Tata Consultancy Services Limited

Rajan Kumar

Venkataramana Runkana

Sri Harsha Nistala

Aditya Pareek

Anirudh Deodhar

Rohan Pandya

Abhishek Baikadi

Vishnu Swaroopji Masampally

Sivakumar Subramanian

Pradeep Rathore

Sagar Srinivas Sakhinana

Mahesh Mynam

Ashit Gupta

Anagha Nikhil Mehrotra

Tathagata Mukherjee

Duc Doan

Jayasree Biswas

Sriharsha Nistala

Manendra Singh Parihar

Pallavi Venugopal Minimol

Moksha Sunil Padsalgi

Nital Shah

Aniruddha Panda

Ratnamala Manna

Abhishek Krishnamoorthy Baikadi

Manedra Singh Parihar

State of the art techniques used for Flue Gas Desulpharization (FGD) process monitoring fail to comprehend the relationship between various process parameters, which is crucial in determining the performance of a FGD process being monitored. The disclosure herein generally relates to industrial process monitoring, and, more particularly, to a method and system for performance optimization of Flue Gas Desulphurization (FGD) Unit. The system identifies Key Performance Indicators (KPIs) associated with the process being monitored, and identifies parameters associated with each KPI. This information is used to generate several predictive models, from which a predictive model having the highest value of composite model score amongst the predictive models is selected as the predictive model for processing the input data, which is then used to perform optimization of the FGD process.

Method and system for performance optimization of flue gas desulphurization (FGD) unit

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

State-of-the-art systems used for plant monitoring and optimization fail to efficiently monitor and improve the performance of blast furnace ironmaking plants due to complexity of such plants. In addition, they attempt optimization without considering the operational stability of the blast furnace. The disclosure herein generally relates to industrial plant monitoring, and, more particularly, to a method and system for ironmaking plant optimization. The system determines an operational stability of the plant in terms of value of a determined Blast Furnace Stability Index (BFSI). Further, if the BFSI or one or more Key Performance Indicators (KPIs) of the plant deviates from corresponding threshold, then the optimization is done.

Method and system for ironmaking plant optimization

A system and method for performing data-based optimization of performance indicators of process and manufacturing plants. The system consists of modules for collecting and merging data from industrial processing units, pre-processing the data to remove outliers and missingness. Further, the system generates customized outputs from data and identifies important variables that affect a given process performance indicator. The system also builds predictive models for key performance indicators comprising the important features and determines operating points for optimizing the key performance indicators with minimum user intervention. In particular, the system receives inputs from users on the key performance indicators to be optimized and notifies the users of outputs from various steps in the analysis that help the users to effectively manage the analysis and take appropriate operational decisions.

Method and system for data-based optimization of performance indicators in process and manufacturing industries

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Pune, India

Rajan Kumar