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首页> 外文期刊>Bioenergy research >Comparative Assessment of the Artificial Neural Network and Response Surface Modelling Efficiencies for Biohydrogen Production on Sugar Cane Molasses
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Comparative Assessment of the Artificial Neural Network and Response Surface Modelling Efficiencies for Biohydrogen Production on Sugar Cane Molasses

机译：甘蔗糖蜜中生物氢生产的人工神经网络和响应面建模效率的比较评估

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摘要

This study comparatively evaluates the modelling efficiency of the Response Surface Methodology (RSM) and the Artificial Neural Network (ANN). Twenty-nine biohydrogen fermentation batches were carried out to generate the experimental data. The input parameters consisted of a concentration of molasses (50–150 g/l), pH (4–8), temperature (35–40 °C) and inoculum concentration (10–50 %). The obtained data were used to develop the RSM and ANN models. The ANN model was a committee of networks with a topology of 4-(6-10)-1 structured on multilayer perceptrons. RSM and ANN models gave R2 values of 0.75 and 0.91, respectively, with predicted optimum conditions of 150 g/l, 8 and 35 °C for molasses, pH and temperature, respectively, with differences in inoculum concentrations (10.11 and 15 %) for RSM and ANN, respectively. Upon validation, 15.12 and 119.08 % prediction errors on hydrogen volume were found for ANN and RSM, respectively. These findings suggest that ANN has greater accuracy in modelling the relationships between the considered process inputs for fermentative biohydrogen production and thus, is more reliable to navigate the optimization space.
机译：这项研究比较评估响应面方法（RSM）和人工神经网络（ANN）的建模效率。进行了29批生物氢发酵，以产生实验数据。输入参数包括糖蜜浓度（50–150 g / l），pH（4–8），温度（35–40°C）和接种物浓度（10–50％）。获得的数据用于开发RSM和ANN模型。 ANN模型是由多层感知器构成的具有4-（6-10）-1拓扑的网络委员会。 RSM和ANN模型的R2值分别为0.75和0.91，预测的最佳条件为糖蜜，pH和温度分别为150 g / l，8和35°C，而接种物的浓度差异最大（10.11和15％） RSM和ANN。经验证，分别发现ANN和RSM的氢气体积预测误差为15.12％和119.08％。这些发现表明，人工神经网络在为发酵生物氢生产所考虑的过程输入之间的关系建模方面具有更高的准确性，因此，在优化空间中的导航更为可靠。

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