Shale gas is revolutionizing the U.S. energy and chemical commodity landscape and can ease the transition to a sustainable decarbonized economy. This work develops an equation-oriented (EO) multiscale modeling framework using the open-source IDAES-PSE platform that tractably incorporates microkinetic detail in process design via reduced-order kinetic (ROK) models. Using multiobjective optimization with embedded heat integration and life-cycle analysis, we simultaneously minimize the minimum selling price of liquid hydrocarbons (e.g., liquid fuels/additives from shale gas) and process emissions (via a CO₂ tax). Optimization reduces greenhouse gas emissions per MJ of fuel produced by over 35% compared to the literature and achieves a carbon efficiency of 87%. The optimizer changes the recycling rate, temperatures, and pressures to mitigate the effect of ROK model-form uncertainty on product portfolio predictions. Moreover, we show that the optimal process design is insensitive to changing CO₂ tax rates. Finally, the EO framework enables a fast sensitivity analysis of shale gas composition variability across 12 regions of the Eagle Ford basin. These results highlight the benefits of the open-source EO framework: fast, scalable, customized, and reproducible system analysis and optimization for sustainable energy technologies beyond shale utilization.

中文翻译：

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多尺度面向方程的优化降低页岩气转化为液体燃料过程的碳强度


页岩气正在彻底改变美国能源和化学商品格局，并可以轻松过渡到可持续脱碳经济。这项工作使用开源 IDAES-PSE 平台开发了一个面向方程 (EO) 的多尺度建模框架，该框架通过降阶动力学 (ROK) 模型将微动力学细节轻松地融入到工艺设计中。使用嵌入热集成和生命周期分析的多目标优化，我们同时最大限度地降低液态碳氢化合物（例如，来自页岩气的液体燃料/添加剂）的最低销售价格和过程排放（通过二氧化碳 ₂ 税） 。与文献相比，优化将每兆焦燃料生产的温室气体排放量减少了 35% 以上，并实现了 87% 的碳效率。优化器改变回收率、温度和压力，以减轻 ROK 模型形式不确定性对产品组合预测的影响。此外，我们表明最佳工艺设计对二氧化碳 ₂ 税率的变化不敏感。最后，EO 框架能够对 Eagle Ford 盆地 12 个区域的页岩气成分变化进行快速灵敏度分析。这些结果凸显了开源 EO 框架的优势：针对页岩利用之外的可持续能源技术进行快速、可扩展、定制和可重复的系统分析和优化。