石油与环境生物技术杂志

抽象的

Optimization of Fermentation Parameters for Bioconversion of Corn to Ethanol Using Response Surface Methodology

Navpreet Kaur Walia*, Kamaljeet Kaur Sekhon, Swaranjit Singh Cameotra, Dharam Paul Chaudhary, Pallavi Srivastava and Anil Dutta

The world is facing the problem of petroleum crisis and is in need of some immediate resolutions. The perception of sustainable development as a means to integrate the environmental, social and economic objectives of the society has been greatly developed in order to maximize human well-being in the present system without compromising the ability of future generations. Development that is not sustainable will inevitably lead to negative social, economic and environmental repercussions (OECD 2001). Energy is the crucial need of mankind and in addition is the priceless gift offered by the nature. The continuous rise in the use of fossil fuel and the extinguishing petroleum stocks have led us to rethink about the use of renewable energy sources that will also reduce carbon dioxide (CO2) emissions. Biofuels, such as ethanol, are generally considered renewable since the CO2 emitted into the atmosphere is recaptured by the growing crop in the next growth cycle. The most important issues relevant to the conversion of carbohydrates to ethanol are the cost and availability of substrate. Consequentially it is worthy to develop an economical process which allows the use of cheap substrates for successive conversion to ethanol. Hence, there is still need for cutting edge research to be done on an effective, economical, and efficient conversion process. The present study was conducted to optimize the ethanol production potential of maize (Zea mays). In order to achieve maximum ethanol production the experiments were conducted by optimizing three fermentation variables i.e. pH, temperature and substrate concentration which were optimized at different conditions using Response Surface Methodology (RSM) by design-expert software (version 8. 0.7.1 Stat-Ease Inc; USA). Parameters were optimized by central composite design observing the effect of combination of two variables and keeping the one constant on ethanol production. During the experiments, the maximum ethanol production was 74.6 g/L at conditions: pH 5.8, temperature 31°C and substrate concentration 160 g/L. The RSM is a better method for optimization of parameters as it is less labor intensive and more accurate than the classical methods. It reduces the number of fermentation batches. The adequacy of all the models was found to be significant at 99% as coefficients of determination were found to be (0.9923) (0.9735) (0.9662).