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.