IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v172y2019icp477-486.html
   My bibliography  Save this article

Analysis of energy saving potentials in intelligent manufacturing: A case study of bakery plants

Author

Listed:
  • Wang, Yanxia
  • Li, Kang
  • Gan, Shaojun
  • Cameron, Ché

Abstract

To address the global challenge of the climate change, more strict legislations worldwide on carbon emission reductions have put energy intensive industries under immense pressure to improve the energy efficiency. Due to the lack of technical support and financial incentives, a range of technical and economic barriers still exist for small-medium enterprises (SMEs). This paper first introduces a point energy technology, which is developed for SMEs to improve the insight of the energy usage in the manufacturing processes and installed in a local bakery. Statistical analysis of electricity consumption data over a seven-day period is conducted, including the identification of operational modes for individual processing units using an enhanced clustering method and the voltage unbalance conditions associated with these identified modes. Two technical strategies, namely electrical load allotment and voltage unbalance minimisation, are then proposed, which could attain more than 800 kwh energy saving during this period and the current unbalance could be reduced to less than 10%. In addition, the genetic algorithm is deployed to solve the job shop scheduling problem based upon the commercial electrical tariffs, and this reduces the electricity bill by £80 per day in the case study. Implementation of the recommendations based on the above analysis therefore may potentially yield significant financial and environmental benefits.

Suggested Citation

  • Wang, Yanxia & Li, Kang & Gan, Shaojun & Cameron, Ché, 2019. "Analysis of energy saving potentials in intelligent manufacturing: A case study of bakery plants," Energy, Elsevier, vol. 172(C), pages 477-486.
  • Handle: RePEc:eee:energy:v:172:y:2019:i:c:p:477-486
    DOI: 10.1016/j.energy.2019.01.044
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219300465
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2019.01.044?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zisopoulos, Filippos K. & Moejes, Sanne N. & Rossier-Miranda, Francisco J. & van der Goot, Atze Jan & Boom, Remko M., 2015. "Exergetic comparison of food waste valorization in industrial bread production," Energy, Elsevier, vol. 82(C), pages 640-649.
    2. Al-Shammari, Eiman Tamah & Shamshirband, Shahaboddin & Petković, Dalibor & Zalnezhad, Erfan & Yee, Por Lip & Taher, Ros Suraya & Ćojbašić, Žarko, 2016. "Comparative study of clustering methods for wake effect analysis in wind farm," Energy, Elsevier, vol. 95(C), pages 573-579.
    3. Paul W. Griffin & Geoffrey P. Hammond & Jonathan B. Norman, 2016. "Industrial energy use and carbon emissions reduction: a UK perspective," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 5(6), pages 684-714, November.
    4. Khatir, Zinedine & Paton, Joe & Thompson, Harvey & Kapur, Nik & Toropov, Vassili, 2013. "Optimisation of the energy efficiency of bread-baking ovens using a combined experimental and computational approach," Applied Energy, Elsevier, vol. 112(C), pages 918-927.
    5. Zhao, G.Y. & Liu, Z.Y. & He, Y. & Cao, H.J. & Guo, Y.B., 2017. "Energy consumption in machining: Classification, prediction, and reduction strategy," Energy, Elsevier, vol. 133(C), pages 142-157.
    6. Meyers, Steven & Schmitt, Bastian & Chester-Jones, Mae & Sturm, Barbara, 2016. "Energy efficiency, carbon emissions, and measures towards their improvement in the food and beverage sector for six European countries," Energy, Elsevier, vol. 104(C), pages 266-283.
    7. Dyer, Caroline H. & Hammond, Geoffrey P. & Jones, Craig I. & McKenna, Russell C., 2008. "Enabling technologies for industrial energy demand management," Energy Policy, Elsevier, vol. 36(12), pages 4434-4443, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Elsa Chaerun Nisa & Yean-Der Kuan & Chin-Chang Lai, 2021. "Chiller Optimization Using Data Mining Based on Prediction Model, Clustering and Association Rule Mining," Energies, MDPI, vol. 14(20), pages 1-14, October.
    2. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2022. "Risk assessment of energy investment in the industrial framework – Uncertainty and Sensitivity Analysis for energy design and operation optimisation," Energy, Elsevier, vol. 239(PA).
    3. Alessandra Cantini & Leonardo Leoni & Filippo De Carlo & Marcello Salvio & Chiara Martini & Fabrizio Martini, 2021. "Technological Energy Efficiency Improvements in Cement Industries," Sustainability, MDPI, vol. 13(7), pages 1-28, March.
    4. Marco Briceño-León & Dennys Pazmiño-Quishpe & Jean-Michel Clairand & Guillermo Escrivá-Escrivá, 2021. "Energy Efficiency Measures in Bakeries toward Competitiveness and Sustainability—Case Studies in Quito, Ecuador," Sustainability, MDPI, vol. 13(9), pages 1-20, May.

