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Project - Data science

Process modelling in the extractive industry

Find out how our Process Improvement team models the process to rationalise the amount of energy needed and stabilise production.

Initial situation

The production of clinker, the basis of cement, is carried out by calcining limestone 24 hours a day. The resulting energy consumption is therefore enormous, and therefore contributes to the CO2 footprint of the process.

The variability of the raw material and the successive chemical reactions (endothermic and exothermic) can disrupt the process and lead to oscillations that result in unwelcome consumption peaks that are difficult to control.

In addition, these oscillations can cause technical problems leading to unplanned kiln shutdowns and high restart consumption.

Solution proposed by our teams

Technord combines two modelling approaches by implementing mass balances and energy balances, reinforced by data-driven Machine Learning algorithms.

Hybrid modelling and the use of historical data enable the development of a decision support tool for the operator.

This tool suggests energy recommendations to the operator in order to remain in the ideal operating mode.

Gain for the customer

After a period of validation of the tool by the process experts, the customer will get a “real time” tool that instantly exploits the available measurements, corrects them if necessary, and helps them to respect the ideal production conditions.

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