EuroChem introduces modern solutions at enterprises as part of the digitalization of the technological process

2025: Digitalization of Manufacturing

EuroChem is actively introducing solutions at enterprises as part of the digitalization of the technological process. These solutions are based on advanced technologies of artificial intelligence, modeling, video analytics and the industrial Internet of Things. Usolsky potash plant is the first enterprise where EuroChem has worked out an integrated approach to digitalization. The company sees digitalization as a long-term trend and tool to improve safety, efficiency and optimization of production processes. The company announced this on March 18, 2025.

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Фото: EuroChem

The integrated digitalization of EuroChem's chemical production is a systematic management approach that covers all stages of the production process - from a separate unit to the workshop and the entire enterprise as a whole. This integrated method allows not only to optimize individual operations, but also to ensure the coordinated operation of the entire production chain in terms of efficiency and safety.

The Usolsk potash plant has become a pilot platform for EuroChem's integrated approach to digitalization of the production process. As of March 2025, more than 20 digital solutions with a total potential of 1.2 billion are being implemented at the site, rubles including a digital industrial platform, recommendation systems, an engraftment monitoring system, video analytics, a digital efficiency tool (InsEf), and BIdashboards an industrial safety control system (SCPB).

Integrated digitalization is based on an automated process control system (APCS), which allows operators to control the process. The digitalization process begins by collecting information about each stage of production using production sensors. Further, the data is aggregated in the digital industrial platform (DSP). This opens up the possibilities for creating digital products.

The introduction of artificial intelligence (AI) systems helps to significantly increase the efficiency and security of processes. Allows you to identify hidden patterns and relationships between alterations, which ultimately leads to increased profits.

At the Usolsk potash plant, recommended drying and granulation systems have been introduced for the flotation processing plant. These systems, developed on the basis of machine learning and big data processing technologies, have increased the production of granulated potassium chloride by 9.9 thousand tons per year and reduced fuel gas consumption by 85 thousand m³ per year. The direct economic effect for 2024 amounted to 59.4 million rubles, which demonstrates the high efficiency of investments in digitalization.

The introduction of recommendation systems based on artificial intelligence was a significant step to optimize the technological process of enrichment, as well as drying and granulation. The systems help operators make decisions and reduce losses of the finished potassium chloride product. All data is transmitted to the monitor in a convenient form, and the operator can only adjust the mode in accordance with the recommendations. This not only speeds up the process, but also allows employees to focus on quality control rather than manual calculations. The technology helps minimize routine operations and improves the efficiency of the entire team, said Roman Bushmakin, director of digitalization and automation at the Usolsky potash plant.

The connection between digital tools at different levels allows not only to track these relationships, but also to take measures to optimize them. For example, at the drying station, the recommendation system calculates and predicts the optimum temperature of the exhaust gases as well as the dried material. The data is then transmitted to the Digital Industrial Platform (DSP). In the CCP on the dashboard, the operator is given recommendations online, with the help of which he can optimize the operating mode of drying furnaces. This approach ensures the stability of the process parameters and significantly reduces the specific consumption of fuel gas.