Selection of Raw Material with Variation and Price Difference in Obtaining Lowest Costs using A Case Study of Ethanol Production

  • Pittaya Hayakwong Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University
  • Seekharin Sukto Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University
Keywords: Time Series Forecasting, Box-Jenkin’s Method, Bioethanol Production, Raw Materials Selection, Decision Making


          This research aims to select raw materials from various kind of sugar mills with different prices, quality and quantity for obtaining the lowest raw material costs in ethanol production. Studying issue are the theoretical factors of converting sugar to ethanol, raw material, quality data collection, molasses C contract price and world sugar price forecast via Box-Jenkin's Auto-Regressive Integrated Moving Average (ARIMA) that is time-series forecast) to analyze the lowest of raw material costs. The results of this research concluded that the ARIMA model (1,2,1) forecast showed that the market sugar price in July 2021 rose to 12,131 baht per ton or 17.75. Cents per pound. As a result, the price of raw materials with the proportion of sugar in raw materials is significantly higher. Then, the process of raw material selection is start with calculation the cost of each raw material the SJM Formula to evaluate the concentration of sugar and molasses. Then the cost and amount of raw materials are sorted to meet customer demands. Therefore, the syrup selection during the sugar mill production period into the production process could be reduced the import of molasses from abroad and molasses B with high cost. The ethanol production process total cost is minimum total cost compare with other conditions. From the simulation scenario, the cost of raw materials can be reduced by 3.14%, or approximately 94.7 million baht per year.


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Arshad, M., Abbas, M. and Iqbal, M. (2019). Ethanol production from molasses: Environmental and socioeconomic prospects in Pakistan: Feasibility and economic analysis, Environmental Technology and Innovation, vol. 14, February 2019, pp. 100317

Naik, S. N. et al. (2010). Production of first- and second-generation biofuels: A comprehensive review, Renewable and Sustainable Energy Reviews, vol. 14(2), October 2009, pp. 578–597.

García-Bustamante, C. A. et al. (2018). Development of indicators for the sustainability of the sugar industry, Environmental and Socio-Economic Studies, vol. 6(4), November 2018, pp. 22–38.

Ihwah, A. and Viandini, U. H. (2020). Forecasting of purchasing quantity of Manalagi apple for apple juice drink production in PT XYZ Malang, IOP Conference Series: Earth and Environmental Science, vol. 475(012054), International Conference on Green Agro-industry and Bioeconomy

Bhatti, Z. A., Rajput, M.-H. and Maitlo, G. (2019). Impact of Storage Time, Rain and Quality of Molasses in the Production of Bioethanol, Mehran University Research Journal of Engineering and Technology, vol. 38(4), October 2019, pp. 1021–1032.

Zhang, P. G. (2003). Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, vol. 50, November 2001, pp. 159–175.

Wright, P. G., Fernandes, A. C., and Zarpelon, F. (2007). Control Calculations for Factories Producing Both Sugar and Alcohol, Proc. Aust. Soc. Sugar Cane Technol, vol. 29(1-13).

Borges, E. P. et al. (2015). The benefits of applied research: 37 years of discoveries, adaptations and solutions, Sugar Industry, vol. 6, April 2015, pp. 209–216.

Ahmad, S. et al. (2019). Study of morphological and qualitative plant traits against the infestation of chilo infuscatellus L. (pyralidae, lepidopetra), Applied Ecology and Environmental Research, vol. 17(3), April 2019, pp. 7057–7065.

อี้หงส์ โง้ว และวิเชียร กิจปรีชาวนิช. กระบวนการผลิตเอทานอลจากโมลาส, สำนักพิมพ์มหาวิทยาลัยเกษตรศาสตร์, 2560

Matyjaszek, M. et al. (2019). Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory, Resources Policy. Elsevier Ltd, vol. 61, December 2018, pp. 283–292.

Sujjaviriyasup, T. and Pitiruek, K. (2018). A comparison between MODWT-SVM-DE hybrid model and ARIMA model in forecasting primary energy consumptions, IEEE International Conference on Industrial Engineering and Engineering Management, December 2017, pp. 799–802.

Işığıçok, E., Öz, R. and Tarkun, S. (2007). Forecasting and Technical Comparison of Inflation in Turkey with Box-Jenkins (ARIMA) Models and the Artificial Neural Network, International Journal of Energy Optimization and Engineering, vol. 9(4), October 2020, pp. 84–103.

กรินทร์ กาญทนานนท์. การพยากรณ์ทางสถิติ. ซีเอ็ดยูเคชั่น, 2561 หน้า 147-173.

Chiewchanchairat, K., Bumroongsri, P. and Kheawhom, S. (2013). Role of hybrid forecasting techniques for transportation planning of broiler meat under uncertain demand in thailand, KKU Engineering Journal, vol. 40, March 2015, pp. 131–138.

Harlianingtyas, I., Salim, A., Hartatie, D., & Supriyadi, S. (2020). Forecasting sugarcane production in the Asembagus sugar factory, IOP Conference Series: Earth and Environmental Science, vol. 411(1), January 2020.

Kwon, H., Do, T. N. and Kim, J. (2020). Comprehensive Decision Framework Combining Price Prediction and Production-Planning Models for Strategic Operation of a Petrochemical Industry, Industrial and Engineering Chemistry Research, vol. 59(25), June 2020, pp. 11610-11620.

Mandade, P. and Shastri, Y. (2019). Multi-objective optimization of lignocellulosic feedstock selection for ethanol production in India, Journal of Cleaner Production, vol. 231, May 2019, pp. 1226-1234.

Thulasizwe T. Ngwenya. (2012). An industrial perspective of factors affecting molasses fermentation by Saccharomyces cerevisiae, Journal of Brewing and Distilling, vol. 3(2), March 2012, pp. 23–28.

US Sugar #11 Futures, United States (2021). Statistic Data, available online:

บริษัท มิตรผลวิจัยพัฒนาอ้อยและน้ำตาลจำกัด (2556). รายงานโครงการศึกษาอุณหภูมิที่มีผลต่อการเสื่อมคุณภาพของกากน้าตาลในระหว่างจัดเก็บ, รายงานโครงการศึกษาอุณหภูมิที่มีผลต่อการเสื่อมคุณภาพของกากน้ำตาลในระหว่างจัดเก็บ, หน้า 9 – 1

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