An open research question lies in how machine learning (ML) can accelerate the design optimization of chemical processes which are at very early experimental development stage with limited data availability. As an example, this article investigates the design of an intensified microwave‐assisted ammonia production reactor with 46 experimental data. We present an integrated approach of neural networks and synthetic minority oversampling technique to quantify the nonlinear input‐output relationships of this process. For ammonia concentration predictions at discrete operating conditions, the approach demonstrates 96.1% average accuracy over other ML methods (e.g., support vector regression 84.2%). The approach has also been applied for continuous optimization, identifying the optimal synthesis conditions at 597.37 K, 0.55MPa with feed flow rate of 1.67 ×10−3 m3/s kg and hydrogen to nitrogen ratio of 1 which is consistent with experimental observations. The data‐driven model enables to integrate this reactor with existing ammonia production infrastructure and benchmark with conventional techniques.

中文翻译：

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使用有限实验数据的机器学习辅助工艺设计：微波辅助氨合成案例研究


一个悬而未决的研究问题在于机器学习 （ML） 如何加速化学过程的设计优化，这些过程处于非常早期的实验开发阶段，数据可用性有限。例如，本文使用 46 个实验数据研究了强化微波辅助制氨反应器的设计。我们提出了一种神经网络和合成少数过采样技术的集成方法，以量化该过程的非线性输入-输出关系。对于离散操作条件下的氨浓度预测，该方法比其他 ML 方法的平均准确率为 96.1%（例如，支持向量回归 84.2%）。该方法还应用于连续优化，确定了 597.37 K、0.55MPa、进料流速为 1.67 ×10−3 m3/s kg、氢氮比为 1 的最佳合成条件，这与实验观察结果一致。数据驱动模型能够将该反应器与现有的氨生产基础设施集成，并使用传统技术进行基准测试。