Real-time Analytics
Tata Chemicals needed to reduce process variation and fuel consumption in its Mithapur plant's carbonation towers and high-pressure boilers, but operators lacked enough historical equipment data and timely analytics to improve yield, sustain boiler efficiency, and respond quickly to deviations.
Reported impact
Tata Chemicals gained an AI-driven operating system for key process assets that improved boiler efficiency and process consistency, raised average yield, reduced fuel-related inefficiency, and delivered measurable annual savings while helping operators sustain production through volatile market conditions.
TCS implemented an AI-driven digital-twin stack for Tata Chemicals Limited's carbonation towers and boilers at the Mithapur factory so operators could move from manual interpretation of process trends to real-time optimization. Plant data from more than 32,000 sensors already flowed through decision support systems, but the site lacked enough retained operating history, so TCS first set up historical data and a data historian to store longer time-series records from multiple sources. TCS then ran a discovery value assessment and deployed digital twins for three carbonation towers and two boilers, using the models to generate live set points and operational guidance for plant staff. Those outputs helped operators adjust parameters, reduce stack-temperature rise, cut fuel consumption, preserve a high steam-to-fuel ratio, and improve yield consistency, while creating a pilot architecture that could be scaled beyond the initial assets.
Operational data from more than 32,000 plant sensors flows through decision support systems into a data historian that preserves longer operating history. The digital twin uses those time-series records plus current process conditions to generate real-time set points and optimization guidance, which plant operators apply to the carbonation towers and boilers to improve yield and fuel efficiency.
"$600,000 in cost savings annually by implementing digital twins for the carbonation towers and boilers."
"Increased average yield, relying on AI-led actionable insights that increased process consistency and significant reduction in standard deviation."
"TCS helped TCL set up the historical data and implement a data historian (a database that collects and stores time-series data from various sources)."
"The digital twin increased the boiler efficiency at the power plant. This AI-driven system generated real-time set points, allowing operators to adjust and boost boiler efficiency."
"The plant's data spanning over 32,000 sensors was stored in decision support systems (DCS) that gave operators a unified view of the plant's operations."
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