Technology
Application of computational fluid & particle dynamics for cement industry
Computational Fluid Dynamics (CFD) is widely used by the global cement Industry to design the process equipment and improve performance. CFD methodology is believed to be a complex technology by the practising engineers. This article by Vivek Vitankar of FluiDimensions, Pune and Ravindra Aglave of Siemens Digital Industries Software, Houston, TX, USA aims to describe the CFD methodology in a detailed but simpler manner.
Cement production is a highly energy intensive (thermal and electrical energy) process. A lot of effort is spent by designers on a trial and error basis to address the energy and pollutant reduction issues. These efforts have helped the industry to achieve a portion the targets over a period of time. On many occasions, the gains are temporary. A scientific approach that is at a lower cost which does not disrupt production and is faster than trial and error approach to identify the problem, its root cause and provide a robust solution is desired.
Virtual process development based (VPD) on numerical models such as Computational fluid Dynamics (CFD) has emerged as a proven technology in the process industry to design and validate the process equipment, debottleneck performance, optimize operating conditions, perform detailed "what if" studies. The equipment performance depends on the operational philosophy, equipment design, the quality of raw materials and the fuel being used. That makes computational fluid dynamics as a best tool to optimize performance equipment individually.
CFD Methodology and Workflow
After identifying an issue or a problem, initial assessment can define the objective of the analysis. Once an objective of the analysis is decided, first step is to draw a three-dimensional CAD geometry based on the GA and internal drawings of the equipment. It is very critical to have the updated drawings with all the dimensions. A site visit or a meeting with designers is recommended at this early stage.
In the second step, meshing, the full scale 3D CAD is discretized into numerous small volume elements. It is in these volume elements, the Navier-Stokes equations are solved. As a general rule, more the volume elements, better is the accuracy of the solution. Figure 1 shows schematic workflow of the entire process with input requirements.
In the third step, the underlying physics and chemistry (if necessary) of the process is represented using the mathematical models available in the CFD software. For example, multiphase physics for gas-solid flow in cyclones, combustion reactions and heat transfer modes modeling in kiln and calciner, NOx reactions etc.
To simulate the problem, an experienced CFD engineer uses the right algorithms, combination of relaxation parameters, tricks of convergence. Adequate knowledge of the software, involved physics and compute resources is a precursor of such work.
The important task of analysing the results starts after the converged solution is obtained. At this stage the understanding of the process and equipment is crucial to connect the link between CFD results and process observations. Analysing overall pressure drop and collection efficiency obtained from CFD results of the cyclone is not sufficient. One needs to look into the detailed velocity contours and vectors at different locations. It is detailed analysis that leads to pinpoint the issues in the performance. Once the issues are identified, most of the times the solution to the problem emerges.
Figure 1. Workflow involved in CFD Analysis
At this stage, we recommend to have discussion with the operations/design team as they can have limitations to implement a theoretically perfect design solution. There could be structural issues, access to the location to carry out implementation or operational challenges.
As the design solution is accepted, the delivery process starts. Delivery package includes engineering design drawings highlighting the changes in the original drawings, fabrication drawings along with the material of construction, total quantity of the material needed and benefits that would be achieved. During the implementation process, CFD engineer needs to guide the fabrication and implementation teams and ensure that the implementation is done in a right way.
Table 1: Benefits achieved in Cement Industry using CFD modeling
*Typical inputs required are drawing details including GA drawings, assembly drawings, refractory drawings, all internals, clearly marked location and sizes of all inlets and outlets. Operating conditions like gas flow rate, temperature, pressure, gas composition, fuel compostion, combustion characteristics etc.
Practices followed at FluiDimensions:
We follow the entire process described above and analyse the performance as per the design and operating conditions with respect to the objective statement. Detailed analysis tools of STAR-CCM+ gives the insight of root cause of the design or operation issue. (for example in Cyclone, Pressure drop and collection efficiency, for Calciner, O2, CO, NOx level at exit, temperature, extent of combustion, residence time distribution, regions of high and low temperature, reducing zones etc). Siemens Simcenter STAR-CCM+ offers a unique feature of design exploration that allows faster optimization over the design or operating variables.
