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

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

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    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.

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