As cement clinker production moves through its digital transformation, the rotary kiln line is no longer just an equipment-monitoring use case. It is a complex industrial site where the thermal regime, clinker quality, energy efficiency, environmental performance, and power load are tightly coupled.
On a cement production floor, data is never scarce. The real challenge is that even as the rotary kiln, preheater, clinker cooler, and quality-control instruments generate large volumes of data in real time, the site still struggles to answer several key business questions quickly: When and where did the anomaly first appear? How much will it disturb downstream processes? Is this a local thermal fluctuation, or a genuine risk to the clinker free-lime index and the kiln-line heat balance?
This is where data-driven kiln-line monitoring starts to matter. What plants need is not simply putting curves onto a screen. They need to use data to sort out cascading cause and effect and move from reactive response to proactive judgment.
Cement clinker production lines: the challenge of coordinated monitoring under high-temperature, tightly coupled processes
Cement clinker production is a typical complex process line. It covers the raw material section, the burning section, the emissions control system, finish grinding, and the quality control center. The rotary kiln KL-01, calciner PC-01, preheater C1-C5, and clinker cooler GC-01 in the burning section run continuously at temperatures above 1400°C, with intense gas-solid heat exchange and very strong physical and thermal coupling between the equipment and the material.
For example, the cooling airflow of clinker cooler GC-01 determines not only the clinker cooling efficiency. The secondary and tertiary air recovered from it are also the heat source on which the rotary kiln and the calciner depend to hold their temperatures. If the cooling airflow in the first section of the clinker cooler is insufficient, the clinker discharge temperature rises abnormally, the secondary and tertiary air temperatures drop sharply, and the fuel combustion in the calciner and the kiln-head burner is directly disturbed. The system is forced to add coal to compensate, which in turn triggers emission fluctuations and even blockage in the kiln tail flue.
Under this kind of high-temperature, continuous, cascading system, single-point device monitoring cannot reconstruct the full picture of the process chain. When the free lime (f-CaO) index of the clinker shows an anomaly, operators often have to spend hours comparing historical trends before they can confirm whether the cause is the raw meal fineness from the vertical roller mill exceeding its limit, unstable calciner temperature, or uneven physical airflow distribution in the clinker cooler.
Plenty of alarms, but decisions stay slow: the operational pain points of complex thermal processes
The same fluctuation can be monitored, but its cause is still hard to identify
For cement clinker, the normal range of a parameter shifts dynamically with kiln temperature and cooling airflow. Take a drop in the secondary air temperature of the rotary kiln. It could be a natural fall caused by a decrease in clinker discharge, or it could be failed cooling caused by a local blockage of the clinker cooler grate plates. If operators look only at the secondary air temperature reading, they cannot directly diagnose equipment degradation. They must cross-check it against the context of the under-grate pressure of the clinker cooler and the fan load.
A single-point alarm has triggered, but the chain reaction is hard to judge
Thermal anomalies rarely stay confined to one piece of equipment. A rising calciner temperature leads to fuel-rich pulverized-coal combustion, lowers the oxygen at the outlet, and causes liquid-phase material formation and build-up on the inner wall of the feed pipe. That in turn raises the outlet negative pressure and finally forces the rotary kiln to cut the raw meal feed rate. In conventional monitoring, these data are spread across different DCS screens, and when faced with a flood of alarms, operators find it hard to build this cascading cause-and-effect chain across the calciner, preheater, and rotary kiln quickly in their minds.
The business consequences are not abstract: clinker downgrades and emissions overruns carry heavy losses
Slow decisions and misjudgments show up directly on the company’s bottom line.
- In the cooling-insufficiency anomaly (shifts SH-011 to SH-012), grate blockage in the clinker cooler pushed the discharge temperature to 155°C. XRF analysis showed the free lime (f-CaO) index rising to 1.9% (the acceptance standard is 1.5% or lower), so 450 tons of clinker were downgraded to low-grade cement raw material because the free lime exceeded the limit, a direct loss of about US$6,000. At the same time, the waste heat power generation output dropped sharply, cutting generation by 7,500 kWh for an additional loss of about US$670, for a total economic loss of about US$6,700.
- In the build-up blockage anomaly (shifts SH-016 to SH-017), calciner overheating caused severe blockage of the C5 feed pipe, forcing the rotary kiln to cut production for 6 hours. This reduced clinker output by 480 tons in total and consumed 3 tons of standard coal equivalent without useful output through over-combustion, a loss of about US$2,800. Losses that steep call for a data system with closed-loop process analysis.