One very important aspect of CFD simulations at FluiDimensions is model validation. It is very important that the CFD results are validated before a design solution is proposed. However, it is not possible to measure detailed velocity, temperature, species concentration at different locations in the industrial setups. Hence, we validate our CFD models and solution methodologies using the experimental data in the published literature. This approach gives us the confidence to solve industrial problems. For example, figure 2 shows comparison of axial velocity and temperature in the IFRF Coal combustion study1 and figure 3 shows the comparison of velocity profiles in cyclone2
Figure 2. Comparison of axial velocity and Temperature with the experimental data [1]
Figure 3. Comparison of tangential velocity and axial velocity with experimental data [2].
The following sections describe few case studies to get better insight of CFD process and value addition.
Case Study I: Performance Improvement of a Cyclone Separator
Based on the GA drawings, a full three dimension actual scale CAD geometry is drawn using CAD features of Siemens Simcenter STAR-CCM+. Next step is to create a polyhedral mesh with required quality criteria. Reynolds stress turbulence model was used as the flow is highly swirling in cyclones. Lagrangian Multiphase model is used to track solid particles in the cyclone. Using the information provided (like gas flow rate, temperature, pressure and solid loading % solids of gas flow rate and particle size distribution), base case simulation were carried out for the gas-solid flow using Simcenter STAR-CCM+. The software gave converged results in @5000 iterations in 8-10 hours using 32 cores. Figure 4 presents the fine polyhedral mesh, velocity and pressure contour obtained from the simulation. The pressure drop and the collection efficiency (87%) were found to match with the plant observation. To improve the collection efficiency, various design modifications (increase dip tube height, increasing roof height, tapered inlet, change involute size etc) were considered for simulation. We used the design exploration feature of STAR-CCM+. This feature automates the whole simulation process leading to rapid optimized solution. In other words, arrive at the solution faster by an automated trial and error method but in virtual space! After simulations, the pressure drop and collection efficiency was noted for every design arriving at optimized design. All the design changes and simulations take over 20-25 days which otherwise would take 30-40 days
Case Study II: Duct design optimization: Cement plants have large ducts to transport air, generally laden with particles from PF Fan, Coal Mill Fan Bag house, ESP, Coal Mills etc. Due to the space constraint, the ducting circuit ends up in multiple sharp bends leading to high pressure drops. Baffles are frequently used to obtain a uniform flow. STAR-CCM+ has been used with an automated work flow to investigate a series of baffle designs in ducts giving most uniform air flow with least pressure drop. In this instance, we used design explorer to rapidly analyse the effects of two design parameters: the number of number of turning vanes (anywhere from 1 to 10) and the dimension of each turning vane’s common radius (from 0.10 meters to 0.50 meters). Figure 5 summarizes the pressure profile for different scenarios.
Figure 5. Duct design optimization
In another case, CFD modelling using STAR-CCM+ was used for a LaFarge cement plant [3] to find an
Before
After
optimum design of a supply duct to an electrostatic precipitator [4]. By increasing the flow uniformity reduced the peak air velocity causing less. This resulted in reduction of number of cleaning cycles per day by 90% and a savings of up to $40,000 per month in maintenance & repair costs [5]. Figure 6 shows the base and modified design.
Figure 6. CFD results of duct to an ESP
Case Study III: Rotary kiln case study: Rotary Kilns consume significant amount of energy and release more than 25 tons of nitrogen oxides (NOx) per year [4] due to the high flame temperatures that result in formation of NOx. Stringent emissions control requirements are forcing operators to develop new and cost effective ways of minimizing/controlling emissions.
The most common post combustion control approaches include Selective Non-Catalytic Reduction (SNCR), and Selective Catalytic Reduction (SCR). The SNCR process involves the injection of ammonia in the form of ammonia water or urea solution in the flue-gas, at a suitable temperature to convert NOx to N2. While the SCR process adds ammonia or urea in the presence of a catalyst to selectively reduce NOx emissions from the exhaust gases.
