IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i22p5662-d1519626.html
   My bibliography  Save this article

Energy Consumption and Fume Analysis: A Comparative Analysis of the Blasting Technique and Mechanical Excavation in a Polish Gypsum Open-Pit Mine

Author

Listed:
  • Andrzej Biessikirski

    (Faculty of Civil Engineering and Resource Management, AGH University of Krakow, 30-059 Krakow, Poland)

  • Przemysław Bodziony

    (Faculty of Civil Engineering and Resource Management, AGH University of Krakow, 30-059 Krakow, Poland)

  • Michał Dworzak

    (Faculty of Civil Engineering and Resource Management, AGH University of Krakow, 30-059 Krakow, Poland)

Abstract

This article presents a comparative assessment of energy consumption and fume emissions such as NOx, CO 2 , and CO associated with the excavation of a specified gypsum volume using two mining methods (blasting and mechanical extraction). The analysis was carried out based on a case study gypsum open-pit mine in Poland where both extraction methods are applied. The findings indicate that, for the same output volume, blasting operations require significantly less energy (ranging from 1298.12 MJ to 1462.22 MJ) compared to mechanical excavation (86,654.15 MJ). Furthermore, a substantial portion of the energy in blasting operations is attributed to explosive loading and drilling (970.95 MJ). Conversely, mechanical mining results in higher fume emissions compared to blasting. However, during mechanical extraction, the fumes are dispersed over a prolonged period of 275 h, whereas blasting achieves the same gypsum volume extraction in approximately 7.5 h. The prediction model suggests that, based on the obtained data, overall gypsum extraction will decline unless new operational levels are developed or the mine is expanded. This reduction in gypsum extraction will be accompanied by a corresponding decrease in energy consumption and emission of fumes.

Suggested Citation

  • Andrzej Biessikirski & Przemysław Bodziony & Michał Dworzak, 2024. "Energy Consumption and Fume Analysis: A Comparative Analysis of the Blasting Technique and Mechanical Excavation in a Polish Gypsum Open-Pit Mine," Energies, MDPI, vol. 17(22), pages 1-28, November.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5662-:d:1519626
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/22/5662/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/22/5662/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cheenkachorn, Kraipat & Poompipatpong, Chedthawut & Ho, Choi Gyeung, 2013. "Performance and emissions of a heavy-duty diesel engine fuelled with diesel and LNG (liquid natural gas)," Energy, Elsevier, vol. 53(C), pages 52-57.
    2. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    3. Ramesh Murlidhar Bhatawdekar & Radhikesh Kumar & Mohanad Muayad Sabri Sabri & Bishwajit Roy & Edy Tonnizam Mohamad & Deepak Kumar & Sangki Kwon, 2023. "Estimating Flyrock Distance Induced Due to Mine Blasting by Extreme Learning Machine Coupled with an Equilibrium Optimizer," Sustainability, MDPI, vol. 15(4), pages 1-26, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jorge-Eusebio Velasco-López & Ramón-Alberto Carrasco & Jesús Serrano-Guerrero & Francisco Chiclana, 2024. "Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics," Mathematics, MDPI, vol. 12(6), pages 1-23, March.
    2. Fadaki, Masih & Asadikia, Atie, 2024. "Augmenting Monte Carlo Tree Search for managing service level agreements," International Journal of Production Economics, Elsevier, vol. 271(C).
    3. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    4. Maghsoodi, Abtin Ijadi, 2023. "Cryptocurrency portfolio allocation using a novel hybrid and predictive big data decision support system," Omega, Elsevier, vol. 115(C).
    5. Miroslav Navratil & Andrea Kolkova, 2019. "Decomposition and Forecasting Time Series in the Business Economy Using Prophet Forecasting Model," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 26-39.
    6. Bose, Probir Kumar & Deb, Madhujit & Banerjee, Rahul & Majumder, Arindam, 2013. "Multi objective optimization of performance parameters of a single cylinder diesel engine running with hydrogen using a Taguchi-fuzzy based approach," Energy, Elsevier, vol. 63(C), pages 375-386.
    7. Fadi Kahwash & Basel Barakat & Ahmad Taha & Qammer H. Abbasi & Muhammad Ali Imran, 2021. "Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study," Energies, MDPI, vol. 14(21), pages 1-23, October.
    8. Cho, Jungkeun & Park, Sangjun & Song, Soonho, 2019. "The effects of the air-fuel ratio on a stationary diesel engine under dual-fuel conditions and multi-objective optimization," Energy, Elsevier, vol. 187(C).
    9. Abu-Jrai, Ahmad M. & Al-Muhtaseb, Ala'a H. & Hasan, Ahmad O., 2017. "Combustion, performance, and selective catalytic reduction of NOx for a diesel engine operated with combined tri fuel (H2, CH4, and conventional diesel)," Energy, Elsevier, vol. 119(C), pages 901-910.
    10. Lorenzo Menculini & Andrea Marini & Massimiliano Proietti & Alberto Garinei & Alessio Bozza & Cecilia Moretti & Marcello Marconi, 2021. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices," Forecasting, MDPI, vol. 3(3), pages 1-19, September.
    11. Romero-Fiances, Irene & Livera, Andreas & Theristis, Marios & Makrides, George & Stein, Joshua S. & Nofuentes, Gustavo & de la Casa, Juan & Georghiou, George E., 2022. "Impact of duration and missing data on the long-term photovoltaic degradation rate estimation," Renewable Energy, Elsevier, vol. 181(C), pages 738-748.
    12. Li, Weifeng & Liu, Zhongchang & Wang, Zhongshu, 2016. "Experimental and theoretical analysis of the combustion process at low loads of a diesel natural gas dual-fuel engine," Energy, Elsevier, vol. 94(C), pages 728-741.
    13. Zhewei Huang & Yawen Yi, 2024. "Short-Term Load Forecasting for Regional Smart Energy Systems Based on Two-Stage Feature Extraction and Hybrid Inverted Transformer," Sustainability, MDPI, vol. 16(17), pages 1-25, September.
    14. Winita Sulandari & Yudho Yudhanto & Sri Subanti & Crisma Devika Setiawan & Riskhia Hapsari & Paulo Canas Rodrigues, 2023. "Comparing the Simple to Complex Automatic Methods with the Ensemble Approach in Forecasting Electrical Time Series Data," Energies, MDPI, vol. 16(22), pages 1-16, November.
    15. Ashish Shrestha & Bishal Ghimire & Francisco Gonzalez-Longatt, 2021. "A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System," Energies, MDPI, vol. 14(11), pages 1-15, June.
    16. Nik Dawson & Sacha Molitorisz & Marian-Andrei Rizoiu & Peter Fray, 2020. "Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism Jobs Crisis in Australia from 2012 to 2020," Papers 2008.12459, arXiv.org, revised Feb 2021.
    17. Luyao Zhang & Fan Zhang, 2023. "Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary Perspective," Papers 2305.02552, arXiv.org.
    18. Md Jamal Ahmed Shohan & Md Omar Faruque & Simon Y. Foo, 2022. "Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model," Energies, MDPI, vol. 15(6), pages 1-18, March.
    19. Ángel Cuevas & Ramiro Ledo & Enrique M. Quilis, 2021. "Seasonal adjustment of the Spanish sales daily data," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(4), pages 687-708, December.
    20. Sprangers, Olivier & Schelter, Sebastian & de Rijke, Maarten, 2023. "Parameter-efficient deep probabilistic forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 332-345.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5662-:d:1519626. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.