A statistical model was developed in this study to describe bioethanol production through a batch fermentation process of sugarcane molasses by locally isolated Saccharomyces cerevisiae Y-39. Response surface methodology RSM based on central composite face centered design CCFD was employed to statistically evaluate and optimize the conditions for maximum bioethanol production and study the significance and interaction of incubation period, initial pH, incubation temperature, and molasses concentration on bioethanol yield. With the use of the developed quadratic model equation, a maximum ethanol production of 255？g/L was obtained in a batch fermentation process at optimum operating conditions of approximately 71？h, pH 5.6, 38°C, molasses concentration 18%？wt.%, and 100？rpm. 1. Introduction There is an increased interest in alternative fuels, especially liquid transportation fuels. Bioethanol is one of the most employed liquid biofuels due to the easy adaptability of this fuel to existing engines and because this is a cleaner fuel with higher octane rating than gasoline [1]. Ethanol market grew from less than a billion liters in 1975 to more than 39 billion liters in 2006 and is expected to reach 100 billion liters in 2015 [2]. Among the widely used substrates for ethanol production are the molasses, the wastes byproduct of sugar industries from sugarcane and sugar beet. This is because they are cheap raw materials, readily available, and ready for conversion with limited pretreatments as compared with starchy or cellulosic materials, as all sugars are present in a readily fermentable form [3]. Yeasts are the most commonly used microorganisms for ethanol fermentation. Saccharomyces cerevisiae is one of the well-known ethanol producers [4]. Ongoing research and development seeking to improve methods by minimizing the numbers of experiments provide information about the direct additive effects of the study variables and interaction effects using design of experiment methods. Recently, this statistical technique has been successfully applied in many fields [5–8]. Response surface Methodology (RSM) is a combination of mathematical and statistical techniques and is used for the modeling and analysis of problems in which a response of interest is influenced by several variables, and the objective is to optimize this response [9]. The most popular RSM design is the central composite design (CCD) for analysis of experimental data. The CCD is applied to estimate the coefficients of a particular model equation. The CCD is efficient and flexible, providing
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