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Antunes, F. A. F, Chandel, A. K., Milessi, T. S. S., Santos, J. C., Rosa, C. A., and Da Silva, S. S. (2014).Bioethanol Production from SugarCane Bagasse by a Novel Brazillian Pentose Fermenting Yeast, Scheffersomyces shehatae UFMG-HM 52.2: Evaluation of Fermentation Medium. International Journal of Chemical Engineering, 2014:8..

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Article

Assessment Evaluation of Bio-Ethanol Yield for Energizing Prosthetics Production Plant Based on Bacterial Growth and Shaking Rate

1Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria

2Department of Metallurgical and Materials Engineering, Federal University of Technology, Owerri, Nigeria

3Department of Mechanical Engineering, Imo State University, Owerri, Nigeria

4Department of Industrial Physics, Ebonyi State University, Abakiliki, Nigeria


Biomedical Science and Engineering. 2015, Vol. 3 No. 1, 15-22
DOI: 10.12691/bse-3-1-4
Copyright © 2015 Science and Education Publishing

Cite this paper:
C. I. Nwoye, P. C. Agu, B. C. Chukwudi, S. O. Nwakpa, I. A. Ijomah, N. E. Idenyi. Assessment Evaluation of Bio-Ethanol Yield for Energizing Prosthetics Production Plant Based on Bacterial Growth and Shaking Rate. Biomedical Science and Engineering. 2015; 3(1):15-22. doi: 10.12691/bse-3-1-4.

Correspondence to: C.  I. Nwoye, Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria. Email: nwoyennike@gmail.com

Abstract

This paper presents an assessment evaluation of bio-ethanol yield based on the bacteria growth (BG) and shaking rate (SR) during bioprocessing of sugar cane molasses with Saccharomyces cerevisiae. Critical computational analysis of generated experimental results indicates that the bio-ethanol yield response typified an empirical model which is exponential-linear in nature. The model was validated prior to evaluation of the yield response coefficient and predictive analysis of generated results. The validity of the derived model expressed as; ζ = 4.6335e[0.0068(ϑ/ɤ)] + 0.00012₰ - 0.00004ε was rooted on the core model expression ζ - 0.00012 ₰ = 4.6335e 0.0068(ϑ/ɤ) - 0.00004ε where both sides of the expression are correspondingly approximately equal. Results of ethanol yield were generated using regression model and its trend of distribution was compared with that from derived model for the purpose of verifying its validity relative to experimental results. The results of the verification process show very close dimensions of covered areas and alignment of curves designating ethanol yield, which precisely translated into significantly similar trend of data point’s distribution for experimental (ExD), derived model (MoD) and regression model-predicted (ReG) results. Ethanol yield per unit input ratio SR/ BG were evaluated from experimental, derived model & regression model predicted results as 0.0496, 0.0573 & 0.0565 rpm/ O.D respectively. Standard errors incurred in predicting ethanol yield for each value of SR, BG & SR/ BG considered as obtained from experiment, derived model and regression model were 0.13369, 0.9674 and 1.3380%, 1.3096, 1.3615 and 1.5300 % & 1.3701, 0.5969 and 1.1459 x 10-5 respectively. The operationally viable deviation range of model-predicted ethanol yield from the experimental results was 0.9 -13.47 %. This translated into 86.53-99.1 % operational confidence and reliability level for the derived models, as well as 0.86 - 0.99 yield response coefficient of ethanol to the input ratio SR/ BG. Consequently, in order to obtain high confidence level, the derived model considers input parameter value; 50 rpm (shaking rate) very extraneous. This was as a result of 23.66% deviation associating the use of this input parameter value.

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