Optimization of Palm Oil Processing Parameters to Improve Crude Palm Oil Yield and Energy Efficiency
Keywords:
crude palm oil yield, energy efficiency, process optimizationAbstract
Crude palm oil yield and mill energy consumption are conventionally managed as separate objectives, yet both are governed by the same set of controllable process parameters, so that improvements pursued independently frequently cancel one another. This paper reviews the parametric basis of palm oil milling and establishes where oil recovery and energy demand are coupled, where they are independent, and where a genuine trade-off exists. A structured review was conducted on peer-reviewed studies indexed in Scopus and SINTA published between 2010 and 2025, covering sterilization, threshing, digestion, pressing, clarification, and the mill utility system. Retained studies were coded for the unit operation examined, the parameters manipulated, the response measured, and the direction of the reported effect, and were then organised into a parameter-response matrix. The synthesis shows that sterilization is the dominant coupling point, since the same steam input that determines enzyme deactivation and bunch loosening also constitutes the largest single thermal load in the mill, while pressing and clarification affect oil recovery with comparatively little energy consequence. Evidence from mill-scale energy studies indicates that the milling process requires in the order of fifteen to twenty kilowatts and five hundred to seven hundred kilograms of steam per tonne of fresh fruit bunches, and that recovering vented sterilization steam and engine waste heat materially increases surplus power. The paper argues that parameter optimization should be formulated as a constrained multi-objective problem anchored on sterilization, and proposes a staged measurement protocol that mills can implement before committing capital to equipment change.
References
Abdullah, M. Z., Guan, L. C., & Mohd Azemi, B. M. N. (2001). Stepwise discriminant analysis for colour grading of oil palm using machine vision system. Food and Bioproducts Processing, 79(4), 223–231.
Booneimsri, P., Kubaha, K., & Chullabodhi, C. (2018). Increasing power generation with enhanced cogeneration using waste energy in palm oil mills. Energy Science & Engineering, 6(3), 154–173. https://doi.org/10.1002/ese3.196
Cheah, W. Y., Siti-Dina, R. P., Leng, S. T. K., Er, A. C., & Show, P. L. (2023). Circular bioeconomy in palm oil industry: Current practices and future perspectives. Environmental Technology & Innovation, 30, 103050. https://doi.org/10.1016/j.eti.2023.103050
Harahap, F., Leduc, S., Mesfun, S., Khatiwada, D., Kraxner, F., & Silveira, S. (2019). Opportunities to optimize the palm oil supply chain in Sumatra, Indonesia. Energies, 12(3), 420. https://doi.org/10.3390/en12030420
Khatun, R., Reza, M. I. H., Moniruzzaman, M., & Yaakob, Z. (2017). Sustainable oil palm industry: The possibilities. Renewable and Sustainable Energy Reviews, 76, 608–619. https://doi.org/10.1016/j.rser.2017.03.077
Razman, K. K., Hanafiah, M. M., Mohammad, A. W., & Ang, W. L. (2022). Life cycle assessment of an integrated membrane treatment system of anaerobic-treated palm oil mill effluent (POME). Membranes, 12(2), 246. https://doi.org/10.3390/membranes12020246
Saidur, R., Ahamed, J. U., & Masjuki, H. H. (2010). Energy, exergy and economic analysis of industrial boilers. Energy Policy, 38(5), 2188–2197. https://doi.org/10.1016/j.enpol.2009.11.087
Siagian, U. W. R., Wenten, I. G., & Khoiruddin, K. (2024). Circular economy approaches in the palm oil industry: Enhancing profitability through waste reduction and product diversification. Journal of Engineering and Technological Sciences, 56(1), 25–49. https://doi.org/10.5614/j.eng.technol.sci.2023.56.1.3
Suharjito, Elwirehardja, G. N., & Prayoga, J. S. (2021). Oil palm fresh fruit bunch ripeness classification on mobile devices using deep learning approaches. Computers and Electronics in Agriculture, 188, 106359. https://doi.org/10.1016/j.compag.2021.106359
Suharjito, Junior, F. A., Koeswandy, Y. P., Debi, Nurhayati, P. W., Asrol, M., & Marimin. (2023). Annotated datasets of oil palm fruit bunch piles for ripeness grading using deep learning. Scientific Data, 10, 72. https://doi.org/10.1038/s41597-023-01958-x
Umar, M. S., Urmee, T., & Jennings, P. (2018). A policy framework and industry roadmap model for sustainable oil palm biomass electricity generation in Malaysia. Renewable Energy, 128, 275–284. https://doi.org/10.1016/j.renene.2017.12.060