An SNCR system’s performance in cement kilns depends on the temperature, residence time, reagent injection rate, turbulence or the degree of mixing between the injected reagent and the combustion gases, oxygen content, and baseline NOx levels in the kiln. The process is relatively ineffective at temperatures below 800oC and above 1150oC. At temperatures below 800oC, excessive amounts of ammonia are released to the atmosphere through the stack because of incomplete reagent dissociation, and at higher temperature, the reactions favour NOx formation and significantly higher reagent injection rates are required to meet the target NOx levels. The SNCR system is typically installed in the preheater of a lime kiln or the pre-calciner of a cement kilns.
The use of Computational Fluid Dynamics (CFD) to study the design and improve the performance of these systems is a cost effective alternative to expensive and time-consuming field tests. One recent case study of interest is that done by KFS [5] in which combustion and SNCR modeling of a rotary kiln with preheater in a lime plant was carried out in step wise procedure using Simcenter STAR-CCM+.
Step 1: 3D simulation of the rotary kiln with models for turbulence, chemistry, and heat transfer for the gas phase, which is coupled to an in-house, developed and validated, bed chemistry model to represent the transport and heat transfer of solids in the kiln
Figure 7. Illustration of gas phase and bed chemistry coupling
Step 2: Mapping of the exhaust gas temperature, velocity, turbulence and species profiles to be used as the inlet conditions for the 3D simulation of the preheater.
Figure 8. Flame profiles with different fuel composition
Step 3. Modeling the SNCR process in the preheater by simulating the urea injection and the subsequent reactions to obtain information related to system performance such as mixing profiles, NOx reduction, NH3 slippage etc. The two-step urea decomposition via the thermolysis and hydrolysis pathways are modeled, and the subsequent NOx reduction based on the 7-step reduced kinetic mechanism is used in the simulations.
Useful insights about the effectiveness of mixing, the gas temperatures encountered in the preheater, and the resulting NOx reduction for a given urea injection rate at specified locations can be obtained from the 3D CFD simulations.
The injector positions and the total number of injectors were varied to identify an optimum configuration that could achieve the desired NOx reduction with minimum urea slippage. The best design resulted in approximately 60% NOx reduction of the baseline furnace value with a urea slippage of less than 1 ppm.
The effect of urea flow rate on NOx reduction efficiency for the optimum configuration can then be studied and compared to field data after the installation. In one example the correct trend was captured for the percentage reduction in NOx by the CFD results as the urea flow rate was increased.
Figure 9. Urea injection location and calculated NOx distribution
The results from these studies demonstrate that CFD is a useful tool to help design and optimize the kiln and the SNCR system for effective NOx control. The potential savings associated with operating a thermally efficient kiln, and a well-controlled SNCR process with minimum urea slippage could be significant. The possibilities are endless! CFD along with a carefully planned design exploration study can be used to gain useful insights into system performance and design whether it is a rotary kiln, a cyclone separator, ESP, Calciner, Ducts, Fans, at a fraction of the time and cost that it takes to actually build and test prototypes of these systems.
References
1.Peters and Weber, "Mathematical Modelling of a 2.4 MW Swirling Pulverised Coal Flame", Combustion Science and Technology, 1997,Vol. 122, page 131-182
2.M. D. Slack, R. O. Prasad, A. Bakker, F. Boysan "Advances in Cyclone Modelling Using Unstructured Grids", TransIChemE, Vol. 78, Part A, November 2000, page 1098- 1104.
3.Porter, M. and TroutComputational Fluid Dynamics (CFD) is widely used by the global cement Industry to design the process equipment and improve performance. CFD methodology is believed to be a complex technology by the practising engineers. This article by Vivek Vitankar of FluiDimensions, Pune and Ravindra Aglave of Siemens Digital Industries Software, Houston, TX, USA aims to describe the CFD methodology in a detailed but simpler manner.
Cement production is a highly energy intensive (thermal and electrical energy) process. A lot of effort is spent by designers on a trial and error basis to address the energy and pollutant reduction issues. These efforts have helped the industry to achieve a portion the targets over a period of time. On many occasions, the gains are temporary. A scientific approach that is at a lower cost which does not disrupt production and is faster than trial and error approach to identify the problem, its root cause and provide a robust solution is desired.
Virtual process development based (VPD) on numerical models such as Computational fluid Dynamics (CFD) has emerged as a proven technology in the process industry to design and validate the process equipment, debottleneck performance, optimize operating conditions, perform detailed "what if" studies. The equipment performance depends on the operational philosophy, equipment design, the quality of raw materials and the fuel being used. That makes computational fluid dynamics as a best tool to optimize performance equipment individually.
