An early case to prove the potential of AI in Cement manufacturing with an autonomous operational mode resulting in energy savings, while stabilizing final clinker temperature.
Operators face a challenging work situation. Tasked with monitoring and controlling a multitude of process variables, operational practices often resort to relying on individuals’ experience, instinct and standard operational modes with room for improvement and optimization. Pushed to the limit, operators take shortcuts and prioritize urgent activities over value-adding improvements; at the expense of performance and stability.
SentianController learns from the available operational data, providing early impact by forecasting key process variables and quality metrics. Enabling operators to make proactive operational decisions to improve quality, stability and reduce cost.
As the model learns and grows so does SentianControllers capabilities. Learning from the best operational performers to stabilize delivery by recommending optimized setpoints, enabling consistent higher performance from all experience levels making operations less dependent on individuals for success. Further increasing quality while minimizing energy consumption and other operational costs.
As a final stage operators utilize SentianControllers autonomous mode to autonomously apply optimized setpoints and corrections towards current quality and production targets. Freeing up operational experience and time for value-adding improvements.