Dr SB Hegde, Professor, Jain College of Engineering and Technology, Hubballi, India and Visiting Professor, Pennsylvania State University, USA, discusses how AI, automation and digital twins are redefining cement manufacturing.

For many years, an experienced kiln operator could judge the condition of the process from the flame colour, kiln sound and important instrument readings. This experience was valuable. But much of it stayed with a few individuals and was not always passed from one shift to another.
A modern cement plant collects a large amount of information every minute: temperatures, pressures, gas readings, motor loads and laboratory results. Too often, this information is used only after a problem arises. The opportunity is to use it earlier, so that plant teams can prevent problems instead of only reacting to them.
I wrote in these pages as early as 2020 that better data gathering, analysis and automated process control would be the first and most visible gains of digitalisation in cement. Six years on, that early promise had turned into hard results on the shop floor.
Three tools are helping plants make this change. AI studies data and predicts how the process may behave. Advanced automation applies approved control actions. A digital twin is a virtual model used to study how the plant may respond to changes. Robots and automated equipment can take over routine or hazardous work. Used together, these tools support what I call the Smart Kiln Era.
In my earlier articles in this magazine, I described this shift as the arrival of Cement 5.0, where advanced technology works side by side with human intelligence and a firm commitment to sustainability. The smart kiln is where that idea becomes real on the shop floor.
Figure 1 explains the smart kiln control loop. Plant instruments send data to a common platform. AI and a calibrated digital model help evaluate operating choices. The control system applies approved changes within safety limits, while operators and engineers remain responsible for the process.

Why the kiln needs intelligence
The kiln is at the centre of cement production. It is also a major source of cost and carbon emissions. The cement sector accounts for around 7 per cent of global CO2 emissions. As a broad industry estimate, roughly 60 per cent of cement-production CO2 comes from limestone calcination and around 40 per cent from fuel and energy. The exact split depends on the plant and the emissions boundary used. Better process control can directly reduce part of the energy-related emissions. Cutting process emissions much further also requires measures such as lower-clinker cement and carbon capture.

Table 1 shows where these emissions come from, and which part smart kiln technologies can influence directly.
A cement kiln is not easy to control. Raw meal quality changes; alternative fuels vary in moisture, ash and heat value; and coating forms and falls inside the kiln. McKinsey has reported large changes in key operating measures, in some cases up to 50 per cent from average conditions, in the plants it studied [5]. That is a reported example, not a normal range for every kiln.
Put simply, the kiln is always changing. Even skilled operators may recognise some disturbances only after they begin to affect the process. Good data analysis can identify early warning signs and help the team act sooner. More stable operation can save fuel, protect refractories and reduce emissions.
A red river in the clinker cooler shows why kiln stability matters. In my earlier article, I reported that such episodes can raise specific heat consumption by about 3–5 per cent and reduce refractory life by about 25–30 per cent in the cases discussed. Changes in burning-zone temperature, oxygen and kiln torque can provide warning signs. These figures and signals should be checked against conditions at each plant.
A plant must now watch carbon as carefully as costs. India’s Carbon Credit Trading Scheme (CCTS) brings emission-intensity obligations into the business discussion, subject to the applicable rules and compliance timelines. I have described the need to measure performance through carbon-adjusted profitability. A stable kiln is a useful starting point.

