This study delves into biodiesel synthesis from non-edible oils and algae oil sources using Response Surface Methodology(RSM)and an Artificial Neural Network(ANN)model to optimize biodiesel yield.Blend of C.vulgaris a...This study delves into biodiesel synthesis from non-edible oils and algae oil sources using Response Surface Methodology(RSM)and an Artificial Neural Network(ANN)model to optimize biodiesel yield.Blend of C.vulgaris and Karanja oils is utilized,aiming to reduce free fatty acid content to 1%through single-step transesterification.Optimization reveals peak biodiesel yield conditions:1%catalyst quantity,91.47 min reaction time,56.86℃reaction temperature,and 8.46:1 methanol to oil molar ratio.The ANN model outperforms RSM in yield prediction accuracy.Environmental impact assessment yields an E-factor of 0.0251 at maximum yield,indicating responsible production with minimal waste.Economic analysis reveals significant cost savings:30%-50%reduction in raw material costs by using non-edible oils,10%-15%increase in production efficiency,20%reduction in catalyst costs,and 15%-20%savings in energy consumption.The optimized process reduces waste disposal costs by 10%-15%,enhancing overall economic viability.Overall,the widespread adoption of biodiesel offers economic,environmental,and social benefits to a diverse range of stakeholders,including farmers,producers,consumers,governments,environmental organizations,and the transportation industry.Collaboration among these stakeholders is essential for realizing the full potential of biodiesel as a sustainable energy solution.展开更多
用RSM法以MRS培养基为基础对副干酪乳杆菌HD1.7的液体培养基进行优化。首先采用Plackett-Burman试验设计筛选显著因子,确定了影响细菌素产生的主要成分:牛肉膏、葡萄糖、酵母粉;运用最陡爬坡试验逼近最大细菌素产生区域;利用RSM法对培...用RSM法以MRS培养基为基础对副干酪乳杆菌HD1.7的液体培养基进行优化。首先采用Plackett-Burman试验设计筛选显著因子,确定了影响细菌素产生的主要成分:牛肉膏、葡萄糖、酵母粉;运用最陡爬坡试验逼近最大细菌素产生区域;利用RSM法对培养基进行优化。试验结果表明培养基最佳配方为酵母粉0.26%、牛肉膏0.88%、蛋白胨1.5%、葡萄糖2.45%、Mg-SO40.06%、K2HPO40.2%、吐温80 0.1%、MnSO40.005%、NaC l 0%。展开更多
为有效求解边坡可靠度,采用拉丁超立方(LHS)抽样提出了基于响应面法(RSM)数据表的边坡可靠度计算模型。考虑影响边坡稳定性的主要强度参数c,φ的不确定性,采用LHS抽样构建RSM随机样本点,借助岩土工程极限平衡的Slide程序获取样本响应值...为有效求解边坡可靠度,采用拉丁超立方(LHS)抽样提出了基于响应面法(RSM)数据表的边坡可靠度计算模型。考虑影响边坡稳定性的主要强度参数c,φ的不确定性,采用LHS抽样构建RSM随机样本点,借助岩土工程极限平衡的Slide程序获取样本响应值;通过将数据表法与RSM结合求解边坡可靠指标及失效概率。以Monte Carlo法计算结果作为可靠性分析的基准解,并与基本2层土坡模型和托巴110 k V施工变电站边坡计算结果进行对比分析,结果表明此方法不仅精度满足要求,且计算量较小、效率高,对实际工程边坡可靠度分析具有一定指导意义。展开更多
基金the financial support provided for this research project entitled“Enhancement of Cold Flow Properties of Waste Cooking Biodiesel and Diesel”under the File Number A/RD/RP-2/345 for the above publication.
文摘This study delves into biodiesel synthesis from non-edible oils and algae oil sources using Response Surface Methodology(RSM)and an Artificial Neural Network(ANN)model to optimize biodiesel yield.Blend of C.vulgaris and Karanja oils is utilized,aiming to reduce free fatty acid content to 1%through single-step transesterification.Optimization reveals peak biodiesel yield conditions:1%catalyst quantity,91.47 min reaction time,56.86℃reaction temperature,and 8.46:1 methanol to oil molar ratio.The ANN model outperforms RSM in yield prediction accuracy.Environmental impact assessment yields an E-factor of 0.0251 at maximum yield,indicating responsible production with minimal waste.Economic analysis reveals significant cost savings:30%-50%reduction in raw material costs by using non-edible oils,10%-15%increase in production efficiency,20%reduction in catalyst costs,and 15%-20%savings in energy consumption.The optimized process reduces waste disposal costs by 10%-15%,enhancing overall economic viability.Overall,the widespread adoption of biodiesel offers economic,environmental,and social benefits to a diverse range of stakeholders,including farmers,producers,consumers,governments,environmental organizations,and the transportation industry.Collaboration among these stakeholders is essential for realizing the full potential of biodiesel as a sustainable energy solution.
文摘用RSM法以MRS培养基为基础对副干酪乳杆菌HD1.7的液体培养基进行优化。首先采用Plackett-Burman试验设计筛选显著因子,确定了影响细菌素产生的主要成分:牛肉膏、葡萄糖、酵母粉;运用最陡爬坡试验逼近最大细菌素产生区域;利用RSM法对培养基进行优化。试验结果表明培养基最佳配方为酵母粉0.26%、牛肉膏0.88%、蛋白胨1.5%、葡萄糖2.45%、Mg-SO40.06%、K2HPO40.2%、吐温80 0.1%、MnSO40.005%、NaC l 0%。
文摘为有效求解边坡可靠度,采用拉丁超立方(LHS)抽样提出了基于响应面法(RSM)数据表的边坡可靠度计算模型。考虑影响边坡稳定性的主要强度参数c,φ的不确定性,采用LHS抽样构建RSM随机样本点,借助岩土工程极限平衡的Slide程序获取样本响应值;通过将数据表法与RSM结合求解边坡可靠指标及失效概率。以Monte Carlo法计算结果作为可靠性分析的基准解,并与基本2层土坡模型和托巴110 k V施工变电站边坡计算结果进行对比分析,结果表明此方法不仅精度满足要求,且计算量较小、效率高,对实际工程边坡可靠度分析具有一定指导意义。
