The transition toward a circular and biobased chemical industry is needed to cut global CO₂ emissions and limit the chemical industry’s overall impact on the environment. However, the development of circular chemical reaction systems is challenging as it requires symbiotic sets of novel chemical reaction pathways and involves unconventional processing steps. We present a methodological pipeline for automated reaction network optimization. The tools can guide the development of circular processes on the reaction pathway level. Chemical big data combined with energetic assessment metrics and state-of-the-art decision-making has the potential to efficiently identify the most promising reaction systems. We mine large-scale chemical reaction data from Reaxys database and automate the screening of pathways based on chemical rules. We then approximate thermodynamic properties for exergy calculations of the prescreened pathways and formulate the optimization problem as linear programming and mixed-integer linear programming problem. The methodological workflow is illustrated in a case study on the conversion of β-pinene to citral. Our results show that the tools are well suited to model circular process interactions within different environment scenarios.

中文翻译：

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通过自动化反应网络优化发现循环过程解决方案

需要向循环和生物基化学工业过渡以减少全球 CO ₂排放和限制化学工业对环境的整体影响。然而，循环化学反应系统的开发具有挑战性，因为它需要一组共生的新型化学反应途径，并且涉及非常规的处理步骤。我们提出了自动化反应网络优化的方法论管道。这些工具可以指导反应途径水平上循环过程的发展。化学大数据与能量评估指标和最先进的决策相结合，有可能有效地识别最有前途的反应系统。我们从 Reaxys 数据库中挖掘大规模化学反应数据，并根据化学规则自动筛选通路。然后，我们为预先筛选的路径的火用计算近似热力学性质，并将优化问题表述为线性规划和混合整数线性规划问题。β-蒎烯转化为柠檬醛的案例研究说明了方法学工作流程。我们的结果表明，这些工具非常适合模拟不同环境场景中的循环流程交互。