How to Use IoT & Data Science to Predict Cement Clinker Quality

Apr 27, 2021

Manufacturing | 7 min READ

In the cement sector, the adoption of technology is growing at a moderate rate in comparison to sectors such as Banking, Healthcare, and Automotive. However, as technological advancement proves to be more helpful in eliminating manufacturing errors and making processes more efficient, the leading cement manufacturers are beginning to adopt the technologies.

Raj Pradhan

Practice Director

Construction and Building Materials Vertical

Birlasoft

Shobhit Joshi

Head of Delivery

Process Manufacturing Business

Birlasoft

As per a research report by McKinsey, integration, and optimization of AI in the cement industry have increased the production levels by 10%. The ability to take decisions basis the prediction data of Clinker Quality is turning out to be an asset to Cement Industry leaders. This has been enabled with AI, ML, and IoT in the overall solution mix.

Why Is Clinker Quality Prediction Critical?

Cement quality is the compressive strength of the cement where the quality of the concrete (made from the cement) determines its usability and application. The manufacturer can sell the cement in different categories and optimize pricing accordingly. Cement quality can not be changed once production is completed, which means the quality control is to be done up to the clinkerization phase of the production. The quality prediction solution enables the manufacturer to run clinker quality forecast, gives control and ability to tweak the composition of materials at Raw Mill, and control the Coal Mill for desired heat rate to be passed to Kiln, henceforth achieving the required quality of Clinker.

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Why Do Cement Manufacturers Struggle With Clinker Quality Forecast?

Several important factors are affecting the clinker quality. In the process of clinker production, strict control of these factors needs to be ensured for the production of high-quality, high-yield clinker. During the process, it often controls vulnerable to human errors, and a minor error will directly impact the clinker quality, which eventually impacts the cement's strength and quality.

Hence to avoid such a situation, the production process is executed with slightly high-quality parameters so that such human error gets knockoff in the overall production process. The challenge is to control the additional cost by ensuring the optimum use of resources and getting standard quality results; hence clinker quality forecast is essential, which will allow to adjusting the production parameters on time to control the quality even before testing the clinker quality.

What's the Solution to Predict Clinker Quality?

The following are the factors affecting the clinker quality, which should be controlled by having real-time alerts, alarms, and notifications that can be configured using IoT, AI, and ML, which will evaluate the continuous production process the correlation of all dependable parameters.

Lime Saturation Factor

The lime saturation factor (LSF) is a ratio of CaO to other oxides; it controls the proportion of cement raw meal. In the cement kiln operation, the thermal system of the Kiln can be affected by the fluctuation of LSF. The high saturation ratio will make the raw meal difficult to burn to clinker.

Oxide	CaO	SiO2	Al2O3	Fe2O3	others
Ratio	62%~67%	20%~24%	4%~7%	5%~6%	<5%

The proportion of oxides in cement clinker

Fineness & Homogenization of Raw Materials

The fineness and homogenization of raw materials have a significant impact on the clinker quality. The fineness of the raw meal needs to be contained between 12% and 16% to ensure the quality of the product clinker. If the raw meal fed to the Kiln is too coarse, it will increase the heat load of the Kiln, decrease the decomposition rate of the raw meal during the calcination process, and the solid-state reaction in the Kiln will not be done entirely, which will reduce the quality of finished clinker.

The low homogenization degree of the raw meal will cause the composition fluctuation of raw meal, which will bring difficulties to the calcination and even cause disorder of the kiln thermal system, which is not conducive to the regular operation of the kiln system.

Coal Fines

How to Use IoT & Data Science to Predict Cement Clinker Quality

Many cement plants use bituminous coal as the fuel of rotary Kiln, so the ash content, volatile matter, calorific value, fineness, and moisture of coal fines will affect the clinker calcination process of the Kiln. Coal fines are usually prepared by air swept coal mill or vertical mill. To produce high-quality Clinker, the fineness of coal fines should be controlled at about 12%, and its moisture content should be controlled within 1.0%. The machines that are usually used to dry the coal fines are rotary dryers.

Calcination Temperature

During the normal production process, the Clinker is always calcined in a fixed temperature range. According to the fluctuation of raw meal composition, the calcination temperature of Clinker is generally between 1350℃ and 1450℃.

If the temperature in the Kiln is too low, it is easy to produce underfired Clinker. Under the low-temperature condition, CaO in the raw meal is not fully absorbed to form C3S but exists in a free state, which makes the strength of calcined clinker decrease.

If the temperature in the Kiln is too high, the Clinker size will increase, which makes the Clinker dense and hard to grind. The large-sized Clinker is also easy to damage the kiln skin, which is not conducive to protecting the refractory lining of the Rotary Kiln.

Therefore, in the operation of rotary Kiln, we need to judge the calcination temperature through the parameters of current NOx and flame temperature at the kiln head and adjust the coal feeding rate, raw material feeding rate, airflow rate, secondary and tertiary air temperature, and kiln speed to control the calcination temperature in an appropriate range.

Atmosphere Inside The Kiln

Under normal conditions, the Clinker is calcined in the oxidation atmosphere; that is, there is sufficient oxygen in the rotary Kiln to make the fuel completely burn. If the oxygen content in the Kiln is insufficient, the fuel will not be able to be wholly combusted and will generate CO to form a reduction atmosphere inside the Kiln.

In the Kiln, the high value of the fuel and the heat intensity of the Clinker is not reduced completely. In the meantime, because the liquid phase in the kiln skin is led out ahead of time and the viscosity is reduced, the ventilation in the Kiln is not good, and the reduction atmosphere is heavier, forming a vicious cycle. During the calcination process in the reduction atmosphere, incomplete fuel combustion causes heat loss and thermal strength reduction in the Kiln, and the transition elements with high valence are reduced to that with low valence, which makes the clinker color abnormal. Moreover, the kiln skin will appear liquid phase in advance, which reduces its viscosity and deteriorates the Kiln's ventilation, and eventually forms an even heavier reduction atmosphere.

In the production process, we can tell if there is a reduction atmosphere by observing the clinker color and the gas composition parameters in the Kiln. If the atmosphere reduction appears, it can be eliminated by improving the ventilation condition of the Kiln and adjusting the coal feeding rate.

Cooling Process

In the clinker production of dry process, the calcined Clinker is quenched in a grated cooler or a rotary cooler after being discharged from the Kiln. Because there are two processes of liquid phase condensation and mineral intersection in the clinker cooling process, the cooling process will have a certain degree of impact on the clinker quality. High quenching speed has the following advantages:

Prevents the Clinker from pulverizing due to C2S transformation
It makes the clinker ball produce internal stress and improves its grindability
Reduces the crystallization of C3A in Clinker and improves the sulphuric acid resistance of finished cement.

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Key Takeaways

The accuracy in clinker quality prediction using IoT and AI in cement industry assures high quality of end-products with optimum and cost-effective utilization of existing resources.

The predictability reduces the storage issues and allows the manufacturers to determine the categorization and sales part of the overall business in advance. With the implementation of the closed-loop process, the possibility of any errors halting the process reduces, which drastically reduces the manufacturing and maintenance cost.

The accurate measurement of the raw materials and quality analysis helps in reducing waste and energy consumption at every stage of manufacturing. All of it results in an efficient manufacturing process and predictable quality of cement.

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