Ammonia synthesis has been gradually altered from the gray process (using fossil fuels as hydrogen sources) to the green process (directly or indirectly using water electrolytic cells as hydrogen sources powered by renewable energy), with the motivation of sustainable development and carbon neutrality. The fluctuating nature of renewable energy and the location mismatch between power plants and the production complex make gray ammonia production roadmaps likely to fail or embrace the change. Establishing knowledge graphs in the form of causal relationship network diagrams will help enterprise decision-makers and engineers better understand the process and generate correct production operations and scheduling. In this study, a chemical engineering-informed method is introduced to generate causal networks of multiple load conditions for green ammonia production. Expert knowledge of chemical engineering is embedded in the determination of the existence and corresponding time delay of the causal relationships of variable pairs. In an industrial case study, the skeletal knowledge graphs and evolution of the control mechanisms were identified in a comparison of the derived causal networks. Extended applications are expected with further integration of controlling theory and algorithms.

中文翻译：

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利用收敛交叉映射算法对绿氨合成过程进行因果分析

在可持续发展和碳中和的推动下，氨合成逐渐从灰色工艺（以化石燃料为氢源）转向绿色工艺（直接或间接以可再生能源为动力的水电解池作为氢源）。可再生能源的波动性以及发电厂和生产综合体之间的位置不匹配使得灰氨生产路线图可能会失败或接受这种变化。以因果关系网络图的形式建立知识图谱，将有助于企业决策者和工程师更好地理解流程并生成正确的生产作业和调度。在这项研究中，引入了一种化学工程知情方法来生成绿色氨生产的多种负荷条件的因果网络。化学工程的专业知识嵌入到变量对因果关系的存在性和相应的时间延迟的确定中。在工业案例研究中，通过比较派生的因果网络，确定了控制机制的骨架知识图和演化。随着控制理论和算法的进一步融合，预计会有更广泛的应用。