EuroChem rolls out machine learning systems across its ammonia plants in Russia

EuroChem has implemented machine learning-based recommendation systems at all of its ammonia production facilities in Russia, extending the use of artificial intelligence across a core part of its fertilizer operations. The company stated that the systems, which analyze large volumes of process data and provide real-time operational guidance, generate an economic effect worth millions of rubles annually through lower natural gas consumption, reduced downtime, and increased production volumes.

The rollout marks one of EuroChem’s most comprehensive applications of recommendation systems to date. According to the company, the tools continuously monitor technological parameters, identify deviations and hidden patterns, and suggest optimal operating modes that balance cost reduction with maximum output.

Valery Cherepanov, Deputy Director for Digitalization at the EuroChem Group, said the project demonstrates how artificial intelligence can be used to manage complex industrial processes more efficiently. He added that the systems improve risk management and operational adaptability, and could serve as a reference point for broader adoption of similar technologies in the Russian industry.

The development and deployment of the systems took nearly two years and involved close cooperation among process engineers, mathematicians, and data specialists. The teams had to adapt the solution to the specific conditions of each ammonia workshop, including licensing requirements, hardware configurations, and existing automation systems.

Sergey Nekretov, head of Ammonia Production Workshop No. 1-V at Nevinnomyssk Azot, stated that the project necessitated extensive preparation and cross-functional coordination. Specialists from production, technology, operational efficiency, and digital units jointly identified key process levers and tested the system, confirming gains in output and cost reductions through optimized operating regimes.

EuroChem said the systems have reduced response times to process deviations to just a few minutes, helping lower gas consumption and increase ammonia output without additional capital investment.

The company is now considering extending similar machine learning solutions to urea production units, a move it says could further improve operational flexibility, reduce exposure to raw material price volatility, and strengthen its position in the global mineral fertilizers market.