South African cement and lime producers face a tight equation: Stable output, predictable quality, lower fuel consumption and controlled emissions must all be achieved simultaneously.

Innomotics says its DigiMine AI Pyro solution takes a data-driven approach to optimising the pyro process by improving stability, thermal efficiency and operational decision-making.

AI Pyro leverages real-time process data together with historical production and quality information to continuously identify optimal operating conditions using the company’s patented Fingerprint Technology and Hybrid AI architecture. Rather than relying solely on predefined process models, AI Pyro continuously learns from a plant’s own historical operating experience to identify stable, high-performing operating conditions unique to each operation.

Based on these insights, the solution forecasts key kiln and process parameters and generates optimised setpoints that can be implemented in open-loop or closed-loop operation.

In kiln applications, the prediction module forecasts critical signals, including sintering zone temperature, kiln inlet temperature, kiln inlet oxygen and kiln inlet nitrogen oxide for the next 15 to 30 minutes. This provides operators with early visibility into process trends and optimisation recommendations that remain within defined operating constraints, supporting stable kiln operation and reliable process performance.

Innomotics states AI Pyro deployments have demonstrated specific heat consumption reductions of between 2% and 5%, while enabling thermal substitution rate improvements of about 1% to 3% where alternative fuels form part of the production strategy.

Continuous optimisation of the thermal process helps to reduce fuel consumption and CO2 emissions while improving process stability, thereby reducing thermal stress on equipment, extending maintenance intervals and supporting production continuity.

For lime operations, AI Pyro extends beyond forecasting. It also incorporates AI-based soft sensors that continuously predict free lime content, complementing periodic laboratory measurements and enabling operators to respond earlier to prevent overburning and underburning.

Fingerprint Technology, combined with AI-based dependency modelling, can also strengthen anomaly prediction by detecting process deviations earlier than conventional threshold-based monitoring, enabling proactive intervention before they escalate.

According to Innomotics, the combination of Fingerprint Technology, neural network prediction models and Hybrid Control transforms complex process data into operational intelligence, supporting more stable kiln operation, improved energy efficiency, consistent product quality and a practical pathway towards autonomous process optimisation.