    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. Sovacool, Benjamin K. & Bazilian, Morgan & Griffiths, Steve & Kim, Jinsoo & Foley, Aoife & Rooney, David, 2021. "Decarbonizing the food and beverages industry: A critical and systematic review of developments, sociotechnical systems and policy options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    2. Fitzpatrick, John J. & Dooley, Paul, 2017. "Holistic view of CO2 reduction potential from energy use by an individual processing company," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 336-343.
    3. Papasidero, Davide & Pierucci, Sauro & Manenti, Flavio, 2016. "Energy optimization of bread baking process undergoing quality constraints," Energy, Elsevier, vol. 116(P2), pages 1417-1422.
    4. Griffin, Paul W. & Hammond, Geoffrey P. & Norman, Jonathan B., 2018. "Industrial energy use and carbon emissions reduction in the chemicals sector: A UK perspective," Applied Energy, Elsevier, vol. 227(C), pages 587-602.
    5. Griffin, Paul W. & Hammond, Geoffrey P., 2019. "Industrial energy use and carbon emissions reduction in the iron and steel sector: A UK perspective," Applied Energy, Elsevier, vol. 249(C), pages 109-125.
    6. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
    7. Josip Orović & Vedran Mrzljak & Igor Poljak, 2018. "Efficiency and Losses Analysis of Steam Air Heater from Marine Steam Propulsion Plant," Energies, MDPI, vol. 11(11), pages 1-18, November.
    8. Wang, Qiang & Song, Xiaoxin, 2021. "How UK farewell to coal – Insight from multi-regional input-output and logarithmic mean divisia index analysis," Energy, Elsevier, vol. 229(C).
    9. Cullen, Jonathan M. & Allwood, Julian M., 2010. "Theoretical efficiency limits for energy conversion devices," Energy, Elsevier, vol. 35(5), pages 2059-2069.
    10. Agnieszka Karman, 2022. "The Homogenization of Carbon Management Practices: How Organizations Response to Isomorphic Pressures to Reduce GHG Emissions," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 148-173.
    11. Jessica Walther & Matthias Weigold, 2021. "A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry," Energies, MDPI, vol. 14(4), pages 1-24, February.
    12. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).
    13. Aleksander Banasik & Argyris Kanellopoulos & G. D. H. Claassen & Jacqueline M. Bloemhof-Ruwaard & Jack G. A. J. Vorst, 2017. "Assessing alternative production options for eco-efficient food supply chains using multi-objective optimization," Annals of Operations Research, Springer, vol. 250(2), pages 341-362, March.
    14. Francesca Ceglia & Elisa Marrasso & Chiara Martone & Giovanna Pallotta & Carlo Roselli & Maurizio Sasso, 2023. "Towards the Decarbonization of Industrial Districts through Renewable Energy Communities: Techno-Economic Feasibility of an Italian Case Study," Energies, MDPI, vol. 16(6), pages 1-23, March.
    15. Xiao, Qinge & Li, Congbo & Tang, Ying & Pan, Jian & Yu, Jun & Chen, Xingzheng, 2019. "Multi-component energy modeling and optimization for sustainable dry gear hobbing," Energy, Elsevier, vol. 187(C).
    16. Ding, Li-Li & Lei, Liang & Zhao, Xin & Calin, Adrian Cantemir, 2020. "Modelling energy and carbon emission performance: A constrained performance index measure," Energy, Elsevier, vol. 197(C).
    17. Chidean, Mihaela I. & Caamaño, Antonio J. & Ramiro-Bargueño, Julio & Casanova-Mateo, Carlos & Salcedo-Sanz, Sancho, 2018. "Spatio-temporal analysis of wind resource in the Iberian Peninsula with data-coupled clustering," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2684-2694.
    18. Silviu Răileanu & Theodor Borangiu & Ionuț Lențoiu & Mihnea Constantinescu, 2024. "Optimizing Energy Consumption of Industrial Robots with Model-Based Layout Design," Sustainability, MDPI, vol. 16(3), pages 1-20, January.
    19. Sylwester Kaczmarzewski & Dominika Matuszewska & Maciej Sołtysik, 2021. "Analysis of Selected Service Industries in Terms of the Use of Photovoltaics before and during the COVID-19 Pandemic," Energies, MDPI, vol. 15(1), pages 1-24, December.
    20. Joanna Kossakowska & Sebastian Bombiński & Krzysztof Ejsmont, 2021. "Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning," Energies, MDPI, vol. 14(20), pages 1-23, October.

    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:eee:energy:v:172:y:2019:i:c:p:477-486. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.