使用人工神经网络（ANN）、响应曲面法（RSM）和自适应神经模糊推理系统（ANFIS）从[具体种子名称未给出]种子中优化生物柴油生产的数据集。
Dataset on optimized biodiesel production from seeds of using ANN, RSM and ANFIS.
作者信息
Hariram V, Bose A, Seralathan S
机构信息
School of Mechanical Sciences, Hindustan Institute of Technology and Science, Chennai, Tamilnadu, India.
出版信息
Data Brief. 2019 Jul 23;25:104298. doi: 10.1016/j.dib.2019.104298. eCollection 2019 Aug.
This dataset disclose the investigational data on the extraction of bio-oil from seeds of through combination of mechanical pressing and soxhlet solvent extractor. Biodiesel is produced through single stage base catalysed transesterification process due to lower free fatty acid content in the bio-oil. Independent variable process parameters like molar ratio, reaction time and catalyst concentration are optimized using Artificial Neural Network, Response Surface Methodology and Adaptive Neuro-Fuzzy Interference System to predict the maximum biodiesel yield and the results are compared with the experimental data. Response Surface Methodology predicted a maximum biodiesel yield of 97.62% at methanol to oil molar ratio 0.2758 v/v, catalyst concentration 1.045 gm of NaOH and reaction duration of 1.11 hrs which is also confirmed with experimental results.
该数据集披露了通过机械压榨和索氏溶剂萃取器相结合从种子中提取生物油的研究数据。由于生物油中游离脂肪酸含量较低，生物柴油通过单级碱催化酯交换过程生产。使用人工神经网络、响应面法和自适应神经模糊推理系统对诸如摩尔比、反应时间和催化剂浓度等独立变量过程参数进行优化，以预测最大生物柴油产率，并将结果与实验数据进行比较。响应面法预测在甲醇与油的摩尔比为0.2758 v/v、催化剂浓度为1.045克氢氧化钠和反应持续时间为1.11小时时，最大生物柴油产率为97.62%，这也得到了实验结果的证实。
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