From passive monitoring to proactive judgment: reorganizing the data chain
Object-oriented modeling: scattered tags return to a unified business view
To give thermal data business meaning, it has to be mapped onto the physical production line in a structured way. TDengine uses Industrial Ontology modeling to organize the raw material vertical mill RM-01, rotary kiln KL-01, clinker cooler GC-01, SNCR denitrification system SN-01, and quality-control instrument XRF-01 into a tree-shaped asset system.
Figure 1: A tree hierarchy organizes plant assets and measurement points into a unified business view
Once the data carries ontology relationships, each tag curve automatically comes with its equipment attributes and process position. While troubleshooting, operators can trace upstream and downstream parameters directly along the asset tree, giving root-cause analysis and quality tracing complete data support.
Real-time analysis and event linkage: alarms return to full process context
On top of the object model, the TDengine real-time analysis engine continuously runs cross-device computations on data streams such as temperature, negative pressure, and flow across the cement kiln line. When it detects an abnormal trend, the system not only triggers a notification but also packages the associated process data into an event context and pushes it to the decision interface.
The built-in intelligent query and proactive recommendations features let users retrieve analysis results dynamically through natural language. When a stretch of under-grate pressure rises abnormally, the system proactively pulls the trends of the kiln-head negative pressure and secondary air temperature and recommends operations such as clearing build-up or lowering the grate speed, cutting decision time.
Figure 2: AI-recommended analyses and creating real-time analyses through natural language
Figure 3: General information settings for real-time analysis
Figure 4: Trigger conditions for real-time analysis
Process analysis and AI insight: judgment shifts from experience-driven to evidence-driven
Once multi-source thermal data is objectified and linked to anomalies, on-site troubleshooting escapes the limits of the traditional “call a meeting” approach and of senior experts deciding by instinct.
TDengine provides an AI question-and-answer service for process analysis and shift comparison. For a specific anomalous shift (such as the SH-011 cooling-insufficiency event), the system automatically pulls the corresponding thermal parameters of a golden shift (SH-005), overlays them for comparison, and uses AI to compute the absolute deviation on each dimension. The AI agent also automatically retraces the link between equipment and quality to verify the root-cause hypothesis, and finally produces a causal analysis report with multiple data charts, so that field handling is backed by documented evidence.
Figure 5: AI interpretation and data mining on the analysis panel
Figure 6: AI event root-cause analysis
The analysis loop in typical anomaly scenarios
Anomaly case 1: insufficient clinker cooler cooling damages clinker quality and cuts waste-heat power generation
In shift SH-011, the system’s first-section under-grate pressure alarm triggered at 09:30. The under-grate pressure of GC-01 rose from a normal 4500 Pa to 5200 Pa (a Warning, sustained for more than 10 minutes), and although the cooling fan speed was automatically raised to 90%, the pressure did not return to normal. At 10:30 the clinker discharge temperature rose from 95°C to 120°C and peaked at 155°C by 12:00. At the same time, the secondary air temperature dropped from 1050°C to 980°C and the tertiary air temperature to 780°C, causing the calciner temperature to fluctuate and forcing coal feed up to 22 t/h to compensate.
After the alarm triggered, the operations team in the central control room loaded the data into the process analysis panel and ran a three-step trace.
Step 1: Confirm the physical degradation of the cooling system.
The GC-01 trend curves show under-grate pressure climbing steadily from 4500 Pa to 5200 Pa, the clinker temperature at the outlet soaring from 95°C to 155°C, and the waste-heat gas temperature following from 280°C to 320°C. This indicates that resistance inside the clinker cooler rose sharply: the cooling air path was physically blocked and heat accumulated in the waste-heat gas.
Step 2: Trace the cross-device heat transfer chain.
Overlay the secondary air temperature of KL-01 and the tertiary air temperature of PC-01. As heat exchange in the clinker cooler deteriorated, the recovered air temperature fell sharply, with secondary air dropping to 980°C and tertiary air to 780°C. This confirms that insufficient cooling caused a cross-device thermal imbalance, and the calciner had to raise the coal feed (from 19.5 to 22 t/h) to keep the reaction going.
Step 3: Quantify the finished clinker quality and the economic loss.
Pull the shift test results of the X-ray fluorescence analyzer XRF-01. In the later part of shift SH-011, the free lime (f-CaO) index rose to 1.9%, clearly above the 1.5% acceptance control line. The anomaly left 450 tons of clinker in this phase nonconforming and it was downgraded, and the waste-heat generator load dropped, for a total economic loss of about US$6,700.
