Abstract: A method and system for training physics-informed operators with dual hypernetwork module and low-rank domain decomposition. The method includes identifying availability of information related to system behavior and geometry complexity. The method further includes dynamically determining training strategy for the PINO based on the identification. The training strategy includes at least one of a soft domain decomposition process or a hard domain decomposition process. The method includes determining whether a size of a hypernetwork output layer exceeds a first predefined threshold. The method includes generating a set of training points if the size does not exceed the first predefined threshold. The method includes iteratively training the PINO using the determined strategy based on the set of training points until an error falls below a second predefined threshold.

Abstract: A method and system for designing curing processes is disclosed.

Abstract: Current approaches for identifying accretion in rotary kiln lack access to information regarding the internal condition of the rotary kiln such as temperatures of the wall, gas or solid bed and specific methodology that is required to calculate accretion and hence forecast. Present disclosure provides method and system for forecasting and diagnosing accretion in rotary kiln. The system first takes historical data associated with rotary kiln, real-time data, and a future time horizon information. Then, system predicts accretion scores for future time horizon based on received data using a pretrained accretion forecasting model which are further utilized to estimate a rate of accretion. Thereafter, the system identifies high accretion (HA) operating regime and low accretion (LA) operating regime over predefined time period. Further, system identifies one or more accretion variables responsible for causing each of the HA operating regime and the LA operating regime using an accretion diagnostic model.

Abstract: As discussed earlier, accretion happens to be the most critical problem faced by users of the rotary kiln as it leads to shutdown of the rotary kiln which ultimately leads to reduction in production. Currently available accretion monitoring systems require expensive equipment and sensors which increases the production cost. Present disclosure provides method and system for monitoring accretion happening inside the rotary kiln. The system first takes real-time data associated with a rotary kiln as input. The system then localizes accretion clusters present in the rotary kiln using an accretion localization model. Thereafter, the system estimates the accretion probability score based on the HTM statistics calculated based on the accretion cluster information and real-time data using an accretion scoring model. Further, the system compute accretion score representative of real-time accretion condition of the rotary kiln based on the accretion probability score and the inputs.

Abstract: A method and system for optimizing performance of Genetic Algorithm (GA) in solving scheduling problem is disclosed. The method includes receiving input constraints associated with supply and demand sides, for scheduling problem. The method include initializing set of schedules using initializer that sets initial set of solutions for GA to start optimization. The method may include generating parent population for GA. The method may include creating child population via evolution using current probabilistic parameters including crossover and mutation operators. The method may include utilizing a Multi-Level Hierarchical Grouping (MLHG) to de-duplicate child population. The method includes determining a new population from a total population including the parent population and the child population, using custom multi-objective sorting technique. The method may further include updating probabilistic parameters of the GA during runtime using runtime adapter, when pre-determined iterations unattained.

Abstract: A method and system for updating prediction model for curing process design is disclosed. The method includes updating a prediction model based on the input through a semi-supervised learning technique. The method may include receiving an input corresponding to a curing process. The method may further include generating a second set of experiments using the updated prediction model and an optimization component associated with the prediction model. The method may further include obtaining a second set of data upon performing a second set of experiments on a physical set-up. The method may further include determining an error between the predicted set of data and the second set of data. The method may further include updating the prediction model based on the second set of data when the error is out of a predefined threshold.

Abstract: The present invention discloses a system for scheduling jobs within a manufacturing environment, integrating a plurality of sensors to capture operational parameters. A processing unit, linked to the plurality of sensors, analyzes datasets to determine the operational parameters. A machine learning module coupled to the processing unit, enhances a scheduling algorithm using the operational parameters. This machine learning module includes a first predictor for estimating job processing times and forecasting operating conditions based on these parameters. A formulator adjusts the scheduling algorithm using the forecasted operating conditions for distinct time intervals. Additionally, a second predictor forecasts subsequent operating conditions based on the modified scheduling algorithm and initial forecasts. The system optimizes job scheduling, enhancing efficiency and productivity within the manufacturing environment.

