Abstract: Ranking materials for post combustion carbon capture by characterizing sorbent materials with a molecular model workflow that generates microscopic figures of merit for materials by microscopic properties; and evaluating the materials from the molecular model workflow with a process model workflow that generates macroscopic figures of merit for process steps of a carbon recovery process. The materials for applicability as a sorbent material are ranked using a combined microscopic performance and macroscopic process feasibility generator that ranks the materials according to the microscopic figures of merit for materials and the macroscopic figures of merit for the process steps.

Abstract: The present disclosure provides methods for determining adsorption isotherms for complex mixtures. In at least one embodiment, a method for obtaining adsorption isotherms for liquid mixtures includes providing a column comprising an adsorbent. The method includes delivering a composition to the column, the composition comprising a multi-component feed and a solvent. The method includes collecting a sample from the column and introducing the sample to a two dimensional gas chromatograph to determine a time-series concentration of one or more components of the sample. The method includes integrating the time-series concentration of at least one of the one or more components to determine an isotherm of the at least one component. The method includes obtaining quantitative information of the at least one component, based on the isotherm of the at least one component.

Abstract: A decision support tool to assist decision-making in the operation of a facility. The decision support tool allows a user to perform operational programming of the facility so that the analysis of specific operational events is flexible and “on-demand.” This is achieved by allowing the user to set scope and timeframe of the analysis according to business needs. As a result, the organization can make better-informed judgments whether to add operational activities and how to adjust the ones already formulated in the program. The decision support tool can also allow for the application of strategies to communicate intent behind the programming instructions and to improve the quality of business decisions.

Abstract: A decision support tool to assist decision-making in the operation of a facility. The decision support tool allows a user to perform operational programming of the facility so that the analysis of specific operational events is flexible and “on-demand.” This is achieved by allowing the user to set scope and timeframe of the analysis according to business needs. As a result, the organization can make better-informed judgments whether to add operational activities and how to adjust the ones already formulated in the program. The decision support tool can also allow for the application of strategies to communicate intent behind the programming instructions and to improve the quality of business decisions.

Abstract: A decision support tool to assist decision-making in the operation of a facility. The decision support tool allows a user to perform planning and scheduling of the events within a facility so that established economic goals do not collide with feasibility of a schedule. This is achieved by flexible time modeling, which introduces a “look-ahead” planning and scheduling technique. This technique analyzes several time periods of the schedule in light of upcoming dominant events, in order for each segment of the schedule to remain as consistent with the planning objectives as possible.

Abstract: A decision support tool to assist decision-making in the operation of a facility. The decision support tool allows a user to perform operational programming of the facility so that the analysis of specific operational events is flexible and “on-demand.” This is achieved by allowing the user to set scope and timeframe of the analysis according to business needs. As a result, the organization can make better-informed judgments whether to add operational activities and how to adjust the ones already formulated in the program. The decision support tool can also allow for the application of strategies to communicate intent behind the programming instructions and to improve the quality of business decisions.

Abstract: A raw material valuation tool to assist purchasing decisions in the operation of a facility. The decision support tool allows a user to apply a modeling and analysis framework for a raw material valuation process. This optimization model allows raw material purchasing decisions to be divided into scenarios ahead of time, thereby addressing operational and market uncertainties of events that occur between the initial planning/scheduling and the final arrival of the raw materials at the facility. Price and availability data of a set of raw materials are input into the optimization model, including probability of occurrence of such data. The model calculates an optimal raw material purchasing scenario, which extends up to a moment in time when the raw material is used at the facility. The flexibility of this optimization model increases revenue generated at the facility, decreases cost of the raw material and improves operational decisions.

Abstract: A decision support tool to assist decision-making in the operation of a facility. The decision support tool allows a user to perform planning and scheduling of the events within a facility so that established economic goals do not collide with feasibility of a schedule. This is achieved by flexible time modeling, which introduces a “look-ahead” planning and scheduling technique. This technique analyzes several time periods of the schedule in light of upcoming dominant events, in order for each segment of the schedule to remain as consistent with the planning objectives as possible.

Abstract: A decision support tool to assist decision-making in the operation of a facility. The decision support tool allows a user to perform operational programming of the facility so that the analysis of specific operational events is flexible and “on-demand.” This is achieved by allowing the user to set scope and timeframe of the analysis according to business needs. As a result, the organization can make better-informed judgments whether to add operational activities and how to adjust the ones already formulated in the program. The decision support tool can also allow for the application of strategies to communicate intent behind the programming instructions and to improve the quality of business decisions.

Abstract: Systems and methods for creating a model of composition for a current composite created by combining a plurality of hydrocarbon streams. The method comprises receiving at least one primary data value related to the current composite, associating the at least one primary data value with a profile of the current composite, selecting a modeling scenario for the current composite using a hierarchy of modeling scenarios, and creating a model of composition for the current composite using at least one processor, The modeling scenario is selected based at least in part on a collection of primary data values associated with the profile of the current composite. The model of composition is created based at least in part on the modeling scenario and the collection of primary data values associated with the profile of the current composite. Systems of creating the model of composition are also disclosed.

Abstract: A method for determining the composition of a material including the steps of fitting multivariate analytical data of the material to a combination of multivariate analytical data in a database to determine coefficients of the combination so as to determine a reference model of composition based on the coefficients and the compositions in the database, wherein the database includes multivariate analytical data of database materials whose compositions are known, and reconciling the reference model of composition to match properties of the material.

Abstract: A method for determining the composition of a material including the steps of fitting multivariate analytical data of the material to a combination of multivariate analytical data in a database to determine coefficients of the combination so as to determine a reference model of composition based on the coefficients and the compositions in the database, wherein the database includes multivariate analytical data of database materials whose compositions are known, and reconciling the reference model of composition to match properties of the material.