# US 2022/0284373 A1 · System and method for just in time characterization of raw materials > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0284373 A1 - Document ID: US20220284373A1 - Status: Published application - Kind: A1 - Application number: 17/753,109 - Filed: 2020-08-20 - Published: 2022-09-08 - Earliest claimed: 2019-08-20 - Assignees: Tata Consultancy Services Limited - Applicants: Tata Consultancy Services Limited - Inventors: ANIRUDH MAKARAND DEODHAR; ABHISHEK BAIKADI; SRIHARSHA NISTALA; RAJAN KUMAR; ASHIT GUPTA; SIVAKUMAR SUBRAMANIAN; VENKATARAMANA RUNKANA; ROHAN PANDYA - Web page: https://patentgrep.com/patent/US20220284373A1 ## CPC - G06Q 10/06395: {Quality analysis or management} - G06N 20/00: Machine learning - G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) - G06Q 10/067: Enterprise or organisation modelling - G06Q 10/0674: {using process simulation} - G06Q 50/04: Manufacturing - Y02P 90/30: Computing systems specially adapted for manufacturing - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS ## Abstract In an industrial plant, various equipment are used to handle processing of raw materials. Considering complexities involved in the processes and the equipment, constant monitoring is required to obtain desired results. The disclosure herein generally relates to industrial process and equipment monitoring, and, more particularly, to data analysis for Just In Time (JIT) characterization of raw materials in any process industry. The system collects real-time plant data among other inputs, and performs characterization of raw materials being used in the plant. The characterization involves categorizing the raw materials into different classes. The class information is further used to predict performance of the industrial plant, and in turn to generate recommendations for optimization of the industrial plant. ## Claims ### Claim 1 A processor implemented method for characterization of materials based on plant data, comprising: receiving the plant data from an industrial plant as input, via one or more hardware processors; determining, by processing the plant data via the one or more hardware processors, change in one or more raw materials used in the industrial plant, wherein the change in the one or more raw materials is detected at least at a plant level or an equipment level; determining at least one class that matches each of the one or more raw materials, using at least one material class identification model, via the one or more hardware processors, wherein the determined at least one class is a newly defined class or is from a set of pre-defined classes; predicting material characteristics for each of the one or more raw materials, via the one or more hardware processors; selecting at least one of a plurality of predictive models associated with at least one of the predicted material characteristics and the determined at least one class of the one or more raw materials, via the one or more hardware processors; predicting performance of the industrial plant, using the selected at least one predictive model, via the one or more hardware processors; and generating at least one recommendation to optimize performance of the industrial plant based on the predicted performance, if an obtained actual performance of the industrial plant is below a threshold of performance, via the one or more hardware processors. ### Claim 2 (depends on 1) The method as claimed in claim 1, wherein determining the change in the one or more raw materials comprises: pre-processing the plant data, wherein the plant data comprises at least one of a) data collected from the industrial plant sensors, b) laboratory data based on sampled measurements of material characteristics or material batch information, and c) soft sensors and synthetic data generated through a plurality of simulators, d) plant design and maintenance data, and e) environment data; filtering preprocessed plant data; and determining the change in one or more of the raw materials based on a change in an observed pattern of the filtered plant data, for at least one equipment. ### Claim 3 (depends on 1) The method as claimed in claim 1, wherein determining the at least one class matching the raw material comprises: identifying one of a plurality of pre-existing classes as a matching class, if the plant data of the raw material are matching plant data associated with the class; and defining a new class if no match is found for the plurality of plant data of the raw material in any of the pre-existing classes, and determining the new class as the class matching the plurality of plant data associated with the raw material. ### Claim 4 (depends on 3) The method as claimed in claim 3, wherein determining the newly defined class as the at least one class matching the at least one raw material comprises: determining that plant data associated with at least one of the raw materials do not match with plant data associated with any of the pre-defined classes; extracting characteristics of the at least one raw material, from the plant data; and defining the new class using the extracted characteristics and associated plant data of the at least one raw material. ### Claim 5 (depends on 1) The method as claimed in claim 1, wherein determining the at least one class comprises: determining class of each of the raw materials used in the industrial plant at the time of monitoring the industrial plant; and updating class information if any of the at least one of the raw materials has transitioned from one form to another, wherein the transition from one form to another is determined by monitoring the at least one raw material over a period of time. ### Claim 6 (depends on 1) The method as claimed in claim 1, wherein information on a plurality of the classes is stored in a database, wherein each of the plurality of classes comprises material characteristics, plant data, predictive models associated with the class, a plurality of optimum settings associated with the class, clustering information, extracted classification rules and one or more secondary information. ### Claim 7 A system for data characterization of materials based on plant data, comprising: one or more hardware processors; a communication interface; and a memory storing a plurality of instructions, wherein the plurality of instructions when executed, cause the one or more hardware processors to: receive the plant data from an industrial plant as input; determine, by processing the plant data, change in one or more raw materials used in the industrial plant, wherein the change in the one or more raw materials is detected at least at a plant level or an equipment level; determine at least one class that matches each of the one or more raw materials, using at least one material class identification model, wherein the determined at least one class