Reaction kinetic modeling, model-based optimization and experimental validation are performed for the direct synthesis of dimethyl ether from CO₂ rich synthesis gas. Among these disciplines, experimental methods and models are aligned in a stringent way of action, i.e., the same setup and models are applied throughout the whole contribution. First, a lumped reaction kinetic model from the literature is modified and parametrized to fit a vast array of 240 data points measured in a laboratory fixed bed reactor. The data were acquired using a mechanical mixture of the commercial catalysts CuO/ZnO/Al₂O₃ and γ-Al₂O₃. For this setup, a predictive model is derived and applied within dynamic model-based optimization. Here, the single-pass CO_x conversion serves as objective function while the operating conditions and composition of the mixed catalyst bed are the optimization variables. Finally, the optimization results obtained numerically are validated experimentally verifying the identified performance enhancement qualitatively. The remaining quantitative deviations yield valuable insights into model and methodological weaknesses or inaccuracies, closing the loop between kinetic investigations, model-based optimization and experimental validation.

中文翻译：

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由富含CO2的合成气直接合成二甲醚的优化：封闭实验研究与基于模型的反应器设计之间的循环

进行了反应动力学建模，基于模型的优化和实验验证，以从富含CO _2的合成气中直接合成二甲醚。在这些学科中，实验方法和模型以严格的动作方式对齐，即，在整个文稿中都应用了相同的设置和模型。首先，对文献中的集总反应动力学模型进行了修改和参数化，以适应在实验室固定床反应器中测得的240个数据点的广泛排列。的数据使用的工业催化剂的CuO /氧化锌/ Al的机械混合物获取的₂ ö ₃和γ-Al系₂ ö ₃。对于此设置，将导出预测模型并将其应用于基于动态模型的优化中。在此，单程CO _x转化用作目标函数，而混合催化剂床的操作条件和组成是最优化变量。最后，通过数值验证获得的优化结果，通过实验验证了定性的性能提升。其余的定量偏差可为模型和方法学的弱点或不准确之处提供有价值的见解，从而消除动力学研究，基于模型的优化和实验验证之间的循环。