Abstract: A method and system for hypernetwork guided domain decomposition in Physics-Informed Neural Operators (PINOs). The method includes identifying a presence of at least one discontinuity in data associated with a target domain to be analyzed by a PINO. Identification of the presence is at least of a successful identification or an unsuccessful identification. The method further includes generating a plurality of sub-domains for the target domain. The plurality of sub-domains is generated uniformly upon the unsuccessful identification, and the plurality of sub-domains is generated based on a predefined discontinuity criteria upon the successful identification. The method further includes generating intra-subdomain points with respect to each of the plurality of sub-domains. The method further includes extracting a plurality of sub-domain identifiers, upon generating the intra-subdomain points, using a pre-defined feature extraction technique.

Abstract: This disclosure relates generally to a method and system for real time monitoring and forecasting of fouling of an air preheater (APH) in a thermal power plant. The system is deploying a digital replica or digital twin that works in tandem with the real APH of the thermal power plant. The system receives real-time data from one or more sources and provides real-time soft sensing of intrinsic parameters as well as that of health, fouling related parameters of APH. The system is also configured to diagnose the current class of fouling regime and the reasons behind a specific class of fouling regime of the APH. The system is also configured to be used as advisory system that alerts and recommends corrective actions in terms of either APH parameters or parameters controlled through other equipment such as selective catalytic reduction or boiler or changes in operation or design.

Abstract: Physics informed machine learning faces challenges in real industrial environment and in digital twins where the systems are far more complex. Physics of the process is multi-dimensional, multi-material and multi-phenomena. Therefore, building physics informed machine learning models is challenging as it needs to be developed from scratch for each new system or for each significant change in the process/equipment. Once built, the models are not suitable for real-time dynamic changes in operating conditions. Therefore, embodiments herein provide a method and system that can enable quick development of Physics Informed Digital Twin (PIDT) models that are generalized, do not need re-training for dynamic conditions in the industrial plants, can learn from multiple data sources, equipment, and materials. On top of this, the method and system can enable Physics Informed Digital Twin (PIDT) models to learn and update themselves with minimal human intervention in digital twin environments.

Abstract: This disclosure relates to a method and system for hybrid data augmentation for estimating performance degradation in industrial plant. Performance degradation in industrial plants cannot be measured by sensors or laboratory measurements and there are no methods to annotate performance degradation state. The embodiments of the present disclosure provide a knowledge-based data augmentation that use physics based information to model performance degradation. The disclosed method augments high fidelity data with knowledge-based methods into high and low confidence data which are used to calculate performance score of high confidence data. A physics-informed machine learning model is trained on high confidence data. The resulting model is then used to predict performance score for low confidence data. The model is further used for training prognostics and diagnostics models to predict and identify root causes responsible for performance degradation.

Abstract: State of the art systems used for industrial plant monitoring have the disadvantage that they fail to correctly assess reason for dip in performance of the plant and in turn trigger appropriate corrective measures. The disclosure herein generally relates to industrial plant monitoring, and, more particularly, to a system and method for development and deployment of self-organizing cyber-physical systems for manufacturing industries. The system monitors and collects data with respect to various parameters, from the industrial plant. If any performance dip is detected, the system determines corresponding cause, and also triggers one or more corrective actions to improve performance of the plant and different plant components to a desired performance level.

Abstract: This disclosure relates generally to a method and system for real time monitoring and forecasting of fouling of an air preheater (APH) in a thermal power plant. The system is deploying a digital replica or digital twin that works in tandem with the real APH of the thermal power plant. The system receives real-time data from one or more sources and provides real-time soft sensing of intrinsic parameters as well as that of health, fouling related parameters of APH. The system is also configured to diagnose the current class of fouling regime and the reasons behind a specific class of fouling regime of the APH. The system is also configured to be used as advisory system that alerts and recommends corrective actions in terms of either APH parameters or parameters controlled through other equipment such as selective catalytic reduction or boiler or changes in operation or design.

