Tata Consultancy Services Limited

Anirudh Deodhar

Venkataramana Runkana

Rajan Kumar

Anand Sivasubramaniam

Sri Harsha Nistala

Pankaj Malhotra

Amarendra Kumar Singh

Aditya Pareek

Ashit Gupta

Sivakumar Subramanian

Mukul Patil

Harshad Girish Bhagwat

Saurabh Jaywant Desai

Muralikrishnan Ramanujam

Dilshad Ahmad

Abhishek Baikadi

Rohan Pandya

Anagha Nikhil Mehrotra

Tanmaya Singhal

Tathagata Mukherjee

Sai Prasad Parameswran

Joel Thomson Diraviam Andrew

Charan Theja Dhanda

Amarend Kumar Singh

Lalith Roshanlal Jain

Dhandapani Subramaniam

Sriharsha Nistala

Vishal Jadhav

Moksha Sunil Padsalgi

Hrishikesh Nilkanth Kulkarni

Ratnamala Manna

Nital Shah

Abhishek Krishnamoorthy Baikadi

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.

System and method for performance and health monitoring to optimize operation of a pulverizer mill

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.

System and method for just in time characterization of raw materials

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.

Method and system for realtime monitoring and forecasting of fouling of air preheater equipment

The ability to comprehend the context of a given programming artifact and extracting the underlying functionality is a complex task extending beyond just syntactic and semantic analysis of code. All existing automation capabilities, hence heavily depend on manual involvement of domain experts. Even recent approaches leveraging Machine Learning Capabilities are supervised techniques, whereby the dependency on domain experts still remains—in preparing suitable training sets. A method and system for automated classification of variables using unsupervised distribution agnostic clustering has been provided. The present disclosure focuses to tap the flexibility of the code and presents a domain agnostic approach using unsupervised machine learning which automatically extracts the context from source code, by classifying the underlying elements of the code. The method and system do not require any manual intervention and opens a wide range of opportunities in reverse engineering and variable level analysis space.

Method and system for monitoring and optimizing the operation of an alumina rotary kiln

Performance optimization of power plants is one of the major challenges. Several machine learning based techniques are available which are used for optimization of the power plants. Coal selection and blending is critical to ensuring optimum operation of thermal power plants. The present disclosure provides a system and method for optimum coal selection for the power plant and power plant optimization. The system mainly comprises two components. First, a coal usage advisory module providing coal usage and blending ratio advice to the operators based on the available coal. The optimization is with respect to the entire power plant operation including its components. And second, a performance optimization advisory module provides operation instruction for boiler, SCR, APH and other power plant equipment based on the implemented coal blend in real-time.

Method and system for optimum coal selection and power plant optimization

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.

System and method for thermo-fluid management of conditioned space

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.

System and method for predicting thermal-insights of a data center

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Pune, India

Anirudh Deodhar