L-（＋）-lactic acid production was studied by immobilized Lactobacillus rhamnosus T1STR108 on crude pectin from Krung Kha Mao Leaves. Central composite design was employed to determine the maximum lactic acid product...L-（＋）-lactic acid production was studied by immobilized Lactobacillus rhamnosus T1STR108 on crude pectin from Krung Kha Mao Leaves. Central composite design was employed to determine the maximum lactic acid production of 42.88 g L-1 in predicted model with the factors at 4.11 g L1 of pectin, 6.05 mLLl inoculum and 1.09 mm of bead diameter. Statistical analyses demonstrated very high significance for the regression model, since the F-value computed 116.09 was much higher than the tabulated F-value 2.08 for the lactic acid production at 5% level for linear and quadratic polynomial regression models. The highest experimental lactic acid production was 43.57 g L^-1 at 96 h of fermentation, 1.58% higher than the predicted value.展开更多
The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition （CI） engine using jatropha biodiesel. Response su...The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition （CI） engine using jatropha biodiesel. Response surface methodology based on Central composite design （CCD） is used to design the experiments. Mathematical models are developed for combustion parameters （Brake specific fuel consumption （BSFC） and peak cylinder pressure （Pmax））, performance parameter brake thermal efficiency （BTE） and emission parameters （CO, NOx, unburnt HC and smoke） using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion （BSFC, Pmax）, performance （BTE） and emission （CO, NOx, HC, smoke） parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NOx, HC, smoke, a multi- objective optimization problem is formulated. Non- dominated sorting genetic algorithm-II is used in predict- ing the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine outputand emission parameters depending upon their own requirements.展开更多
文摘L-（＋）-lactic acid production was studied by immobilized Lactobacillus rhamnosus T1STR108 on crude pectin from Krung Kha Mao Leaves. Central composite design was employed to determine the maximum lactic acid production of 42.88 g L-1 in predicted model with the factors at 4.11 g L1 of pectin, 6.05 mLLl inoculum and 1.09 mm of bead diameter. Statistical analyses demonstrated very high significance for the regression model, since the F-value computed 116.09 was much higher than the tabulated F-value 2.08 for the lactic acid production at 5% level for linear and quadratic polynomial regression models. The highest experimental lactic acid production was 43.57 g L^-1 at 96 h of fermentation, 1.58% higher than the predicted value.
文摘The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition （CI） engine using jatropha biodiesel. Response surface methodology based on Central composite design （CCD） is used to design the experiments. Mathematical models are developed for combustion parameters （Brake specific fuel consumption （BSFC） and peak cylinder pressure （Pmax））, performance parameter brake thermal efficiency （BTE） and emission parameters （CO, NOx, unburnt HC and smoke） using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion （BSFC, Pmax）, performance （BTE） and emission （CO, NOx, HC, smoke） parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NOx, HC, smoke, a multi- objective optimization problem is formulated. Non- dominated sorting genetic algorithm-II is used in predict- ing the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine outputand emission parameters depending upon their own requirements.