3. AI and machine learning in process control
   AI can support a kiln operator by studying past and current plant data. Machine learning models look at the links between fuel flow, kiln speed, feed rate, fan speed, oxygen, temperature and clinker free lime. They can predict a change before it becomes serious and suggest more suitable settings. In a validated closed-loop system, some settings can be adjusted automatically within limits fixed by the plant.
   Advanced process control (APC) and model predictive control (MPC) and AI are related but not identical. APC and MPC use process models and operating limits to keep the kiln steady. AI can add better pattern recognition, disturbance prediction and operating recommendations. Their roles
   may overlap in commercial systems. Plants with reliable APC already have a useful foundation for adding AI; they do not always need to replace the existing system.
   AI also helps with plant-wide cement quality control. Normally, the final 28-day strength result comes too late to correct the batch that produced it. A machine-learning “soft sensor” can estimate 28-day strength much earlier, based on available process and laboratory data. Engineers can then review fineness and blend targets sooner. Where cement is consistently made stronger than needed, the plant may be using more clinker or energy than necessary; the actual saving depends on cement composition and quality limits.
   Table 2 gives published and company-reported examples. It is important to separate measured improvements from programme targets: projected savings are not yet achieved results.
   Global momentum, 2024–2026. Three lessons stand out. First, AI can work with existing APC. At Heidelberg Materials’ Mokra plant in Czechia, AI was added to an existing control system, with a reported 2.2 per cetn reduction in specific heat consumption after adjustments for fuel mix and raw material quality [9]. Second, Indian producers are also investing in AI process control: the announced JK Cement and Adani Cement programme includes a five-year savings and CO2-reduction target. These are targets, not results already delivered. Third, financial outcomes matter as much as technical improvements. Plants should measure their own results against a fair baseline. Most figures cited here are reported by companies or solution providers.
   Why do some AI projects succeed while others stop after a pilot? In my experience, the main reason is often not the algorithm. A project struggles when it is left only to the IT team, when operators have no role in its design, or when there is no plan to update the model after changes in limestone, fuel or equipment. The plant head must own the results. The system needs a budget, periodic review and continuing support.
   A sensible approach has two stages. In advisory mode, AI recommends settings, but the operator makes the final decision. After the recommendations have been tested and trusted, selected controls may move to closed-loop operation. Even then, the system must respect process limits, safety interlocks and operator override. My advice is simple: do not rush this step. Trust is earned on the shop floor, not in a presentation.
   Figure 2 shows this journey as a staircase. Each step builds on the trust and data quality earned in the one before it.

Digital twins
A digital twin is a computer-based model that is kept in step with the real plant using measurements and equipment information. Engineers can test ‘what if’ questions: What happens if alternative fuel use rises by 5 per cent? What if limestone quality falls? What if a shutdown is delayed? The twin helps compare options before making changes in the plant. It is a decision-support tool, not a guarantee of real-world results.
Figure 3 shows the basic idea. Live data updates the virtual model. Engineers study possible changes in the model and then check the most promising actions through plant procedures and field validation.
A digital twin is useful only when it is properly calibrated and checked against real plant behaviour. Start with one area, such as the kiln and cooler. Agree on how accurately the model must predict key process conditions before using it for decisions. Once it performs reliably, extend it to other equipment. A twin can also become a training simulator. New operators can practise a cold start, a red river situation or a fuel change without exposing people or equipment to real risk.
In my series on gas balancing, I described how digital twin models can test combustion and gas balancing strategies before they touch the live kiln, with kiln exit O2 and CO giving real-time feedback on how well the plant is burning. The same thinking extends to product design: machine learning can match the right cement blend to local raw materials, climate and end use, which is vital if low-clinker cements are to reach their full potential.
Digital twins are useful beyond daily kiln control. In new projects, models can help review layouts and equipment choices before construction. During operation, they can bring information from different departments onto one platform. I see the digital twin as both a record of how the plant behaves and a safe place to test ideas.

Automation, robotics and predictive maintenance
Automation is not new to cement; plants were among the early adopters of distributed control systems. What is new is the level of intelligence and the range of tasks machines can now handle.
Automated laboratories can collect, prepare and test samples throughout the day and night, reducing delays and variation. Online analysers can support faster raw-mix correction. Robots are also being explored for handling, inspection and quality tasks. Drones and crawler robots help inspect stacks, silos and preheater towers, reducing the need for dangerous access. All such applications still require site-specific safety checks.
My suggestion is to consider robotics first where people face the greatest risk, such as cyclone cleaning, kiln inlet inspection, hot meal sampling and confined-space work. The aim should be safer work, with productivity improvements as an added benefit.
Predictive maintenance can provide early value. Changes in vibration, temperature or electrical current can lead to developing equipment problems. Kiln-shell scanning and suitable refractory models can also help the team plan repairs before an emergency shutdown. These methods support engineering inspection; they do not replace it. A single unplanned kiln stoppage can cancel out many earlier efficiency gains.
Holcim reported in 2024 that AI-based predictive maintenance was being used at 45 plants, with plans to expand it to more than 100 plants by 2028. This is an example of growing industrial adoption; the expansion figure is a plan, not a completed deployment.
AI is also making the control room smarter. By studying how operators respond to alarms, it can filter out nuisance alarms and highlight the few that truly need fast action. In the case of red river, automation already serves as an early-warning system that keeps people out of harm’s way before molten clinker reaches the cooler.

