Artificial intelligence and machine learning are changing many different types of business. When it comes to artificial intelligence in the concrete industry, there are many ways that new technologies can help optimize production and allow companies to turn out high-quality products.

Energy efficiency, better control systems, and an optimized production process improve how building companies source cement products. For example, an industry white paper reveals how AI aids in increasing feed by over 3 tph, and also reduced some aspects of energy usage by 20 kcal/kh. In a regulatory context, the company can avoid zero SO2-emissions violations and other penalties.

Some of these artificial intelligence systems and learning algorithms work in real-time and some use data analytics behind the scenes to chart better courses for business processes. Let’s look at some of the major ways that AI is powering better cement processes based on data science and new ways of improving production.

Artificial Intelligence in the Cement Industry: Raw Materials

One way that people can use artificial intelligence in the cement industry is to apply data science processes to getting the raw materials, including:

  • Limestone
  • Sand
  • Iron ore

There’s also the job of mixing materials accurately. That’s one of the initial processes where AI and machine learning can apply better and more precise formulation for a product. That precise mix is analyzed by sensors, data visualization tools, and more to inform process engineers how well things are going. Then they can alter the process as necessary.

For instance, what about 28-day predictions based on materials sampling? This type of analysis is another place that AI can be useful in optimizing the physical production processes. But then, there are others aimed at specific parts of the manufacturing life cycle.

Kiln Processes

After the mix is done, cement products move through a kiln oven, where specific heat treating processes contribute to the final result.

This is another place where there are many opportunities for AI to make the cement manufacturing process and cement production more dependent on automation and high-end technology. The AI and ML insights will help to form every part of a new and modern life cycle for turning out these end products.

For example, heating gases used in the process may be evaluated in terms of CO2 emission. Stakeholders may analyze stages of the process, including:

  • Drying
  • Preheating
  • Burning
  • Cooling

In the metals world, part of a cooling process is often called annealing. In the cement world, cooling happens in a particular zone of the manufacturing facility and uses specialized equipment. How do companies know that this is happening in a consistent and optimal way? With AI observation and insight systems. Some call this “industry 4.0,” a way to innovate and explore industrial AI and its potential.

Evaluation of Equipment

All through the life cycle of the cement manufacturing process, companies can use artificial intelligence tools to optimize the use of machines like a mill, pre-heater, kiln, or cooler. These types of equipment correspond to the production stages mentioned above, and each one has its own process that can be fed into an AI engine to automate monitoring and control.

In general, using critical control parameters, engineers can figure out how to assure a better process through repetitive analysis. The plant may go through a certain production process, evaluate that, and feed it back into future processes to get the needed improvements. There’s also the fuel-air mix. Much like in many combustion systems, cement production requires this formula. AI optimization can be as simple as adding sensors to control the fuel-air mix. High-quality engineered vendor services use sensors to generate the data. AI controls the process accordingly.

Lower Energy Consumption

There are also many ways that AI-based systems can help with energy savings in the cement industry. It’s easy to imagine how these powdering, mixing, and heating processes can have their own carbon footprints and how they affect the environment.

Understanding CO2 emissions in the cement production process helps companies think about how to modernize the ways they produce these construction materials.

That may relate to the use of fuels, the oven process mentioned above, or other types of secondary business processes. In any case, it’s the intersection of data gleaned from physical production, and analytics tools, that coordinate change.

Artificial Intelligence in the Cement Industry: Chemical Optimization

As a complex material, cement is made of those raw materials and chemical interactions that create the final shipping product.

So when those processes are controlled by automation, different types of supervising and predictions lead to dramatic changes in how people work to produce these materials and products.

For example, drying is its own type of chemical process that can respond to new AI or ML insights generated by tools using data science to function.

One way that experts sometimes put it is that automating a human process frees up labor and resources to focus on other components. So whether it’s evaluating the drying, the mixing, the heating or anything else, those individual AI modules work dynamically to assist teams and to provide valuable information to those at the top.

These are just some of the ways AI in the cement industry contributes to efforts to optimize production.

Another way to think of it is in terms of workflows and their inputs and outputs.

Initial workflows might have those individual raw materials as inputs, with mechanical or physical processes that create an output. That gets fed into the next stage of the process. Both in overall monitoring and in optimization, AI and ML reveal efficiencies for cement plant production. This is a very real part of how new tech powers a field that has been known for its significance in the construction world.

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