Herein, the production of biohydrogen by dark fermentation was optimized using a novel hybrid approach that combines ANNs (artificial neural networks) with RSM (response surface methodology). Using the limited numbers of data (15 runs) as training data set together with one cross‐out method for validation, the complete 29 runs of well‐established data matrix were created from ANNs for RSM statistical analysis in order to correlated the critical process parameters with hydrogen production performance. This methodology was found to be robust, cost‐effective, reliable, and can be extensively analyzed the critical operational parameters, that is, carbon sources (obtained from potato peel wastes and starchy wastes), metal cofactor Fe⁰, pH, and dose levels of microbes on the hydrogen production, along with concentrations of other metabolites, such as acetic acid, propionic acid, butyric acid, valeric acid, and ethanol. The established ANNs‐RSM model using Box–Behnken design indicates the significant changes caused by the variations of a few critical operation parameters. The resultant model shows an exceptionally good result in terms of nonlinear noisy processes. Both single and multiple objective optimizations for dark hydrogen fermentation can achieve by using the established hybrid ANN‐RSM system. The optimal operating conditions (starch 6.2 kg/m³, pH 6.7, Fe⁰ 11.7 g/m³, sludge 24.6 g/m³) could lead to the generation of hydrogen with a yield of 106.2 (cm³/g) and metabolites, that is, propionic acid (2.8 kg/m³), butyric acid (2E−2 kg/m³), valeric acid (4E−4 kg/m³) acetic acid (1.9 kg/m³), and ethanol (0.1 kg/m³) simultaneously.

中文翻译：

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使用混合人工神经网络（ANN）和响应面方法（RSM）的方法优化暗发酵生产生物氢

在此，通过使用ANN（人工神经网络）与RSM（响应表面方法）相结合的新型混合方法，优化了暗发酵生产生物氢的方法。使用有限数量的数据（15次运行）作为训练数据集以及一种交叉验证方法，从ANN创建完整的29次运行良好的数据矩阵以进行RSM统计分析，以便关联关键过程参数具有制氢性能。发现该方法可靠，经济高效，可靠，并且可以广泛地分析关键操作参数，即碳源（从马铃薯皮废物和淀粉状废物获得），金属辅因子Fe ⁰，pH值和产氢微生物的剂量水平，以及其他代谢产物的浓度，例如乙酸，丙酸，丁酸，戊酸和乙醇。使用Box-Behnken设计建立的ANNs-RSM模型表明，由于一些关键操作参数的变化而引起的重大变化。所得模型在非线性噪声过程方面显示出异常好的结果。通过使用已建立的混合ANN-RSM系统，可以实现暗氢发酵的单目标优化和多目标优化。最佳操作条件（淀粉6.2 kg / m ³，pH 6.7，Fe ⁰ 11.7 g / m ³，污泥24.6 g / m ³）可导致产生氢气，产率为106.2（cm）³ / g）和代谢物，即丙酸（2.8 kg / m ³），丁酸（2E-2 kg / m ³），戊酸（4E-4 kg / m ³）乙酸（1.9 kg / m ³），同时加入乙醇（0.1 kg / m ³）。