Abstract: The paper presents a proposal to reduce the cost of the pipeline transport of hydromixture in the lime production process. The purpose of the research was to reduce the energy and water demand during the flow of lime hydromixture with a deflocculant of a specific composition in a selected pipeline. Since the addition of an appropriate deflocculant significantly reduces the effect of particle size on the flowability of the suspension, the selection of optimal transport conditions is a fundamental issue. To provide the most efficient transport condition of the hydromixture tested in the analysed pipeline, a mathematical model has been developed that assumes the most influential factors in the operation of the investigated enterprise technology. The input parameters were proposed using a statistical method. In the article, the consumption costs of the components (materials and energy) in the process of piping a lime hydromixture of different mass concentrations were compared. The analysis carried out aimed to reduce the amount of electrical energy needed to drive the pump during the flow of a hydromixture of four different mass concentrations and its volumetric flow rate in the pipeline, while maintaining a constant amount of the transported mass of the solid phase in the hydromixture. The results of the analysis revealed that controlling selected operating factors, such as the mass concentration of the hydromixture, the flow rate, and the pump head, improves the energy efficiency of the hydrotransport process in the limestone mine. As a result of the investigation, significant reduction of the total cost of the pipeline transport is possible.
B I B L I O G R A F I A1. Jasińska-Biliczak, A. Smart-city citizen engagement: the answer to energy savings in an economic crisis? Energies 2022, 15, 8828. https://doi.org/10.3390/en15238828
2. URE: Energy Regulatory Office. Average selling price of electricity on the competitive market (annual and quarterly) in Poland in 2018–2024. Available on: https://www.ure.gov.pl/pl/energia-elektryczna/ceny-wskazniki/7852,Srednia-cena-sprzedazyenergii-elektrycznej-na-rynku-konkurencyjnymroczna-i-kwa.html
3. PGE: Polish Energy Group S.A. Tariff for electricity distribution services of PGE Distribution S.A. Available on: https://pgedystrybucja.pl/strefa-klienta/informacje-dla-konsumenta/taryfy-i-cenniki
4. Ashok, S. Peak-load management in steel plants. Applied Energy. 2006, 83(5), 413–424. https://doi.org/10.1016/j.apenergy.2005.05.002
5. Wang, J., Wang, Q.,Sun, W. Optimal power system flexibility-based scheduling in iron and steel production: A case of steelmaking-refining-continuous casting process. Journal of Cleaner Production. 2023, 414, 137619. https://doi.org/10.1016/j.jclepro.2023.137619
6. Skoczylas, A., Stefaniak, P., Gryncewicz, W., Rot, R. The Concept of an intelligent decision support system for ore transportation in underground mine. Procedia Computer Science. 2023, 225, 922–931. https://doi.org/10.1016/j.procs.2023.10.079
7. Energy Regulatory Office in Poland. Average selling price of electricity in the competitive market. https://www.ure.gov.pl/pl/energia-elektryczna/ceny-wskazniki/7852,Srednia-cena-sprzedazy-energii-elektrycznej-na-rynku-konkurencyjnym-roczna-i-kwa.html (accessed on 20 July 2024)
8. Chhabra, R.P. and Richardson, J.F. Non-Newtonian Flow and Applied Rheology, 2nd ed. Butterworth-Heinemann: Oxford, UK, 2008.
