Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Chemical analysis
Reexamination Certificate
2002-02-05
2004-07-13
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Chemical analysis
C702S030000, C702S081000, C702S182000, C436S037000, C436S147000, C436S159000, C700S266000, C700S268000
Reexamination Certificate
active
06763309
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to materials development methods, and, more particularly to a process for the rational development of materials used in chemical processes, including but not limited to heterogeneous catalysts. When applied to heterogeneous catalysts, this process may be referred to as a catalyst development engine (CDE).
BACKGROUND OF THE INVENTION
The still emerging, recent application of combinatorial chemistry to high-speed high throughput synthesis and screening of materials does not adequately address the commercial requirements that a catalyst must meet. Current combinatorial methods are based on random screening of large libraries of materials, prepared and evaluated under unrealistic conditions that are difficult to scale up. Thus, little useful knowledge is derived from such experiments to guide the selection of the next set of experiments or materials and to scale up the material. A different approach for catalytic material discovery and development is needed in order to reduce the time to market which includes scalable high-throughput methods for catalyst synthesis and real-world conditions catalyst evaluation to accelerate generation of useful data coupled with a process that maximizes learning from these data and rapidly and efficiently identifies new material candidates. This knowledge driven process uses integrated scientific and empirical modeling tools to complement and mine experimental data in order to build predictive models that the scientist can use to guide material selection. This knowledge process for rational (as opposed to random) material discovery and development is the subject of this invention.
SUMMARY OF THE INVENTION
The present invention comprises a research process, preferably computer-assisted, for use by the scientist to guide the selection of new materials and accelerate the rational development of materials. This system comprises a Knowledge Cycle™ (KC), a Testing Cycle™ (TC) and a knowledge management system. The KC comprises data-based and science-based modeling tools that are integrated in a knowledge management system, in order to maximize learning and enhance the scientist's decision-making capabilities for efficient experiment planning.
Additional advantages of the invention will be set forth in part in the description which follows, and in part will be learned from the description, or may be learned by practice of the invention. The advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims.
The above and other features and advantages are achieved through the use of a novel material development process as herein disclosed. In accordance with one embodiment of the present invention, a method of identifying chemical reaction mechanisms for a chemical process includes the step of specifying a reactant set that includes a plurality of chemical substances that may engage in a chemical reaction with one or more other substances in the reactant set. It also includes the step of specifying a plurality of possible products that may result from the reaction of two or more of the substances included in the reactant set. It also includes the step of identifying a reaction mechanism set that includes a plurality of reaction mechanisms, wherein each reaction mechanism is a combination of two or more elementary steps representing the chemical process. It also includes the step of selecting a plurality of catalytic materials, where each catalytic material is associated with at least one of the reaction mechanisms in the reaction mechanism set, and each catalytic material is also associated with experimental data. It also includes the steps of associating a kinetic constant value with each elementary step of each reaction mechanism, as well as generating a kinetic model associated with each reaction mechanism and each catalytic material. Further, it includes the step of using a processing device to screen the reaction mechanism set by applying a goodness of fit test to the experimental data associated with each catalyst, eliminating the reaction mechanisms having a worst fit, and grouping the remaining reaction mechanisms associated with each catalytic material to provide a first reaction mechanism subset for each catalytic material.
Optionally, the method also includes the steps of selecting a performance variable and, for the reaction mechanisms contained in the first reaction mechanism subset, identifying one or more associated kinetic parameters to which the performance variable is most sensitive.
The method of may also include the steps of using a processing device to calculate a modeled kinetic constant for a plurality of the elementary steps associated with a plurality of the reaction mechanisms using the processing device to screen the first reaction mechanism subset by eliminating the reaction mechanisms having associated kinetic constants that least closely relate to their corresponding modeled kinetic constants, and associating the remaining reaction mechanisms not eliminated in the second screening step with a second reaction mechanism subset. In this option, the calculating step may comprise using molecular modeling to calculate the modeled kinetic constant. Also, the option may include the additional steps of selecting a performance variable, and, for the reaction mechanisms contained in the second reaction mechanism subset, identifying one or more associated kinetic parameters to which the performance variable is most sensitive.
In accordance with an alternate embodiment of the invention, a method of identifying materials for the performance of a chemical process includes the step of selecting a data set for a set of materials. The data set includes one or more dependent performance variables for a chemical process, as well as independent variables including, but not limited to, calculated or measured properties of the materials or preparation parameters relating to the materials. The method also includes the step of building a model that correlates the dependent performance variables with one or more of the independent variables, as well as the step of identifying one or more of the independent variables having values that yield improved values of the dependent performance variables based on the results of the model built in the building step. Further, the method includes the step of identifying one or more new materials that are associated with the values of the one or more independent variables that yield improved values of the dependent variables.
Optionally, in this embodiment, the step of building a model comprises the use of recursive partitioning. Also optionally, one or more dependent performance variables or one or more independent variables may comprise kinetic parameters that have been associated with reaction mechanisms in a reaction mechanism set. Also optionally, the method may include the steps of applying a Monte Carlo kinetic simulation to calculate at least one modeled performance parameter for each material included in the material set, and selecting at least one materials class based on the results of the Monte Carlo simulation.
Further, the method may include the steps of (i) selecting a selected reaction mechanism from a reaction mechanism set, wherein each reaction mechanism in the set comprises a combination of two or more elementary steps in a chemical process; (ii) applying a Monte Carlo kinetic simulation to calculate at least one modeled performance parameter for each material identified in the identifying step, wherein the simulation is associated with the selected reaction mechanism; and (iii) selecting at least one materials class based on the results of the Monte Carlo simulation. With this option, each reaction mechanism in the reaction mechanism set may have been screened, using a goodness of fit test, to eliminate reaction mechanisms for which experimental data associated with reaction mechanism catalysts has been determined to have a poor fit. Each reaction
Iglesia Enrique
Kieken Laurent
Neurock Matthew
Trenkle John Matthew
Desta Elias
Hoff Marc S.
NovoDynamics, Inc.
Pepper Hamilton LLP
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