In a resource-intensive world like cement production, operational inefficiencies can translate into significant financial and environmental costs. Historically, cement mills have depended on manual workflows frequently resulting in increased downtime and lack of consistent performance.
The integration of AI is reshaping this landscape by introducing automated set-point recommendations, a feature of automation that ensures the mill operates consistently at optimal conditions. These set-point recommendations adjust key parameters in real time, reducing human intervention while improving accuracy and operational stability. The following is an analysis of the achievements of one of Wizata's clients.
The client's cement mills faced several problems prior to AI implementation. Production optimization and yield improvement were challenging due to inconsistent processes. This inconsistency stemmed from the limitations of manual monitoring; operators could not analyze data in sufficient detail to identify trends indicating that the mill was heading toward suboptimal conditions. By the time these deviations were noticeable, and adjustments were made, the mill had already been operating inefficiently, leading to increased energy consumption and reduced output. This challenge created the need for an intelligent, data-driven solution capable of providing real-time insights and adjustments.
To overcome these issue, the client selected Wizata's software for automation and optimization. The first phase involved integrating real-time sensor data, historical maintenance logs, and laboratory reports into a Unified Name space or Digital Twin. This provided a comprehensive view of machine conditions and operational trends, forming the foundation for AI analysis.
The next step was deploying AI models in an open-loop configuration. In this phase, the models generated recommendations that operators manually executed, allowing for thorough testing while building operator trust in the technology. During this process, AI insights identified and corrected inefficiencies while ensuring stable operations. Once the model was validated, the loop was closed, enabling complete automation.
This process begins with areas where the AI model has the most data, ensuring a smoother transition. In this case, since the process is divided by different cement types, deployment started with the cement types where the most data was available
The AI models, run on the Edge, in direct communication with the mill's PLCs. This setup improved efficiency and reduced the need for constant manual supervision. Through this systematic approach, the cement mill achieved a significant leap in operational precision and performance.
Data is the backbone of AI's effectiveness in cement mills. While it would be ideal to have extensive historical sensor data, which is not always the case, only a minimum of six months is required to develop a reliable AI model. The first step involves connecting to the data and ensuring real-time access. Next, the model is built and tested in an open-loop configuration, where operators execute the AI's recommendations manually. The last step is the close loop. While the platform leverages cloud infrastructure, its AI models execute locally on Edge devices, facilitating ultra-fast data processing and direct integration with the mill's PLCs.
Typically, the timeline involves a four-month data acquisition and model development phase, followed by a three-month testing period, ensuring the system is meticulously fine-tuned to align with the mill's unique operational nuances for dependable performance and precision.
The integration of AI has delivered significant improvements for the client. Some key achievements include:
Wizata's vision for the future includes refining model adaptability and supporting an expanded range of cement types. Currently, human intervention is only required for specific cement types where sufficient historical data is not yet available to fully train the AI model. This evolution will eliminate the need for manual overrides in scenarios where the model has been fully trained and tested, ensuring optimal performance across all operations. In addition, potential risks such as deviations in operational patterns from historical norms are mitigated by alert mechanisms, flagging interruptions in data flow or unreliable recommendations.
The success of AI integration in cement mills has set the stage for wider adoption. This company has Wizata AI now running in two mills and plans to expand to four more. By delivering automated set-point recommendations and elevating energy efficiency, AI solidifies its position as a groundbreaking catalyst for innovation and sustainability in cement production.
This case study demonstrates the transformative impact of AI in cement mills. Regardless of the type, be it ball mills, vertical roller mills, or hammer mills, the AI is an invaluable asset that can improve many aspects of cement manufacturing, from boosting efficiency and lowering costs to advancing sustainability initiatives and predictive maintenance. Beyond cement mills, AI technology also holds significant potential for optimizing other critical machinery such as rotary kilns, roller presses, and bucket elevators which we will cover in another article. The opportunities for optimizing industrial processes are vast, paving the way for smarter, more resilient, and highly adaptive operations across the industry.