CFD Methodology and Workflow
After identifying an issue or a problem, initial assessment can define the objective of the analysis. Once an objective of the analysis is decided, first step is to draw a three-dimensional CAD geometry based on the GA and internal drawings of the equipment. It is very critical to have the updated drawings with all the dimensions. A site visit or a meeting with designers is recommended at this early stage.
In the second step, meshing, the full scale 3D CAD is discretized into numerous small volume elements. It is in these volume elements, the Navier-Stokes equations are solved. As a general rule, more the volume elements, better is the accuracy of the solution. Figure 1 shows schematic workflow of the entire process with input requirements.
In the third step, the underlying physics and chemistry (if necessary) of the process is represented using the mathematical models available in the CFD software. For example, multiphase physics for gas-solid flow in cyclones, combustion reactions and heat transfer modes modeling in kiln and calciner, NOx reactions etc.
To simulate the problem, an experienced CFD engineer uses the right algorithms, combination of relaxation parameters, tricks of convergence. Adequate knowledge of the software, involved physics and compute resources is a precursor of such work.
The important task of analysing the results starts after the converged solution is obtained. At this stage the understanding of the process and equipment is crucial to connect the link between CFD results and process observations. Analysing overall pressure drop and collection efficiency obtained from CFD results of the cyclone is not sufficient. One needs to look into the detailed velocity contours and vectors at different locations. It is detailed analysis that leads to pinpoint the issues in the performance. Once the issues are identified, most of the times the solution to the problem emerges.
Figure 1. Workflow involved in CFD Analysis
At this stage, we recommend to have discussion with the operations/design team as they can have limitations to implement a theoretically perfect design solution. There could be structural issues, access to the location to carry out implementation or operational challenges.
As the design solution is accepted, the delivery process starts. Delivery package includes engineering design drawings highlighting the changes in the original drawings, fabrication drawings along with the material of construction, total quantity of the material needed and benefits that would be achieved. During the implementation process, CFD engineer needs to guide the fabrication and implementation teams and ensure that the implementation is done in a right way.
Table 1: Benefits achieved in Cement Industry using CFD modeling
*Typical inputs required are drawing details including GA drawings, assembly drawings, refractory drawings, all internals, clearly marked location and sizes of all inlets and outlets. Operating conditions like gas flow rate, temperature, pressure, gas composition, fuel compostion, combustion characteristics etc.
Practices followed at FluiDimensions:
We follow the entire process described above and analyse the performance as per the design and operating conditions with respect to the objective statement. Detailed analysis tools of STAR-CCM+ gives the insight of root cause of the design or operation issue. (for example in Cyclone, Pressure drop and collection efficiency, for Calciner, O2, CO, NOx level at exit, temperature, extent of combustion, residence time distribution, regions of high and low temperature, reducing zones etc). Siemens Simcenter STAR-CCM+ offers a unique feature of design exploration that allows faster optimization over the design or operating variables.
One very important aspect of CFD simulations at FluiDimensions is model validation. It is very important that the CFD results are validated before a design solution is proposed. However, it is not possible to measure detailed velocity, temperature, species concentration at different locations in the industrial setups. Hence, we validate our CFD models and solution methodologies using the experimental data in the published literature. This approach gives us the confidence to solve industrial problems. For example, figure 2 shows comparison of axial velocity and temperature in the IFRF Coal combustion study1 and figure 3 shows the comparison of velocity profiles in cyclone2
Figure 2. Comparison of axial velocity and Temperature with the experimental data [1]
Figure 3. Comparison of tangential velocity and axial velocity with experimental data [2].
The following sections describe few case studies to get better insight of CFD process and value addition.