is a newly defined class or is from a set of pre-defined classes; predict material characteristics for each of the one or more raw materials; select at least one of a plurality of predictive models associated with at least one of the predicted material characteristics and the determined at least one class of the one or more raw materials; predict performance of the industrial plant, using the selected at least one predictive model; and generate at least one recommendation to optimize performance of the industrial plant based on the predicted performance, if a measured actual performance of the industrial plant is below a threshold of performance. ### Claim 8 (depends on 7) The system as claimed in claim 7, wherein the system determines the change in the one or more raw materials by: pre-processing the plant data, wherein the plant data comprises at least one of a) data collected from the industrial plant sensors, b) laboratory data based on sampled measurements of material characteristics or material batch information , and c) soft sensors and synthetic data generated through a plurality of simulators, d) plant design and maintenance data, and e) environment data; filtering preprocessed plant data; and determining the change in one or more of the raw materials based on a change in an observed pattern of the filtered plant data, for at least one equipment. ### Claim 9 (depends on 7) The system as claimed in claim 7, wherein the system determines the at least one class matching the raw material by: identifying one of a plurality of pre-existing classes as a matching class, if the plant data of the raw material are matching plant data associated with the class; and defining a new class if no match is found for the plurality of plant data of the raw material in any of the pre-existing classes, and determining the new class as the class matching the plurality of plant data associated with the raw material. ### Claim 10 (depends on 9) The system as claimed in claim 9, wherein the system determines the newly defined class as the at least one class matching the at least one raw material by: determining that plant data associated with at least one of the raw materials do not match with plant data associated with any of the pre-defined classes; extracting characteristics of the at least one raw material, from the plant data; and defining the new class using the extracted characteristics and associated plant data of the at least one raw material. ### Claim 11 (depends on 7) The system as claimed in claim 7, wherein the system determines the at least one class by: determining class of each of the raw materials used in the industrial plant at the time of monitoring the industrial plant; and updating class information if any of the at least one of the raw materials has transitioned from one form to another, wherein the transition from one form to another is determined by monitoring the at least one raw material over a period of time. ### Claim 12 (depends on 7) The system as claimed in claim 7, wherein the system stores information on a plurality of the classes in a database, wherein each of the plurality of classes comprises material characteristics, plant data, predictive models associated with the class, a plurality of optimum settings associated with the class, clustering information, extracted classification rules, and one or more secondary information. ### Claim 13 A non-transitory computer readable medium for characterization of materials based on plant data, the non-transitory computer readable medium performs the characterization of the materials by: receiving the plant data from an industrial plant as input, via one or more hardware processors; determining, by processing the plant data via the one or more hardware processors, change in one or more raw materials used in the industrial plant, wherein the change in the one or more raw materials is detected at least at a plant level or an equipment level; determining at least one class that matches each of the one or more raw materials, using at least one material class identification model, via the one or more hardware processors, wherein the determined at least one class is a newly defined class or is from a set of pre-defined classes; predicting material characteristics for each of the one or more raw materials, via the one or more hardware processors; selecting at least one of a plurality of predictive models associated with at least one of the predicted material characteristics and the determined at least one class of the one or more raw materials, via the one or more hardware processors; predicting performance of the industrial plant, using the selected at least one predictive model, via the one or more hardware processors; and generating at least one recommendation to optimize performance of the industrial plant based on the predicted performance, if an obtained actual performance of the industrial plant is below a threshold of performance, via the one or more hardware processors. ### Claim 14 (depends on 13) The non-transitory computer readable medium as claimed in claim 13, wherein determining the change in the one or more raw materials comprises: pre-processing the plant data, wherein the plant data comprises at least one of a) data collected from the industrial plant sensors, b) laboratory data based on sampled measurements of material characteristics or material batch information, and c) soft sensors and synthetic data generated through a plurality of simulators, d) plant design and maintenance data, and e) environment data; filtering preprocessed plant data; and determining the change in one or more of the raw materials based on a change in an observed pattern of the filtered plant data, for at least one equipment. ### Claim 15 (depends on 13) The non-transitory computer readable medium as claimed in claim 13, wherein determining the at least one class matching the raw material comprises: identifying one of a plurality of pre-existing classes as a matching class, if the plant data of the raw material are matching plant data associated with the class; and defining a new class by extracting characteristics of the at least one raw material, if no match is found for the plurality of plant data of the raw material in any of the pre-existing classes, and determining the new class as the class matching the plurality of plant data associated with the raw material. ### Claim 16 (depends on 13) The non-transitory computer readable medium as claimed in claim 13, wherein information on a plurality of the classes is stored in a database, wherein each of the plurality of classes comprises material characteristics, plant data, predictive models associated with the class, a plurality of optimum settings associated with the class, clustering information, extracted classification rules and one or more secondary information.