Figure 7: Judgment path for the abnormal rise in the first-section plenum pressure of the clinker cooler and out-of-limit clinker free lime
Through the three-step method, the site identified the root cause: first-section grate plates locally jammed by large clinker chunks → rising resistance and unbalanced airflow → a sharp drop in clinker heat-recovery efficiency → lower secondary and tertiary air temperatures with incomplete coal combustion → insufficient clinker cooling with out-of-limit f-CaO.
Anomaly case 2: calciner temperature runaway causes kiln inlet build-up blockage and production cuts
In shift SH-016, the anomaly began with a deviation in the weighing mechanism of the coal feed scale on calciner PC-01. From 10:00, the actual coal feed ran about 1.5 t/h higher than the value shown by the system. At 11:00 the calciner temperature rose to 945°C and the outlet oxygen fell from 2.5% to 1.8% (a Warn alarm triggered). At 11:30 the calciner temperature ran out of control, climbing to 965°C (above the 920°C Major alarm line). At 12:00 the negative pressure at the C5 feed pipe outlet jumped from -850 Pa to -1100 Pa (a Critical alarm), and the system was forced to cut production: the raw meal feed to the rotary kiln dropped from 320 t/h to 240 t/h and then to 200 t/h while the blockage was cleared, while the NOx concentration from the SNCR denitrification system rose to 380 mg/Nm³.
For this event, the operations team ran a causal trace through process analysis.
Step 1: Identify the fuel-rich combustion and overheating state.
Look at the temperature and oxygen trends of calciner PC-01. The data show the temperature climbing in quick succession to 965°C while the outlet oxygen falls to 1.8%. This indicates a clear oxygen-deficient, fuel-rich combustion state in the calciner, with excessive heat release that readily triggers liquid-phase sintering of the material.
Step 2: Confirm the physical evolution of the feed pipe blockage.
Compare the C5 outlet negative pressure with the material flow indicators. As the calciner temperature went out of limit, the feed pipe negative pressure jumped from a normal -850 Pa to -1100 Pa, showing that liquid-phase material quickly stuck to the pipe wall and formed build-up, raising local draft resistance and making it hard for material to pass through.
Step 3: Quantify the production cuts and the environmental-penalty risk.
Track the rotary kiln feed rate and the denitrification indicators. Because the feed pipe was partly blocked, the raw meal feed to the kiln had to be cut from 320 t/h to 200 t/h to limit production and clear the blockage. That cut clinker output by 480 tons directly and burned 3 tons of standard coal to no purpose, an economic loss of about US$2,800. At the same time, fuel-rich combustion pushed the NOx concentration to 380 mg/Nm³, close to the 400 mg/Nm³ environmental red line.
Figure 8: Judgment path for calciner overheating, feed pipe build-up, and production cuts
Along this chain, the root cause was determined: a fault in the coal feed scale that over-injected coal, plus fluctuation in the burnability of the raw meal → fuel-rich combustion and overheating in the calciner → liquid-phase sintering and build-up of material in the C5 feed pipe of the preheater → a sudden rise in discharge resistance that blocked the channel → a forced production cut with manual clearing at the rotary kiln → reduced output accompanied by NOx above the limit.
Cement clinker production monitoring: what matters is data-driven decisions
In a continuous, high-heat cement process, the key to building an intelligent system has never been how many tags are connected or how many pages are stacked. It is whether the data can, within a critical window of tens of minutes, give operators actionable operating guidance backed by process logic.
TDengine merges scattered DCS tags into correlatable process objects and weaves single-equipment alarms into event chains with cause and effect. As the data flows, it becomes the business judgment needed to resolve out-of-limit clinker free lime, preheater blockages, and energy-efficiency losses, safeguarding fine-grained operations in the cement and building materials industry.
TDengine includes a high-performance, distributed time-series database, Industrial Ontology modeling and an Industrial Agent Runtime, providing a full-stack solution for industrial data streams from collection and storage through real-time analysis to visualization, event management, and root-cause analysis. To learn more about TDengine, visit the official website at www.tdengine.com and download it for free.
Try it yourself
Install and deploy TDengine
Visit the TDengine Download Center, select TDengine All-in-One, choose the deployment platform and architecture that matches your environment, and follow the guided steps to complete the installation.
Load the sample data
On first activation, choose Cement kiln production analysis in the sample data loading screen and wait for it to finish loading.
For customers who have already activated the product, click the user avatar in the top-right corner, open the Management Console, select Sample Data on the left, then choose Cement kiln production analysis to load it. Wait a few minutes for it to finish and you can start exploring.
Cement kiln production analysis
