This study employs Random Forests (RF) and Artificial Neural Networks (ANN) to model the transient behavior of Ni catalyst deactivation during steam and dry reforming of model biogas containing H2S, with a focus on hydrogen production. Deactivation, induced by carbon deposition and sulfur poisoning, is a complex and transient phenomenon demanding precise kinetic mechanisms for accurately predicting Ni catalyst behavior in biogas reforming. Black-box machine learning (ML) models are developed, incorporating catalyst properties, biogas composition, and operating conditions. Encompassing both dry and steam reforming, the ML models aim to predict catalyst behavior, expressed in terms of packed bed reactor exit mole fractions (H2, CO, CH4, and CO2) and conversions (CH4 and CO2). The ML models are trained and tested across a temperature range of 700–900 C with 0–145 ppm of H2S in model biogas (CH4/CO2 ratio varying from 1.0 to 2.0). RF outperforms the ANN across all performance metrics, including overall R2 and root mean squared error (RMSE). The RF achieves a mean overall R2 of 0.979, with training and testing RMSE equal to 6.7×10−3 and 1.47×10−2 respectively. In contrast, the ANN achieves a mean overall R2 of 0.939, with training and testing RMSE equal to 2.6×10−2 and 2.55×10−2 respectively. Moreover, pre-trained RF models are validated with unseen data of dry reforming of biogas containing 30 ppm of H2S (25 data points). It is suggested that 35 % of this unseen experimental data is required to train the RF model for it to predict catalyst deactivation, achieving a validation R sufficiently2> 0.9. The mean overall R2 values attained by the RF fine-tuned on 35 % of the unseen experiment data for both CH4 and CO2 conversions, as well as for all mole fraction predictions, are 0.952 and 0.948, respectively.

中文翻译：

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使用机器学习预测沼气蒸汽和干法重整中镍催化剂的失活，用于制氢


本研究采用随机森林 （RF） 和人工神经网络 （ANN） 来模拟含 H2S 的模型沼气在蒸汽和干重整过程中 Ni 催化剂失活的瞬态行为，重点是制氢。由碳沉积和硫中毒诱导的失活是一种复杂而短暂的现象，需要精确的动力学机制来准确预测沼气重整中的 Ni 催化剂行为。开发了黑盒机器学习 （ML） 模型，结合了催化剂特性、沼气成分和操作条件。ML 模型包括干重整和蒸汽重整，旨在预测催化剂行为，以填充床反应器出口摩尔分数（H2、CO、CH4 和 CO2）和转化率（CH4 和 CO2）表示。ML 模型在 700-900 C 的温度范围内使用 0-145 ppm 的 H2S 在模型沼气中进行训练和测试（CH4/CO2 比率从 1.0 到 2.0 不等）。RF 在所有性能指标上都优于 ANN，包括总体 R2 和均方根误差 （RMSE）。RF 的平均总体 R2 为 0.979，训练和测试 RMSE 分别等于 6.7×10−3 和 1.47×10−2。相比之下，ANN 的平均总体 R2 为 0.939，训练和测试 RMSE 分别等于 2.6×10-2 和 2.55×10-2。此外，预训练的 RF 模型通过含有 30 ppm H2S 的沼气干重整（25 个数据点）的看不见数据进行了验证。建议需要 35% 的这些看不见的实验数据来训练射频模型，使其预测催化剂失活，从而实现足够的验证 R 2> 0.9。 对于 CH4 和 CO2 转换以及所有摩尔分数预测，RF 对 35% 的看不见实验数据进行微调后，获得的平均总体 R2 值分别为 0.952 和 0.948。