An automated method has been developed to fully characterize the three-dimensional structure of zeolite porous networks. The proposed optimization-based approach starts with the crystallographic coordinates of a structure and identifies all portals, channels, and cages in a unit cell, as well as their connectivity. We apply our algorithms to known zeolites, hypothetical zeolites, and zeolite-like structures and use the characterizations to calculate important quantities such as pore size distribution, accessible volume, surface area, and largest cavity and pore limiting diameters. We aggregate this data over many framework types to gain insights about zeolite selectivity. Finally, we develop a continuous-time Markov chain model to estimate the probability of occupancy of adsorption sites throughout the porous network. ZEOMICS, an online database of structure characterizations and web tool for the automated approach is freely available to the scientific community (http://helios.princeton.edu/zeomics/).

A hierarchical computational approach is introduced that combines materials screening with process optimization. This approach leads to novel materials for cost-effective CO2 capture. Zeolites are screened using shape, size, and adsorption selectivities. Next, process optimization is introduced to generate a rank-ordered list based on total cost of capture and compression. We not only select the most cost-effective materials, but we also attain the optimal process conditions while satisfying purity, recovery, and other process constraints. The top ten zeolites (AHT, NAB, MVY, ABW, AWO, WEI, VNI, TON, OFF and ITW) can capture and compress CO2 to 150 bar from a mixture of 14% CO2 and 86% N2 at less than $30 per ton of CO2 captured. Several zeolites have moderate selectivities, yet they cost-effectively capture CO2 with 90% purity and 90% recovery using a 4-step adsorption process. Such nonintuitive selection demonstrates the necessity of combining materials-centric and process-centric viewpoints.

a b s t r a c tWe present a multi-scale framework for the optimal design of CO 2 capture, utilization, and sequestration (CCUS) supply chain network to minimize the cost while reducing stationary CO 2 emissions in the United States. We also design a novel CO 2 capture and utilization (CCU) network for economic benefit through utilizing CO 2 for enhanced oil recovery. Both the designs of CCUS and CCU supply chain networks are multi-scale problems which require decision making at material, process and supply chain levels. We present a hierarchical and multi-scale framework to design CCUS and CCU supply chain networks with minimum investment, operating and material costs. While doing so, we take into consideration the selection of source plants, capture processes, capture materials, CO 2 pipelines, locations of utilization and sequestration sites, and amounts of CO 2 storage. Each CO 2 capture process is optimized, and the best materials are screened from large pool of candidate materials. Our optimized CCUS supply chain network can reduce 50% of the total stationary CO 2 emission in the U.S. at a cost of $35.63 per ton of CO 2 captured and managed. The optimum CCU supply chain network can capture and utilize CO 2 to make a total profit of more than 555 million dollars per year ($9.23 per ton). We have also shown that more than 3% of the total stationary CO 2 emissions in the United States can be eliminated through CCU networks at zero net cost. These results highlight both the environmental and economic benefits which can be gained through CCUS and CCU networks. We have designed the CCUS and CCU networks through (i) selecting novel materials and optimized process configurations for CO 2 capture, (ii) simultaneous selection of materials and capture technologies, (iii) CO 2 capture from diverse emission sources, and (iv) CO 2 utilization for enhanced oil recovery. While we demonstrate the CCUS and CCU networks to reduce stationary CO 2 emissions and generate profits in the United States, the proposed framework can be applied to other countries and regions as well.

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