9. Grobler, L.J. and den Heijer, W.L. The effect of data period selection for the development of measurement and verification baselines for mine pumping projects. Energy Engineering, 2006, 103(3). https://doi.org/10.1080/01998590609509466
10. Wu, J., Graham, L., Wang, S. and Parthasarathy, R. Energy efficient slurry holding and transport. Minerals Engineering. 2010, 23, 705–712. https://doi.org/10.1016/j.mineng.2010.04.008
11. Zhang, H., Zhang, J. and Xia, X. Optimal sizing and operation of pumping systems to achieve energy efficiency and load shifting. Electric Power Systems Research. 2012, 86, 41–50. https://doi.org/10.1016/j.epsr.2011.12.002
12. Xia, X. and Zhang, J. operation efficiency optimisation modelling and application of model predictive control. In IEEE/CAA Journal of Automatica Sinica. 2015, 2(2), 166–172. https://doi.org/10.1109/JAS.2015.7081656
13. Brand, H., Vosloo, J. C. and Mathews, E. H. Automated energy efficiency project identification in the gold mining industry. Proceedings of the Conference on the Industrial and Commercial Use of Energy, 2015, 17–22. https://doi.org/10.1109/ICUE.2015.7280241
14. Van Staden, A.J., Zhang, J. and Xia, X. A model predictive control strategy for load shifting in a water pumping scheme with maximum demand charges. Applied Energy, 2009, 88(12), 1–7. https://doi.org/10.1016/J.APENERGY.2011.06.054
15. Jaworska-Jóźwiak, B. Assessment of slurry transport efficiency after applying deflocculant in the lime production process. Management and Production Engineering Review, 2023, 14(4), 1–8. https://doi.org/10.24425/mper.2023.147203
16. Sinchuk, O., Strzelecki, R., Beridze, T., Peresunko, I., Baranovskyi V., Kobeliatskyi D. and Zapalskyi V. Model studies to identify input parameters of an algorithm controlling electric supply/consumption process by underground iron ore enterprises. Mining of Mineral Deposits. 2023, 17(3), 93–101. https://doi.org/10.33271/mining17.03.093
17. Middelberg, A., Zhang, J., and Xia X. An optimal control model for load shifting with application in the energy management of a colliery. Applied Energy, 2009, 86, 1266–1273. https://doi.org/10.1016/j.apenergy.2008.09.011
18. Patterson, S.R., Kozan, E. and Hyland, P. An integrated model of an open-pit coal mine: improving energy efficiency decisions. International Journal of Production Reserach, 2016, 54(14). https://doi.org/10.1080/00207543.2015.1117150
19. Sinchuk, O., Strzelecki, R., Sinchuk, I., Beridze, T., Fedotov, V. and Baranovskyi, V. Mathematical model to assess energy consumption using water inflow-drainage system of iron-ore mines in terms of a stochastic process. Mining of mineral Deposits, 2022, 4, 19–28. https://doi.org/10.33271/mining16.04.019
20. Hao, C.H., Wesseh, P.K.Jr., Wang, J., Abudu, H., Dogah, K.E., Okorie, D.I. and Opoku, E.E.O. Dynamic pricing in consumer-centric electricity markets: A systematic review and thematic analysis. Energy Strategy Reviews, 2024, 52, 101349. https://doi.org/10.1016/j.esr.2024.101349
21. Xia, X. and Zhang, J. Mathematical description of the performance for the measurement and verification of energy efficiency improvement. Applied Energy, 2013, 111, 247–256. http://dx.doi.org/10.1016/j.apenergy.2013.04.063
22. Shook, C. and Roco, M. Slurry Flow: Principles and practice. 1991, Boston: Butterworth-Heinemann.
23. Jaworska-Jóźwiak, B. and Dziubinski, M. Effect of Deflocculant Addition on Energy Savings in Hydrotransport in the Lime Production Process. Energies, 2022, 15(11), 3869. https://doi.org/10.3390/en15113869
24. Chłopek, Z. and Piaseczny, L. Application of the theory of experimental planning in the study of the ecological properties of combustion engines. Automotive Archive, 2002, 2(3), 69–93.
25. Koźlak, W. Evaluation of possibility to use sodium water glasses produced in Poland to remove Ag+ ions from aqueous media. Ecol Chem Eng A. 2012, 19(10), 1289–1295. https://doi.org/10.2428/ecea.2012.19(10)124
26. Matinfar, M., Nychka, J.A. A review of sodium silicate solutions: Structure, gelation, and syneresis. Advances in Colloid and Interface Science 2023, 322, 103036. https://doi.org/10.1016/j.cis.2023.103036 
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