Case Study I: Performance Improvement of a Cyclone Separator
Based on the GA drawings, a full three dimension actual scale CAD geometry is drawn using CAD features of Siemens Simcenter STAR-CCM+. Next step is to create a polyhedral mesh with required quality criteria. Reynolds stress turbulence model was used as the flow is highly swirling in cyclones. Lagrangian Multiphase model is used to track solid particles in the cyclone. Using the information provided (like gas flow rate, temperature, pressure and solid loading % solids of gas flow rate and particle size distribution), base case simulation were carried out for the gas-solid flow using Simcenter STAR-CCM+. The software gave converged results in @5000 iterations in 8-10 hours using 32 cores. Figure 4 presents the fine polyhedral mesh, velocity and pressure contour obtained from the simulation. The pressure drop and the collection efficiency (87%) were found to match with the plant observation. To improve the collection efficiency, various design modifications (increase dip tube height, increasing roof height, tapered inlet, change involute size etc) were considered for simulation. We used the design exploration feature of STAR-CCM+. This feature automates the whole simulation process leading to rapid optimized solution. In other words, arrive at the solution faster by an automated trial and error method but in virtual space! After simulations, the pressure drop and collection efficiency was noted for every design arriving at optimized design. All the design changes and simulations take over 20-25 days which otherwise would take 30-40 days
Case Study II: Duct design optimization: Cement plants have large ducts to transport air, generally laden with particles from PF Fan, Coal Mill Fan Bag house, ESP, Coal Mills etc. Due to the space constraint, the ducting circuit ends up in multiple sharp bends leading to high pressure drops. Baffles are frequently used to obtain a uniform flow. STAR-CCM+ has been used with an automated work flow to investigate a series of baffle designs in ducts giving most uniform air flow with least pressure drop. In this instance, we used design explorer to rapidly analyse the effects of two design parameters: the number of number of turning vanes (anywhere from 1 to 10) and the dimension of each turning vane’s common radius (from 0.10 meters to 0.50 meters). Figure 5 summarizes the pressure profile for different scenarios.
Figure 5. Duct design optimization
In another case, CFD modelling using STAR-CCM+ was used for a LaFarge cement plant [3] to find an
Before
After
optimum design of a supply duct to an electrostatic precipitator [4]. By increasing the flow uniformity reduced the peak air velocity causing less. This resulted in reduction of number of cleaning cycles per day by 90% and a savings of up to $40,000 per month in maintenance & repair costs [5]. Figure 6 shows the base and modified design.
Figure 6. CFD results of duct to an ESP
Case Study III: Rotary kiln case study: Rotary Kilns consume significant amount of energy and release more than 25 tons of nitrogen oxides (NOx) per year [4] due to the high flame temperatures that result in formation of NOx. Stringent emissions control requirements are forcing operators to develop new and cost effective ways of minimizing/controlling emissions.
The most common post combustion control approaches include Selective Non-Catalytic Reduction (SNCR), and Selective Catalytic Reduction (SCR). The SNCR process involves the injection of ammonia in the form of ammonia water or urea solution in the flue-gas, at a suitable temperature to convert NOx to N2. While the SCR process adds ammonia or urea in the presence of a catalyst to selectively reduce NOx emissions from the exhaust gases.
An SNCR system’s performance in cement kilns depends on the temperature, residence time, reagent injection rate, turbulence or the degree of mixing between the injected reagent and the combustion gases, oxygen content, and baseline NOx levels in the kiln. The process is relatively ineffective at temperatures below 800oC and above 1150oC. At temperatures below 800oC, excessive amounts of ammonia are released to the atmosphere through the stack because of incomplete reagent dissociation, and at higher temperature, the reactions favour NOx formation and significantly higher reagent injection rates are required to meet the target NOx levels. The SNCR system is typically installed in the preheater of a lime kiln or the pre-calciner of a cement kilns.
The use of Computational Fluid Dynamics (CFD) to study the design and improve the performance of these systems is a cost effective alternative to expensive and time-consuming field tests. One recent case study of interest is that done by KFS [5] in which combustion and SNCR modeling of a rotary kiln with preheater in a lime plant was carried out in step wise procedure using Simcenter STAR-CCM+.
Step 1: 3D simulation of the rotary kiln with models for turbulence, chemistry, and heat transfer for the gas phase, which is coupled to an in-house, developed and validated, bed chemistry model to represent the transport and heat transfer of solids in the kiln
Figure 7. Illustration of gas phase and bed chemistry coupling
Step 2: Mapping of the exhaust gas temperature, velocity, turbulence and species profiles to be used as the inlet conditions for the 3D simulation of the preheater.
