Objectives

Automating coke and sinter sizing to improve energy efficiency

Industry Served

Steel Manufacturing

Solution

Ripik Vision

What Calls for a Change

A formidable player in the global steel industry, this 1 million tonne steel plant focuses on producing both flat and long products through its Blast Furnace (BF) and Basic Oxygen Furnace (BFO). With a keen emphasis on cost competitiveness, the plant is acutely aware of the supercritical importance of maintaining economic viability for long-term survival in the competitive steel market.

Problem

Traditionally, coke and sinter sizing process were conducted manually with a sampling frequence of just one per day, effectively 3 times a day. Hence there was no track to find out what sort of coal is being fed throughout the shifts and how it is impacting the fuel rate and efficiency of BF. Blast Furnace (BF) operating with a fuel rate exceeding the benchmark, often exacerbated by an excess of feed due to improper input coal control. The critical factor in this scenario is the uncontrolled reaction rate of particle size, contributing to process variability. This infrequent and random sampling approach has resulted in increased costs, making it difficult for the client to survive in the cost competitive industry of Steel manufacturing.

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The Solution

Ripik.AI focuses on reducing operational variability through optimal process control. We implemented our patented Cognitive and Vision AI based solution Ripik Vision, capable of particle differentiation based on color, moisture and size. With the plant team interaction and first-hand process control, we swiftly delved into comprehending their requirements.

Implementation of Blast Furnace (BF) Fuel Rate Control helped operators with the tools to pinpoint the key factors contributing to the increased fuel rate in the furnace. The extended applications of computer vision also predicted slag chemistry and hot metal silicon, optimizing operational efficiency. The application also forecasted scrap quality in Electric Arc Furnaces (EAF), contributing to enhanced performance and steelmaking precision.

This approach empowered operators to optimize fuel efficiency, enhance furnace performance, and contribute to more efficient and sustainable operations.

The Impact

In the first few weeks of deployment, our client saw a significant 3% reduction in fuel use, which was ascribed to precise Coal and Sinter size control and uniformity. The integration of smart alerts, leveraging video feeds, and the capability for post-mortem analytics through the retrieval of historical images/videos, enhanced their operational efficiency and productivity.

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“Ripik.AI understands the complex blast furnace process and their knowledge in the field of data science has been invaluable in helping us optimise our plant operations and improve overall efficiency.”

Chief Digital Officer

Explore how Ripik Vision can transform your steel manufacturing operations.