Sugar-ethanol-electricity co-generation in Hawai’i: An application of linear programming (LP) for optimizing strategies

• Published in: Biomass & bioenergy Vol. 48; pp. 203 - 212
• Main Authors: Illukpitiya, Prabodh, Yanagida, John F., Ogoshi, Richard, Uehara, Goro
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.01.2013; Elsevier
• Subjects: Agronomy. Soil science and plant productions; Applied sciences; Biofuel; Biofuels; Biological and medical sciences; Biomass; crop yield; electricity; Energy; Energy crops; Ethanol; Exact sciences and technology; feedstocks; Fundamental and applied biological sciences. Psychology; General agronomy. Plant production; Hawaii; land resources; Linear program; linear programming; Natural energy; Non-prime land; production costs; profits and margins; Sorghum; sugarcane; sugars; sweet sorghum; Use of agricultural and forest wastes. Biomass use, bioconversion; Maui; Hawaii; Polynesia; Oceania; USA, Hawaii; Hawaii; Biofuel; Energy crops; Ethanol; Non-prime land; Linear program; Cogeneration; Cane sugar; Linear programming; Biomass; Optimization; Energy crop; Sugar cane(by product); Sugar cane; Electric power production
• ISSN: 0961-9534; 1873-2909
• DOI: 10.1016/j.biombioe.2012.11.003

This research develops a linear programming (LP) model to assess various options for sugar and biofuel production from sugarcane and other feedstock in Hawaii. More specifically, the study focuses on finding optimal sugar and biomass feedstock that would maximize producer profits in the production of sugar, ethanol and electricity. Feedstock included in the model were sugarcane, banagrass, energy cane and sweet sorghum. Given available land resources for growing energy crops on the island of Maui, four land resource scenarios were considered. If available land resources were used in the production of sugarcane and energy crops with added utilization of non-prime lands, Hawaii's ethanol goal for year 2020 could be achieved while maintaining two-thirds of Hawaii's current sugar production. Crop yields and unit production costs are key factors in determining optimal quantities of feedstock in the optimization model tested in this study.