Figure 8. Flame profiles with different fuel composition
Step 3. Modeling the SNCR process in the preheater by simulating the urea injection and the subsequent reactions to obtain information related to system performance such as mixing profiles, NOx reduction, NH3 slippage etc. The two-step urea decomposition via the thermolysis and hydrolysis pathways are modeled, and the subsequent NOx reduction based on the 7-step reduced kinetic mechanism is used in the simulations.
Useful insights about the effectiveness of mixing, the gas temperatures encountered in the preheater, and the resulting NOx reduction for a given urea injection rate at specified locations can be obtained from the 3D CFD simulations.
The injector positions and the total number of injectors were varied to identify an optimum configuration that could achieve the desired NOx reduction with minimum urea slippage. The best design resulted in approximately 60% NOx reduction of the baseline furnace value with a urea slippage of less than 1 ppm.
The effect of urea flow rate on NOx reduction efficiency for the optimum configuration can then be studied and compared to field data after the installation. In one example the correct trend was captured for the percentage reduction in NOx by the CFD results as the urea flow rate was increased.
Figure 9. Urea injection location and calculated NOx distribution
The results from these studies demonstrate that CFD is a useful tool to help design and optimize the kiln and the SNCR system for effective NOx control. The potential savings associated with operating a thermally efficient kiln, and a well-controlled SNCR process with minimum urea slippage could be significant. The possibilities are endless! CFD along with a carefully planned design exploration study can be used to gain useful insights into system performance and design whether it is a rotary kiln, a cyclone separator, ESP, Calciner, Ducts, Fans, at a fraction of the time and cost that it takes to actually build and test prototypes of these systems.
References
1.Peters and Weber, "Mathematical Modelling of a 2.4 MW Swirling Pulverised Coal Fl
Dr Y Chandri Naidu, Chief Technology Officer, Nextcem Consulting highlights how digital technologies are enabling Indian cement plants to improve efficiency, reduce emissions, and transition toward sustainable, low-carbon manufacturing.
Cement manufacturing is inherently resource- and energy-intensive due to high-temperature clinkerisation and extensive material handling and grinding operations. In India, where cement demand continues to grow in line with infrastructure development, producers must balance capacity expansion with sustainability commitments. Energy costs constitute a major share of operating expenditure, while process-related carbon dioxide emissions from limestone calcination remain unavoidable.
Traditional optimisation approaches, which are largely dependent on operator experience, static control logic and offline laboratory analysis, have reached their practical limits. This is especially evident when higher levels of alternative fuel and raw materials (AFR) are introduced or when raw material variability increases.
Digital technologies provide a systematic pathway to manage this complexity by enabling
real-time monitoring, predictive optimisation and integrated decision-making across cement manufacturing operations.
Digital cement manufacturing is enabled through a layered architecture integrating operational technology (OT) and information technology (IT). At the base are plant instrumentation, analysers, and automation systems, which generate continuous process data. This data is contextualised and analysed using advanced analytics and AI platforms, enabling predictive and prescriptive insights for operators and management.
Digital optimisation of energy efficiency
- Thermal energy optimisation
The kiln and calciner system accounts for approximately 60 per cent to 65 per cent of total energy consumption in an integrated cement plant. Digital optimisation focuses on reducing specific thermal energy consumption (STEC) while maintaining clinker quality and operational stability.
Advanced Process Control (APC) stabilises critical parameters such as burning zone temperature, oxygen concentration, kiln feed rate and calciner residence time. By minimising process variability, APC reduces the need for conservative over-firing. Artificial intelligence further enhances optimisation by learning nonlinear relationships between raw mix chemistry, AFR characteristics, flame dynamics and heat consumption.
Digital twins of kiln systems allow engineers to simulate operational scenarios such as increased AFR substitution, altered burner momentum or changes in raw mix burnability without operational risk. Indian cement plants adopting these solutions typically report STEC reductions in the range of 2 per cent to 5 per cent. - Electrical energy optimisation
Electrical energy consumption in cement plants is dominated by grinding systems, fans and material transport equipment. Machine learning–based optimisation continuously adjusts mill parameters such as separator speed, grinding pressure and feed rate to minimise specific power consumption while maintaining product fineness.
