Strategic AI: How Cement Manufacturing Companies Can Achieve Higher Profitability with Artificial Intelligence

Traditionally, cement manufacturing companies have booked plant improvements as capital expenditures. Artificial Intelligence now offers a much less expensive alternative by allowing companies to use the vast amount of data they routinely collect. While AI technologies have made tangible improvements to supply chains and administrative functions, they lack notorious presence in cement manufacturing (and other manufacturing industries), which is interesting, given that cement plants were early adopters of automation and control systems and have used digitized sensors and signals for many years already. For decades cement manufacturers have been “digitizing” their plants with distributed and supervisory control systems and, in some cases, advanced process controls. While this has greatly improved visualizations for operators, most companies with these systems have not kept up with the latest advances in analytics and AI-based decision-support solutions.

The process of manufacturing cement has a few common denominators across the long list of companies manufacturing it. These common denominators in the process are: raw materials (predominantly limestone), the process of crushing, milling, preheating, calcining and sintering.  We could say calcination is a key step in the entire process. It is done in a rotating kiln at extremely high temperatures. The end result is the clinker, a stone-like product. This clinker then goes to the lab for quality analysis. Then, depending on the cement type, other aggregates come into the picture before it goes through the final cement mill and off to a final storing silo. Depending on each plant, you will find extra steps in the processes such as storing, cooling, mixing, etc.

   The cement industry also faces common challenges, such as improving productivity while reducing cost. They have to comply with environmental regulations while providing a safe environment for materials, machines and ultimately employees. For these reasons, the adoption of Process Control Systems has been rising among cement manufacturing companies. Sensors and PLCs have been installed across the plants, monitoring each step of the process and delivering the data to a series of configurable dashboards in a control room.

   A process control system is an important initial step in the digital transformation effort any cement company needs to go through. It provides good structured data. Tons of it. It provides a perfectly accurate, real-time picture of what is happening along the manufacturing process.

   It is important to mention that cement manufacturing operations demand a lot of energy during the manufacturing process, more so at the calcination and milling level. For that reason, when the lab finds out that the quality of the clinker has deviated from the set standard, minutes or even multiple hours of production must be disposed of. A lot of waste is generated in terms of materials and energy. At the end of the year, the dollar amount of wastage could be substantial. Saving production time, they will optimize fuel consumption greatly as well, and minimize the production site carbon footprint, increasing efficiency.

Predicting Cement Quality from the early stages of the process using AI models can not only reduce wastage but save energy as well. 

  This is where Artificial Intelligence can bring a lot of value to the equation. Most corporations cannot currently predict the quality of the clinker from the beginning of the process. As an analogy, a process control system can provide an accurate x-ray of the day-to-day operation where you could immediately identify if a bone is broken. But the x-ray has to be interpreted by an expert to see if there is something wrong, such as potential cancerous cells forming, or if the subject is completely healthy, etc. This is what AI can do for a cement plant. With the creation of AI models from the structured data the process control system generates, it is now possible to predict the quality of the clinker at very early stages before it goes through preheating and calcination. AI not only helps corporations to save energy but also avoid potential substantial material waste. All this information can then be delivered to the same control room and added to current dashboards, displaying info related to energy consumption, production volumes, performance figures, asset management, etc.

   The value AI can bring to a cement manufacturing plant can exponentially grow and scale. If the plant belongs to a group of plants, once the first plant is up and running with a reliable AI solution, the adoption of AI by the rest of the plants could be a relatively easy step.

   Cement plant manufacturers with process control systems installed have a great advantage already. The data is mostly structured and delivered to a PI (process information) server where an API can be integrated into an AI system either in the cloud or in situ. Structured data is the Achilles heel of AI. AI requires structured data to perform correctly. Other industries do not have this advantage of having structured data from the outset of the AI adoption and the data has to go through a refinement process, so to speak. In many cases, the data has to be structured manually, adding considerable time to the process. However, the data structuring process could also be automated with AI if needed. At any rate, it is good cement companies have structured data available from the beginning in most cases.

   Cement companies adopting AI for in near-real-time prediction of Free Lime quality using ‘Soft Sensors’ (Models) to reduce standard deviation of the error in Free Lime quantity, have a tremendous strategic advantage against those companies not adopting it. They will be able to know the quality of the output from an early stage.

   Nowadays the adoption of an AI solution for quality prediction for cement manufacturers is easier to do than you can imagine. It could be adopted as a subscription, thus avoiding costly upfront payments and other out of pocket costs. I will be happy to guide you through the adoption process if you want to know more. Feel free to private message me for additional info. 😉