Artificial Intelligence: Zyfra to Implement ‘Intelligent Mine’ Solution in India

March 16, 2020 | Artificial Intelligence

Zyfra is a Finnish-Russian Artificial Intelligence (AI) and Industrial Internet of Things (IIoT) solutions firm.

Zyfra said Sunday it had won an order from Odisha-based Thriveni Earthmovers Private Ltd to implement an ‘intelligent mine’ solution. It would deploy the solution at the state-run power generator NTPC’s Pakri Barwadih coal mining project in Jharkhand, India.

The contract would form part of the firm’s broader plans to introduce its AI solutions in the Indian mining industry. (CANINDIA)

Speaking in an interview with Scrabbl, Pavel Rastopshin, Managing Director, ZYFRA, said: “We give the highest importance to India as its growth rate should stabilize at 8% during the next decades, ranking the country as the world’s fastest-growing economy. We plan to reach a target of 50 million dollars in India-related deals by 2021. That would be up from three million dollars currently.”

India – key for ZYFRA

India has already become a key market for the company. It said in its statement that the country had more than 500 Computerised Numerical Control (CNC) machines. These were connected to its MDC plus real-time machine monitoring and manufacturing data collection system. By 2021, the company hopes to have more than 2,000 MDC plus installations in India.

“We recently received a confirmation from the Indian Railways our products have been chosen for tracking machine operation for the largest railways network in the world,” said Rastopshin to Scrabbl.

AI for Indian mining

“AI-based solutions allow to integrate the unmanned vehicles (dump trucks, excavators, bulldozers) into a single digital control system for the mining and transport complex and ensure that there are no people left in dangerous areas,” said Rastopshin. “They will help India to avoid open cast mine fatal accidents.”

At the Pakri Barwadih coal mining project in Jharkhand, Zyfra said that computer vision-based automatic blasting result assessment and excavator shovel monitoring would increase productivity and reduce equipment downtime.

Further, it had successfully tested two AI-based solutions that determine excavator shovel teeth conditions in real-time and provide continuous rock size estimation in the shovel bucket.

By accordingly adjusting blasting operations, the system improves process productivity by 3-4%.

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