Data-Driven Molecular to Systems Optimization: Reactor and Process Design for Shale Gas Processing

The US shale gas sector has expanded its production in recent years. Dry natural gas (NG) and natural gas liquids (NGL) production has surged considerably, demanding adequate handling of these unique resources. Its efficient and strategic utilization is crucial for the energy transition towards net-zero emission. The combination of process development along with environmental assessment can provide crucial guidance during the conceptual development stages of these sustainable processes. Multiscale modeling, therefore, plays a critical role in modernizing the energy economy including the responsible use of shale gas. The incorporation of detailed kinetic information in process design facilitates confident and meaningful product portfolio estimations. On the other hand, adding environmental impact metrics capable of observing GHG emissions from different process configurations, operating conditions, and design thresholds facilitates the selection of sustainable processing schemes.

State-of-the-art processes have studied the feasibility of producing liquid fuels from different shale gas feedstocks including its associated environmental impacts. At the heart of the catalytic shale gas upgrading process is the oligomerization reactor which dictates the transformation of NG and NGLs to heavier alkenes that can be used as fuel additives. Considerable prior work has been focused on studying and modeling the kinetics of catalytic oligomerization, resulting in the development of complex, i.e. O(1000) reaction rates, microkinetic (MK) models. Due to numerical tractability concerns, there are limitations to adopting MK models for detailed reactor modeling, optimization, and design. Engineers often use conversion or equilibrium models (e.g., Gibbs free energy minimization) for process design and optimization. For complex reaction networks, including oligomerization in NG upgrading, such simplified models may lead to inaccurate conclusions by not considering chemical kinetics.

In this work, we develop an equation-oriented (EO) multiscale modeling framework to tractably incorporate microkinetic detail in process design using validated reduced-order kinetic models combined with a tailor-made emissions assessment tool and simultaneous heat integration. The proposed framework is capable of identifying operating conditions that simultaneously reduce associated process emissions and the minimum selling price of our desired product. The equation-oriented approach makes this framework ideal for fast, scalable, customized, and reproducible process systems analysis and optimization that is not restricted by application.