CO₂ hydrogenation was optimized by a combination of AANs (Artificial Neuron Networks) with RSM (Response Surface Methodology) in a microchannel reactor using a K-promoted iron-based catalyst. This robust and cost-effective methodology was reliable to extensively analyze the effect of operating conditions i.e. gas ratio, temperature, pressure, and space velocity on product distribution of selective CO₂ hydrogenation. With experimental data as training data using ANNs and Box-Behnken design as design of experiment, the obtained model was able to present good results in a nonlinear noisy process with significant changes of critical operation parameters in an experimental design plan during CO₂ hydrogenation using K-promoted iron-based catalyst in a microchannel reactor. The achieved quadratic model was flexible and effective in optimizing either single or multiple objections of product distribution for CO₂ hydrogenation.

在使用K促进的铁基催化剂的微通道反应器中，通过AAN（人工神经元网络）与RSM（响应表面方法学）的组合优化了CO ₂氢化。这种可靠且具有成本效益的方法可靠地广泛分析了操作条件（即气体比率，温度，压力和空速）对选择性CO ₂加氢产物分布的影响。将实验数据作为训练数据，并使用人工神经网络和Box-Behnken设计作为实验设计，所获得的模型能够在非线性噪声过程中表现出良好的效果，并且在CO ₂期间实验设计计划中关键操作参数的显着变化在微通道反应器中使用K促进的铁基催化剂进行加氢。所获得的二次模型在优化CO ₂氢化产物分布的单个或多个对象方面既灵活又有效。