Cement manufacturing is highly resource-intensive and requires many manual and energy-intensive processes.
AI aims to enhance existing processes and make cement production more robust, greener and more efficient. In the cement industry, every piece of equipment is unique.
To achieve the best results in your plant, the system must be trained for each process separately and used in combination.
The cement industry is a large energy consumer and can significantly reduce operating costs by using AI applications to improve energy efficiency, reduce carbon emissions and improve production efficiency and supply chain efficiency improvements
Using AI, it can save on production costs, raw material purchases, factory maintenance, improve productivity and reduce depreciation.
Cement production is resource-intensive, with many manual operations and energy-intensive processes.
AI will strengthen existing processes and make cement production more robust, environmentally friendly and efficient.
In the cement industry, each piece of equipment is unique.
The system must be trained on each individual process and then combined to achieve the best results in the factory.
Cement production begins with the collection of raw limestone, sand, iron ore and alumina, and throughout the process it is mixed in the right proportions, heated to extremely high temperatures.
The typical equipment is called a kiln and most of the heat needed to create cement is done through it.
After the material successively passes through the drying zone, pre-heating zone, decomposition zone, combustion zone and cooling zone, clinker is created as a product of the kiln firing process.
This can be done in three ways.
- Equipment management: Several types of equipment such as raw material crushers, preheaters and calciners, rotary kilns, coolers and cement mills are used to produce cement, these types of equipment Consumes a lot of cement energy and is easily damaged and requires regular maintenance.
Furnaces, fans, carbon mills, bag filters, heat exchangers and air filters are additional equipment for the process.
These assets require targeted monitoring and powerful predictive analytics to operate at optimal and enhanced performance.
IoT sensors and IoT-enabled wireless networks will augment existing monitoring systems and along with operational parameters, the data generated can be fed into AI models.
Thanks to that, the AI model can predict machine reliability and recommend the most optimal settings for the device. - Process: Plant data can be used to monitor and control the process and ensure stable process operation.
An AI platform that leverages this data can deliver improvements in energy, productivity, and quality that go beyond existing baseline efficiency.
Magic happens when science-based, first-principles models are combined with data-driven models.
Domain expertise combined with AI capabilities is a powerful approach that needs to be adopted by the industry.
Cement manufacturing processes, including pulping, mixing and heating processes, have carbon emissions and environmental impacts.
Understanding the carbon footprint of cement production helps companies think about how to modernize the way they produce these construction materials.
In all cases, it is the intersection of data collected in physical manufacturing and analytics tools that orchestrates change.
Cement is complex, made up of raw materials and the chemical interactions that create the final product.
So, as these processes are controlled by automation, different types of monitoring and prediction will lead to significant changes in the way people work to produce these materials and products.

AI can work flexibly to support teams and provide valuable information to senior management by evaluating drying processes, mixture heating or anything else.
- Supply chain: Logistics costs represent a significant portion of the total cost of manufacturing and sourcing cement.
Data analytics is now being used to improve logistics efficiency.
However, more powerful AI algorithms are being built to spatially analyze the supply chain, from supplier to end customer.
This can lead to significant improvements in inventory availability and forecasting, forecasting and reduction in shipping times, and improved transparency and delivery times.
Use case study, application of Bert Platform solution: In an ideal environment where AI supports cement production, data is collected during cement management and supply chain planning Cement supply as well as input and output will be in the cloud, a centralized cloud server (Bert Nova).
The data collected (Bert Maximus and Bert Qrious) from all factories will be stored and processed giving visual cues to create a digital twin (Bert Geminus) – of each unit and factory.
Bert Mus Optimus Reinforcement Learning (RL) is performed through an exploration/exploitation process on the Digital Twin (Bert Geminus) in real time, capturing the control levers and implementing them for end business goals Finally, improve production and improve energy.
Multi-agent self-learning platform enables Bert Platform solution actions to be performed on granular real-time data with underlying machine physics with reusable underlying models.
It delivers multi-agent decentralized learning and consensus based on platform-integrated multi-layer abstractions and fully automated 360° goal-driven real-time controls.
With this, plant operators and presidents can predict 96-97% of operating performance using the Bert Optimus prediction model.

Custom-built models combine the strengths of AI models and First Principles models, controlling complex processes and driving them toward greater operational efficiency.
An AI-based system can operate on a much fuller scale, helping to achieve process efficiency.
Deep learning neural networks will help achieve the best possible results.
Historical data is a complex matrix of entries and timestamps from data collected over many years.
The model tries to achieve the best relationship between variables over time.
Many optimization algorithms control all processes.
For example, they provide optimal (regulatory) parameters and priority parameters for plant operations.
Bert Platform Solutions can therefore optimize efficiency and energy savings through efficient use of resources by minimizing unplanned downtime and accidents.
Electricity and fuel are the main costs in cement production (about 30% of total costs).
In particular, electricity and coal consume more energy and directly affect the operation of the cement factory.
Electricity is used at many stages, from raw material grinding to clinker grinding. Artificial intelligence (AI) and machine learning (ML) applications take the factory to a better future with just one click towards digitalization, energy savings and reduced carbon emissions.