Smart thinking, smart grinding

A network of increasingly complex difficulties and opportunities sits at the heart of the mineral raw materials processing sector. Navigating this landscape entails overcoming numerous obstacles, such as the rapid growth of market regulations. With new standards and demands appearing on a regular basis, businesses must remain adaptable to keep up with these changes. Falling behind is not an option. It is a path that will eventually lead to irrelevance and extinction.

The growing scarcity of trained workers complicates this dynamic. Companies need people who can oversee complex production processes, people who can balance efficiency and quality against the backdrop of a continuously changing landscape, now more than ever. As if that was not challenging enough, these professionals also need to handle a constant deluge of operational data.

Faced with a vast sea of information, even the most seasoned operators can feel overwhelmed. This reality often forces them to adopt higher operational safety margins, a decision with a double-edged impact. While it ensures the continuity of plant operation, it can lead to significant fluctuations in product quality and a marked reduction in plant performance. Safety, while necessary, comes at the expense of efficiency.

Layered onto these internal challenges are external pressures. The global focus on environmental sustainability is increasing, and businesses are expected to modify their processes in response. Reduced carbon footprints and more energy-efficient operations have become a non-negotiable requirement rather than a value-add. To navigate these various layers of complexity, both invention and resilience are required.

In this challenging landscape, the potential of smart grinding emerges, offering a beacon of hope. With its promise to transform traditional methods through the application of data science and machine learning, smart grinding is a novel approach for controlling dry grinding and sifting circuits.

Harnessing data science for intelligent control

Cemtec-Digital, which was launched in 2022 by the Cemtec Group to provide a central contact point for digital solutions, tackles industry challenges with an approach rooted in the belief that data is not just a by-product of operations, but a valuable resource. Leveraging data science and machine learning, this approach is changing the way dry grinding circuits are controlled and managed.

A digital twin – a virtual version of a real-world grinding plant – was created as part of this data-driven strategy. Serving as a safe simulation ground, this digital twin enables the testing of various operational scenarios, outcome analyses, and impact forecasting without interfering with real-world operations. However, the potential of a digital twin is increased when it is combined with cutting-edge machine learning techniques such as reinforcement learning. This technique is akin to training a pet, in that positive behaviours are reinforced while negative habits are penalised. Over time, the pet learns to maximise positive actions and minimise negative ones.

Similarly, a reinforcement learning algorithm functioning within the digital twin’s environment is trained through an action-feedback loop. Depending on the outcome of the actions taken, the algorithm receives rewards or penalties. This iterative process allows the algorithm to learn and adapt over time, gradually recognising the actions that yield the most positive results.

Enjoyed what you've read so far? Read the full article and the rest of the November Issue of World Cement by registering today for free!

Read the article online at: https://www.worldcement.com/special-reports/10112023/smart-thinking-smart-grinding/