Bokaro Steel Plant Begins Trial Of Its AI Based Predictive Monitoring System

BSL has collaborated with Hyderabad-based company M/S MINTO AI for enhancing the predictive system.

The Bokaro Steel Plant (BSL) recently began the trial of its Artificial Intelligence (AI) based Predictive Monitoring System. BSL, a unit of Steel Authority of India Limited (SAIL), is on the verge of digital transformation for better transparency, productivity and efficiency for its core steel production process.

BSL has collaborated with Hyderabad-based company M/S MINTO AI for enhancing the predictive system. Under the following trial, current based smart sensors have been installed at equipment such as cold screen motor of Sinter Plant, rotation motor for material distribution in blast furnace and crane main hoist motors at Hot Strip Mill, which work on the “Spidersense”, and an Industrial Internet on Things (IoT) platform of M/S MINTO AI.

“A big leap has been achieved by BSL in the strategic direction of digital transformation with the beginning of trials of the Artificial Intelligence-based Predictive Monitoring System at various shops such as blast furnace, sinter plant and hot strip mill,” said Manikant Dhan, Chief of communication, BSL.  

Dhan added saying, “These sensors use AI, deep process knowledge and physics to provide operational intelligence to plant engineers and operators which help in planning the maintenance based on predictive monitoring and alerts.”

The convener of the Centre coordinated the trial execution of this predictive monitoring system for Digital Transformation, Saurabh Singh, who is senior manager of the Energy Management Department (EMD) in BSL. 

Speaking on trial, Singh said, “The system has started collecting and storing various critical data on cloud which will be modelled under an algorithm for creating alerts and analytics. This will be able to help in machine health monitoring through digital technology.”

More Great AIM Stories

Victor Dey
Victor is an aspiring Data Scientist & is a Master of Science in Data Science & Big Data Analytics. He is a Researcher, a Data Science Influencer and also an Ex-University Football Player. A keen learner of new developments in Data Science and Artificial Intelligence, he is committed to growing the Data Science community.

More Stories


8th April | In-person Conference | Hotel Radisson Blue, Bangalore

Organized by Analytics India Magazine

View Event >>

30th Apr | Virtual conference

Organized by Analytics India Magazine

View Event >>

Yugesh Verma
All you need to know about Graph Embeddings

Embeddings can be the subgroups of a group, similarly, in graph theory embedding of a graph can be considered as a representation of a graph on a surface, where points of that surface are made up of vertices and arcs are made up of edges

Yugesh Verma
A beginner’s guide to Spatio-Temporal graph neural networks

Spatio-temporal graphs are made of static structures and time-varying features, and such information in a graph requires a neural network that can deal with time-varying features of the graph. Neural networks which are developed to deal with time-varying features of the graph can be considered as Spatio-temporal graph neural networks. 

Vijaysinh Lendave
How to Evaluate Recommender Systems with RGRecSys?

A recommender system, sometimes known as a recommendation engine, is a type of information filtering system that attempts to forecast a user’s “rating” or “preference” for an item. In this post, we will look at RGRecSys, a library that performs constraint evaluation of recommender systems.

3 Ways to Join our Community

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Telegram Channel

Discover special offers, top stories, upcoming events, and more.

Subscribe to our newsletter

Get the latest updates from AIM