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Priscila Gonçalves Ferreira,Fernando Augusto da Silveira,Raquel Cristina Vieira dos Santos,Hugo Leonardo André Genier,Raphael Hermano Santos Diniz,José Ivo Ribeiro Júnior,Luciano Gomes Fietto,Flávia M 한국식품과학회 2015 Food Science and Biotechnology Vol.24 No.4
The simultaneous saccharification and fermentation (SSF) process is a promising strategy to obtain ethanol from cellulosic biomass. In this study, sugarcane bagasse was supplemented with ricotta whey to increase the sugar, vitamin, and trace metal concentrations in the fermentation medium. The optimum conditions for SSF ethanol production from a mixture of sugarcane bagasse and ricotta whey produced by Kluyveromyces marxianus CCT 7735 were evaluated considering five factors: cellulase concentration, cellulosic biomass concentration, pH, temperature, and agitation. The highest ethanol yield was 49.65 g/L with a cellulosic biomass of 80 g/L, pH value of 5.05, agitation at 65 rpm and temperature of 39.2ºC. The results demonstrated that a mixture of the cellulosic residue of sugarcane bagasse and ricotta whey is promising for ethanol production because the ethanol yield in the mixture was higher than that in single substrate of sugarcane bagasse.
Mauro Castelli,Leonardo Trujillo,Ivo Gonçalves,Aleš Popovič 사단법인 한국계산역학회 2017 Computers and Concrete, An International Journal Vol.19 No.6
High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, it is a highly complex material and modeling its behavior represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process.