(643b) Sustainable Process Design and Optimization for Microwave-Assisted Ammonia Production

The ongoing trend toward energy-efficient, cost-effective, and environmentally sustainable chemical production has driven the innovation of ammonia production technologies [1, 2]. Among these, microwave-assisted ammonia synthesis has emerged as a promising alternative to the conventional Haber-Bosch process, offering advantages such as rapid localized heating, reduced reaction temperatures and pressures, and potential for distributed modular deployment [3]. However, despite its promising lab-scale results, there exists a critical research gap in systematically evaluating the techno-economic and environmental viability of microwave-assisted ammonia synthesis at the systems level. Furthermore, there is a pressing need for a unified modeling framework that can benchmark this novel technology against established processes, while simultaneously exploring its integration into complete process flowsheets to assess system-wide impacts. This includes the concurrent assessment of modularization and process intensification opportunities, which are essential to fully leverage the unique characteristics of microwave-based systems and facilitate scalable implementation.

In this study, we develop a holistic modeling and superstructure optimization framework for ammonia production incorporating multiple production pathways to optimize energy efficiency, cost-effectiveness, and environmental sustainability. The superstructure consists of four major processing sections with the following representative technologies considered: (i) hydrogen production via methane steam reforming or water electrolysis, (ii) nitrogen extraction through cryogenic air separation or membrane-assisted separation, (iii) ammonia synthesis using the Haber-Bosch reactor or microwave reactors, and (iv) ammonia purification via thermal separation or membrane separation. This formulation captures a comprehensive range of both commercialized and emerging technologies, enabling comparative evaluations under diverse process configurations. To address the computational complexity of large-scale superstructure optimization, we employ data-driven surrogate modeling based on high-fidelity first-principles simulation built in Aspen Plus, Aspen Custom Modeler, Chemcad, etc [4]. The resulting surrogate models, developed using artificial neural networks, are then integrated to a single mixed-integer nonlinear programming (MINLP) problem for superstructure optimization. The objective is to identify the optimal technology selection with respect to different design goals. Four process design scenarios are studied including the optimization of energy consumption, equivalent annualized operating cost, carbon emissions, and Eco-Indicator 99 for environmental footprint. The Eco-Indicator 99 considers key sustainability metrics such as global warming potential, resource depletion, and impacts on human health and ecosystem quality [5]. This study provides a comprehensive evaluation of conventional and emerging ammonia production technologies, offering valuable insights into the trade-offs between economic and environmental considerations. Key research findings include: (i) Break-even analysis for MW reactor capital cost, (ii) Optimal modularization scheme of MW due to the nonlinear relationship between processing capability and ammonia concentration, (iii) The optimal selection of technologies with respect to various decision-making objectives.

Reference:

[1] Lakshmanan, A., & Biegler, L. T. (1996). Synthesis of optimal chemical reactor networks. Industrial & engineering chemistry research, 35(4), 1344-1353.

[2] Bertran, M. O., Frauzem, R., Zhang, L., & Gani, R. (2016). A generic methodology for superstructure optimization of different processing networks. In Computer Aided Chemical Engineering (Vol. 38, pp. 685-690). Elsevier.

[3] Araia, A., Wang, Y., Robinson, B., Jiang, C., Brown, S., Wildfire, C., ... & Hu, J. (2022). Microwave-assisted ammonia synthesis over Cs-Ru/CeO2 catalyst at ambient pressure: Effects of metal loading and support particle size. Catalysis Communications, 170, 106491.

[4] Henao, C. A., & Maravelias, C. T. (2011). Surrogate‐based superstructure optimization framework. AIChE Journal, 57(5), 1216-1232.

[5] Hugo, A., & Pistikopoulos, E. N. (2005). Environmentally conscious long-range planning and design of supply chain networks. Journal of Cleaner Production, 13(15), 1471-1491.