India’s progress in smart cement manufacturing
India is the world’s second-largest cement producer. The figures cited in my earlier review indicate installed capacity of about 690 million tonnes per year and direct employment of more than one million people. An average clinker factor of about 67.5 per cent and CCTS emission-intensity targets covering 186 cement facilities have also been reported. These are source-specific figures and should be checked against the latest official definitions and notifications before publication. Table 3 summarises the cited position.
This creates a unique challenge: India must build capacity faster than almost any country, while lowering emissions per tonne. Smart kiln technologies help on both fronts. They squeeze more output from existing kilns, and they make it easier to use alternative fuels, which are variable and hard to manage by hand.
India is beginning to show practical examples. The supplier’s programme set targets of US$100 million in savings and 4 million tonnes of CO2 reductions over five years. These targets should not be presented as achieved savings. Mangrol has also been identified in my earlier article as the site of India’s first commercial LC3 production, showing the connection between better process control and new cement formulations.
Mid-sized producers can also start. Some AI applications work with the instruments and control systems already installed, although the amount of integration needed will vary. Begin with one
kiln line, one measurable problem and a six-month trial. Compare the result with a clear baseline that accounts for changes in fuel, raw material and production conditions.
Notably, every major decarbonisation route, from alternative fuels and LC3 to carbon capture, depends on precise, data-driven control. A kiln that already wobbles on coal will wobble far more on refuse-derived fuel, and a carbon capture unit will only be as efficient as the kiln feeding it. In my view, the large wave of new Indian capacity is a once-in-a-generation chance to build plants that are digital from day one, rather than retrofitting them later at higher cost.
I have reviewed the wider structure and outlook of the Indian cement industry elsewhere [14]; the point here is that digital capability will increasingly separate the leaders from the rest.
Digital systems need trained people. Surveys discussed in my earlier article reported that about 65 per cent of Indian plant managers faced shortages of workers with the required skills, while about 20 per cent of workers could use advanced systems. These estimates depend on the population survey and method; they should not be treated as a census of the whole industry. They reinforce the need to train operators and engineers as technology changes.

Challenges on the road
AI and digital tools offer real opportunities, but four practical barriers must be addressed. Table 4 lists them and suggests what plant teams can do.
None of these barriers is a reason to abandon digitalisation. They show why plants should prepare properly, begin on a manageable scale and learn from each stage. AI cannot correct poor raw mix preparation, an unstable burner, unreliable instruments or weak maintenance practices. Good process engineering must come first.

The way forward
From my experience, the plants that succeed follow a simple roadmap:

1. Start with the kiln and one clear goal, such as specific heat consumption, kiln stability or red river incidents, and measure it honestly before and after.
2. Fix the basics first: calibrated instruments, clean data and one shared data platform.
3. Put carbon on the daily dashboard next to cost, so every decision is seen through both lenses.
4. Build mixed teams of process engineers, operators and data scientists, and invest seriously in reskilling.
5. Move from advisory to closed-loop control step by step, earning operator trust at each stage.
6. Scale what works across lines and plants and share the learning across the industry.

Five questions every plant head
should ask
• How much of our variation in heat consumption comes from the kiln itself, and how much from raw mix and fuel quality?
• Do we trust our critical instruments, and when were they last calibrated?
• Which daily decisions are still made from memory rather than from data?
• Have we told our operators clearly how their roles will change, and what new skills we will give them?
• Do we track carbon per tonne as closely as cost per tonne?

Prospects for the future
• AI may use better information from instruments, process historians, laboratories and soft sensors to improve its predictions.
• Some models may be updated more frequently as fuel and raw materials change. Automatic retraining should still be checked and approved before it affects critical kiln controls.
• Digital twins are likely to play a larger role in new cement plant projects, helping teams test designs and train operators before start-up.
• AI-based analysis may also support carbon capture operations. Stable kiln gas flow and composition can make capture systems easier to control, although economics depend on the full capture process.
• Carbon-adjusted profitability may become an increasingly useful way to compare operational decisions.

Conclusion
The smart kiln era is not about replacing experienced people with machines. It is about giving them better tools to make sound decisions, consistently and safely. AI, automation and digital twins can improve kiln stability, energy efficiency, quality and environmental performance. But they cannot replace reliable instruments, sound process engineering, disciplined maintenance or skilled operators. The cement industry has always been built on fire, stone and practical knowledge. Its next chapter will also use data and digital intelligence. A smart kiln is not simply a kiln controlled by AI. It is a kiln where technology
and human experience work together to produce better results.

Disclaimer: References for this article will be available on the Indian Cement Review website.

About the author:
Dr SB Hegde, a global cement industry leader with over 30 years of experience, is a Professor at Jain College of Engineering, India, and a Visiting Professor at Pennsylvania State University, USA.