Predictive maintenance analytics identify inefficiencies caused by wear, fouling or imbalance in fans and motors. Plants implementing plant-wide electrical energy optimisation typically achieve
3 per cent to 7 per cent reduction in specific power consumption, contributing to both cost savings and indirect CO2 reduction.
Digital enablement of AFR
AFR challenges in the Indian context: Indian cement plants increasingly utilise biomass, refuse-derived fuel (RDF), plastic waste and industrial by-products. However, variability in calorific value, moisture, particle size, chlorine and sulphur content introduces combustion instability, build-up formation and emission risks.
Digital AFR management: Digital platforms integrate real-time AFR quality data from online analysers with historical kiln performance data. Machine learning models predict combustion behaviour, flame stability and emission trends for different AFR combinations. Based on these predictions, fuel feed distribution, primary and secondary air ratios, and burner momentum are dynamically adjusted to ensure stable kiln operation. Digitally enabled AFR management in cement plants will result in increased thermal substitution rates by 5-15 percentage points, reduced fossil fuel dependency, and improved kiln stability.
Digital resource and raw material optimisation
Raw mix control: Raw material variability directly affects kiln operation and clinker quality. AI-driven raw mix optimisation systems continuously adjust feed proportions to maintain target chemical parameters such as Lime Saturation Factor (LSF), Silica Modulus (SM), and Alumina Modulus (AM). This reduces corrective material usage and improves kiln thermal efficiency.
Clinker factor reduction: Reducing clinker factor through supplementary cementitious materials (SCMs) such as fly ash, slag and calcined clay is a key decarbonisation lever. Digital models simulate blended cement performance, enabling optimisation of SCM proportions while maintaining strength and durability requirements.
Challenges and strategies for digital adoption
Key challenges in Indian cement plants include data quality limitations due to legacy instrumentation, resistance to algorithm-based decision-making, integration complexity across multiple OEM systems, and site-specific variability in raw materials and fuels.
Successful digital transformation requires strengthening the data foundation, prioritising high-impact use cases such as kiln APC and energy optimisation, adopting a human-in-the-loop approach, and deploying modular, scalable digital platforms with cybersecurity by design.
Future Outlook
Future digital cement plants will evolve toward autonomous optimisation, real-time carbon intensity tracking, and integration with emerging decarbonisation technologies such as carbon capture, utilisation and storage (CCUS). Digital platforms will also support ESG reporting and regulatory compliance.
Digital pathways offer a practical and scalable solution for sustainable cement manufacturing in India. By optimising energy consumption, enabling higher AFR substitution and improving resource efficiency, digital technologies deliver measurable environmental and economic benefits. With appropriate data infrastructure, organisational alignment and phased implementation, digital transformation will remain central to the Indian cement industry’s low-carbon transition.
About the author:
Dr Y Chandri Naidu is a cement industry professional with 30+ years of experience in process optimisation, quality control and quality assistance, energy conservation and sustainable manufacturing, across leading organisations including NCB, Ramco, Prism, Ultratech, HIL, NCL and Vedanta. He is known for guiding teams, developing innovative plant solutions and promoting environmentally responsible cement production. He is also passionate about mentoring professionals and advancing durable, resource efficient technologies for future of construction materials.
Cement Demand Revives As Prices Decline In Q3 FY26
Refractory demands in our kiln have changed
Digital supply chain visibility is critical
Redefining Efficiency with Digitalisation
Cement Additives for Improved Grinding Efficiency
Cement Demand Revives As Prices Decline In Q3 FY26
Refractory demands in our kiln have changed
Digital supply chain visibility is critical
Redefining Efficiency with Digitalisation
Cement Additives for Improved Grinding Efficiency
-
Concrete4 weeks agoAris Secures Rs 630 Million Concrete Supply Order
-
Concrete4 weeks agoNITI Aayog Unveils Decarbonisation Roadmaps
-
Economy & Market3 weeks agoBudget 2026–27 infra thrust and CCUS outlay to lift cement sector outlook
-
Concrete4 weeks agoJK Cement Commissions 3 MTPA Buxar Plant, Crosses 31 MTPA