. https://doi.org/10.1016/j.mineng.2003.10.013
- Bortnowski, P., Gładysiewicz, L., Król, R., & Ozdoba, M. (2021). Energy efficiency analysis of copper ore ball mill drive systems. Energies, 14(6), 1786. https://doi.org/10.3390/en14061786
- Merkus, H.G., & Meesters, G.M. (2016). Production, handling and characterization of particulate materials. Volume 25. Cham, Switzerland: Springer Cham, 548 p. https://doi.org/10.1007/978-3-319-20949-4
- Nikolić, V., Doll, A., & Trumić, M. (2022). A new methodology to obtain a corrected Bond ball mill work index valid with non-standard feed size. Minerals Engineering, 188, 107822. https://doi.org/10.1016/j.mineng.2022.107822
- Austin, L.G., & Trass, O. (1997). Size reduction of solids crushing and grinding equipment. Handbook of Powder Science & Technology, 586-634. Boston, United States: Springer US. https://doi.org/10.1007/978-1-4615-6373-0_12
- Cleary, P.W., Delaney, G.W., Sinnott, M.D., Cummins, S.J., & Morrison, R.D. (2020). Advanced comminution modelling: Part 1 – Crushers. Applied Mathematical Modelling, 88, 238-265. https://doi.org/10.1016/j.apm.2020.06.049
- Satria, I., & Rusli, M. (2018). A comparison of effective tension calculation for design Belt conveyor between CEMA and DIN Standard. MATEC Web of Conferences, 166, 01007. https://doi.org/10.1051/matecconf/201816601007
- AggFlow: Planning for Profits. (2025). What is AggFlow? Retrieved from: https://www.aggflow.com/