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Techno-economic analysis of enzymatic biodiesel co-produced in palm oil mills from sludge palm oil for improving renewable energy access in rural areas

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  • Ng, Wei Zhe
  • Obon, Aaron Anthony
  • Lee, Chin Loong
  • Ong, Yi Hui
  • Gourich, Wail
  • Maran, Kireshwen
  • Tang, Dennis Boon Yong
  • Song, Cher Pin
  • Chan, Eng-Seng

Abstract

To date, irregularity and lack of power supply remain a global problem in the rural areas of underdeveloped and developing countries. This paper proposes a new concept for rural electrification by using palm oil mills to co-produce biodiesel from sludge palm oil. We performed a case study based on a medium-sized palm oil mill in Central Kalimantan. Approximately 2.5 tonnes of biodiesel could be produced daily using an enzymatic batch process and potentially support around 1,375 households of 4 people each in the surrounding areas. Subsequently, we performed a techno-economic assessment to study the commercial feasibility of the concept for rural electrification using the enzymatic biodiesel as opposed to commercial diesel. A 10% price deduction was applied to incentivize the local community to use the co-produced biodiesel. From our analysis, the rural electrification concept was found to be commercially viable with a return on investment of 29% and a breakeven period of less than 4 years of operation. Barring the short-term impacts of the COVID-19 pandemic and the price fluctuations of diesel and crude palm oil, the long-term commercial prospect of this rural electrification concept is attractive as it financially benefits both the rural community and the mill operators.

Suggested Citation

  • Ng, Wei Zhe & Obon, Aaron Anthony & Lee, Chin Loong & Ong, Yi Hui & Gourich, Wail & Maran, Kireshwen & Tang, Dennis Boon Yong & Song, Cher Pin & Chan, Eng-Seng, 2022. "Techno-economic analysis of enzymatic biodiesel co-produced in palm oil mills from sludge palm oil for improving renewable energy access in rural areas," Energy, Elsevier, vol. 243(C).
  • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221029947
    DOI: 10.1016/j.energy.2021.122745
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    1. Deng, Lei & Shi, Congling & Li, Haoran & Wan, Mei & Ren, Fei & Hou, Yanan & Tang, Fei, 2023. "Prediction of energy mass loss rate for biodiesel fire via machine learning and its physical modeling of flame radiation evolution," Energy, Elsevier, vol. 275(C).
    2. Krishnamoorthy, Amarnath & Rodriguez, Cristina & Durrant, Andy, 2023. "Optimisation of ultrasonication pretreatment on microalgae Chlorella Vulgaris & Nannochloropsis Oculata for lipid extraction in biodiesel production," Energy, Elsevier, vol. 278(PB).

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