Abstract: Pulverizers are very critical equipment in overall functioning of a plant. They need to be controlled and monitored properly for the optimized operation of the pulverizers. A system and method for performance and health monitoring to optimize operation of a pulverizer is provided. The system comprises a digital twin that can mimic the performance of the pulverizer in real-time and assist the operators in decision making related to operation, maintenance and scheduling. The digital twin is configured to receives real-time sensor data from a plurality of data sources and provides real-time soft sensing of key health and performance parameters of the pulverizer. One more key aspect of the solution is the advisory system that alerts and recommends corrective actions in terms of parameters controlled through other equipment or changes in operation or design or changes in cleaning schedule.

Abstract: Fouling is formation of deposits on the heat exchanger surfaces that adversely affects operation of heat exchanger. Fouling can be approximated through a set of estimated heat exchanger parameters, which may not be accurate, leading to uncertainty in operation/maintenance decisions and hence the losses. A system and a method for identification and forecasting fouling of a plurality of heat exchangers in a refinery has been provided. The system comprises a digital replica of the heat exchanger network. The digital replica is configured to receive real-time sensor data from a plurality of data sources and provides real-time soft sensing of key parameters. The system is also configured to diagnose the reasons behind a specific condition of fouling. Further, an advisory is provided, that alerts and recommends corrective actions. The system provides estimate for the remaining useful life (RUL) of the heat exchangers and suggests the cleaning schedule.

Abstract: This disclosure relates generally to conditioned spaces, and more particularly to a system and method for thermo-fluid management in the conditioned space. In one embodiment, the method includes retrieving geometry and operational information of the conditioned space from a conditioned space data. A 3D geometry of the conditioned space is automatically generated in a format suitable for a mesh generation model for numerical analysis by parsing the conditioned space data. A mesh is created within the 3D geometry using the mesh generation model. A simulation data is generated based at least on an operational data of the plurality of components. The simulation data is applied on the mesh to simulate a thermo-fluid model of the conditioned space.

Abstract: Disclosed is a method for real-time prediction of thermal-insights for a heat dissipating device in a data center cooled by one or more cooling units. The method uses a concept of influence mass fractions in conjunction with proper orthogonal decomposition (POD) based reduced order model. It may be understood that, the influence mass fractions may be computed by performing a fixed number of CFD simulations based on mass flow rates of the one or more cooling units. The method further facilitates to identify a set of reference scenarios for a given range of operational parameters of the one or more cooling units impacting the heat dissipating device. The set of reference scenarios may then be provided to the POD in order to predict the thermal-insights of the data center such as a temperature, mass flow rate, and insights into thermal influence of air sources on the heat dissipating device.

Abstract: This disclosure relates generally to conditioned spaces, and more particularly to a system and method for thermo-fluid management in the conditioned space. In one embodiment, the method includes retrieving geometry and operational information of the conditioned space from a conditioned space data. A 3D geometry of the conditioned space is automatically generated in a format suitable for a mesh generation model for numerical analysis by parsing the conditioned space data. A mesh is created within the 3D geometry using the mesh generation model. A simulation data is generated based at least on an operational data of the plurality of components. The simulation data is applied on the mesh to simulate a thermo-fluid model of the conditioned space.

Abstract: Disclosed is a method for real-time prediction of thermal-insights for a heat dissipating device in a data center cooled by one or more cooling units. The method uses a concept of influence mass fractions in conjunction with proper orthogonal decomposition (POD) based reduced order model. It may be understood that, the influence mass fractions may be computed by performing a fixed number of CFD simulations based on mass flow rates of the one or more cooling units. The method further facilitates to identify a set of reference scenarios for a given range of operational parameters of the one or more cooling units impacting the heat dissipating device. The set of reference scenarios may then be provided to the POD in order to predict the thermal-insights of the data center such as a temperature, mass flow rate, and insights into thermal influence of air sources on the heat dissipating device.