Eric L. First

Predictive Framework for Shape-Selective Separations in Three-Dimensional Zeolites and Metal–Organic Frameworks

With the growing number of zeolites and metal-organic frameworks (MOFs) available, computational methods are needed to screen databases of structures to identify those most suitable for applications of interest. We have developed novel methods based on mathematical optimization to predict the shape selectivity of zeolites and MOFs in three dimensions by considering the energy costs of transport through possible pathways. Our approach is applied to databases of over 1800 microporous materials including zeolites, MOFs, zeolitic imidazolate frameworks, and hypothetical MOFs. New materials are identified for applications in gas separations (CO2/N2, CO2/CH4, and CO2/H2), air separation (O2/N2), and chemicals (propane/propylene, ethane/ethylene, styrene/ethylbenzene, and xylenes).

A multi-scale framework for CO 2 capture, utilization, and sequestration: CCUS and CCU

Abstract We 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

Stereochemically consistent reaction mapping and identification of multiple reaction mechanisms through integer linear optimization.

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 (

A multi-scale approach for the discovery of zeolites for hydrogen sulfide removal

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.

Estimation of diffusion anisotropy in microporous crystalline materials and optimization of crystal orientation in membranes

Reaction mappings are of fundamental importance to researchers studying the mechanisms of chemical reactions and analyzing biochemical pathways. We have developed an automated method based on integer linear optimization, ILP, to identify optimal reaction mappings that minimize the number of bond changes. An alternate objective function is also proposed that minimizes the number of bond order changes. In contrast to previous approaches, our method produces mappings that respect stereochemistry. We also show how to locate multiple reaction mappings efficiently and determine which of those mappings correspond to distinct reaction mechanisms by automatically detecting molecular symmetries. We demonstrate our techniques through a number of computational studies on the GRI-Mech, KEGG LIGAND, and BioPath databases. The computational studies indicate that 99% of the 8078 reactions tested can be addressed within 1 CPU hour. The proposed framework has been incorporated into the Web tool DREAM ( http://selene.princeton.edu/dream/ ), which is freely available to the scientific community.

A Novel Framework for Carbon Capture, Utilization, and Sequestration, CCUS

Abstract Removing H2S from industrial gases is important to avoid operational hazards and to meet environmental regulation. Microporous zeolites are potential adsorbents for separating H2S from other gases. While large number of candidate zeolites exists, it is not trivial to select cost-effective zeolites capable of satisfying process constraints and specifications. In this work, a novel method for the zeolite-based H2S separation is put forward which pertains to a multi-scale modeling, simulation, and optimization framework for combined material screening and process optimization to reduce the overall process cost. The framework spans the atomistic and mesoscopic scales for the screening and selection of zeolites and the macroscopic scale for the simulation and selection of optimal conditions for pressure swing adsorption (PSA)-based H2S separation technology. Applying this framework, several novel zeolites have been identified for the first time for separation of H2S from representative H2S/CO2, H2S/N2, and H2S/CH4 mixtures. The zeolites which are screened are capable of removing H2S from natural gas, acid gas, tail gas, flue gas, refinery gas, biogas, landfill gas, and other gases of industrial importance. Results show that it is possible to perform cost-effective H2S removal by exploiting reverse selectivity of the gas molecules using novel micro-porous materials. We have also identified zeolite ABW as an adsorbent with high potential for commercialization for multi-purpose gas separation including acid gas removal from natural gas and carbon capture from power plants.

Discovery of New Zeolites for H2S Removal through Multi-scale Systems Engineering

The complex nature of the porous networks in microporous materials is primarily responsible for a high degree of intracrystalline diffusion anisotropy. Although this is a well-understood phenomenon, little attention has been paid in the literature with regards to classifying such anisotropy and elucidating its effect on the performance of membrane-based separation systems. In this paper, we develop a novel methodology to estimate full diffusion tensors based on the detailed description of the porous network geometry through our recent advances for the characterization of such networks. The proposed approach explicitly accounts for the tortuosity and complex connectivity of the porous framework, as well as for the variety of diffusion regimes that may be experienced by a guest molecule while it travels through the different localities of the crystal. Results on the diffusion of light gases in silicalite demonstrate good agreement with results from experiments and other computational techniques that have been reported in the literature. A comprehensive computational study involving 183 zeolite frameworks classifies these structures in terms of a number of anisotropy metrics. Finally, we utilize the computed diffusion tensors in a membrane optimization model that determines optimal crystal orientations. Application of the model in the context of separating carbon dioxide from nitrogen demonstrates that optimizing crystal orientation can offer significant benefit to membrane-based separation processes.

Computational characterization of zeolite porous networks: an automated approach

Abstract Recent global warming and climate change are often attributed to anthropogenic CO 2 emissions from burning and consumption of fossil fuels. CO 2 capture, utilization and sequestration (CCUS) is an enabling technology toward reducing such emissions from stationary sources. However, significant challenges remain to be addressed before CCUS can be deployed at the industrial scale. A major challenge is to reduce the overall cost of CCUS. To this end, we apply a multi-scale approach and provide a comprehensive framework to elucidate materials-centric, process-centric and network-centric understanding toward reducing the overall CCUS cost. At the materials level, a hierarchical in silico screening method is developed to select the candidate adsorbent materials and optimize process conditions in tandem for adsorption-based postcombustion CO 2 capture. At the process level, detailed cost-based modeling and optimization of different capture processes are performed, which enable us to develop explicit expressions for the investment and operating costs of capture technologies, and to determine the most cost-effective materials and processes to be used for CO 2 capture and compression when addressing diverse emission scenarios. At the network level, we design an optimal nationwide CCUS structure that uses the most appropriate source plants, capture technologies and materials, transportation network, and CO 2 utilization and storage sites. We also discuss the factors that affect the CCUS network costs.

MOFomics: Computational pore characterization of metal–organic frameworks

Abstract Removal of H2S from industrialgas mixturesis important to avoid operational hazards, and to meet stringent environmental regulationto curb H2S emission. Zeolites are microporous materialswhich have shown excellent potential as adsorbents for molecular gas separation, and can be applied for separating H2S from other gases. We apply a multi-scale systems engineering framework to discover or identify highly selective, feasible and cost-effective zeolites for pressure swing adsorption (PSA)-based H2S separation. Using the in silico framework, several new and cost-effective zeolites are identified for adsorption-based H2S separation. We demonstrate the applicability of the multi-scale approach for H2S separation fromrepresentative binary gas mixtures such as acid gas (H2S/CO2), tail gas (H2S/N2), and natural gas (H2S/CH4). A key component of the proposed approach is an efficient and hierarchical computational screening that combines selection of materials with advanced process optimization. We anticipate that this approach is suitable for the discovery of novel materials for other molecular gas separation of industrial importance.