Technology
Intelligent Optimisation
The most important physical property of particulate samples is particle size. Particle size measurement is routinely carried out across a wide range of industries and is often a critical parameter in the manufacture of many products.
In the quest to optimise our cement production processes, we must understand the process itself, in terms of what we are producing and what we are producing it from.
Why measure particle properties?
There are two main reasons why many industries routinely employ particle characterisation techniques:
Better control of product quality
In an increasingly competitive global economy, better control of product quality delivers real economic benefits such as:
- ability to charge a higher premium for your product;
- reduce customer rejection rates and lost orders;
- demonstrate compliance in regulated markets.
Better understanding of products, ingredients and processes
In addition to controlling product quality, a better understanding of how particle properties affect your products, ingredients and processes will allow you to:
- improve product performance;
- troubleshoot manufacturing and supply issues;
- optimise the efficiency of manufacturing processes;
- increase output or improve yield;
- stay ahead of the competition;
Particle Properties
Particle size
By far the most important physical property of particulate samples is particle size. Particle size measurement is routinely carried out across a wide range of industries and is often a critical parameter in the manufacture of many products. Particle size has a direct influence on material properties such as:
- reactivity or dissolution rate;
- stability in suspension;
- efficacy of application;
- texture and feel;
- appearance;
- flowability and handling;
- packing density and porosity.
Measuring particle size and understanding how it affects your products and processes can be critical to the success of many manufacturing businesses.
What to do with particle size data
In order to simplify the interpretation of particle size distribution data, a range of statistical parameters can be calculated and reported. The choice of the most appropriate statistical parameter for any given sample will depend upon how that data will be used and with what it will be compared. For example, if you wanted to report the most common particle size in your sample, you could choose between the following parameters:
- Mean – ?average, size of a population;
- Median – size in the middle of a frequency distribution;
- Mode – size with highest frequency.
If the shape of the particle size distribution is asymmetric, as is often the case for many samples, you would not expect these three parameters to be exactly equivalent, as illustrated in Figure 1.
Means
There are many different means that can be defined, depending upon how the distribution data is collected and analysed. The three most commonly used for particle sizing are described below.
Number length mean D[1,0] or Xnl
The number length mean, often referred to as the arithmetic mean, is most important when the number of particles is of interest, e.g., in particle counting applications. It can only be calculated if we know the total number of particles in the sample, and is therefore limited to particle counting applications.
Surface area moment mean D[3, 2] or Xsv
The surface area mean (Sauter Mean Diameter) is most relevant when the specific surface area is important e.g., bioavailability, reactivity, dissolution. It is most sensitive to the presence of fine particulates in the size distribution.
Volume moment mean D[4, 3] or Xvm
The volume moment mean (De Brouckere Mean Diameter) is relevant for many samples as it reflects the size of those particles which constitute the bulk of the sample volume. It is most sensitive to the presence of large particulates in the size distribution.
An example of the surface area and volume moment means is shown in the particle size distribution below. If the aim is to monitor the size of the coarse particulates that make up the bulk of this sample, then the D[4,3] would be most appropriate. If, on the other hand, it is actually more important to monitor the proportion of fines present, then it might be more appropriate to use the D[3,2].
Percentiles
For volume-weighted particle size distributions, such as those measured by laser diffraction, it is often convenient to report parameters based upon the maximum particle size for a given percentage volume of the sample.
Percentiles are defined as XaB where:
- X= parameter, usually D for diameter
- a = distribution weighting, e.g., n for number, v for volume, i for intensity
- B = percentage of sample below this particle size e.g. 50 per cent, sometimes written as a decimal fraction i.e., 0.5
For example, the Dv50 would be the maximum particle diameter below which 50 per cent of the sample volume exists – also known as the median particle size by volume. The most common percentiles reported are the Dv10, Dv50 and Dv90, as illustrated in the frequency and cumulative plots in Figure 2.
By monitoring these three parameters, it is possible to see if there are significant changes in the main particle size, as well as changes at the extremes of the distribution, which could be due to the presence of fines, as shown in the particle size distribution in Figure 3, or oversized particles/agglomerates.
Particle shape
As well as particle size, the shape of constituent particles can also have a significant impact upon the performance or processing of particulate materials. Many industries are now also making particle shape measurements in addition to particle size measurements in order to gain a better understanding of their products and processes.
How do we define particle shape?
Particles are complex three-dimensional objects and, as with particle size measurement, some simplification of the description of the particle is required in order to make measurement and data analysis feasible. Particle shape is most commonly measured using imaging techniques, where the data collected is a two-dimensional projection of the particle profile. Particle shape parameters can be calculated from this two-dimensional projection using simple geometrical calculations.
Particle form
Aspect ratio can be used to distinguish between particles that have regular symmetry, such as spheres or cubes, and particles with different dimensions along each axis, such as needle shapes or ovoid particles. Other shape parameters that can be used to characterise particle form include elongation and roundness.
Particle outline
As well as enabling the detection of agglomerated particles, the outline of a particle can provide information about properties such as surface roughness. Particles with very smooth outlines will have a convexity/solidity value close to 1, whereas particles with rough outlines, or agglomerated primary particles, will have consequently lower convexity/solidity values.
Universal shape parameters
Some shape parameters capture changes in both particle form and outline. Monitoring these can be useful where both form and outline may influence the behaviour of the material being measured. The most commonly used parameter is circularity. Circularity is often used to measure how close a particle is to a perfect sphere, and can be applied in monitoring properties such as abrasive particle wear. However, care should be exercised in interpreting the data, since any deviations could be due to either changes in surface roughness or physical form, or both.
While circularity can be very useful for some applications, it is not suitable for all situations. To date, there is no definition of a universal shape parameter that will work in every case. In reality, careful consideration is necessary to determine the most suitable parameter for each specific application.
Summary
This article touches the surface of the different means we have at our disposal to analyse particle properties. Such information can be used to optimise and refine our cement production processes, increasing the efficiency and product quality.
(This article has been authored by Dr Michael Caves, Malvern Aimil Instruments Pvt Ltd, New Delhi).
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.
Refractory demands in our kiln have changed
Digital supply chain visibility is critical
Redefining Efficiency with Digitalisation
Cement Additives for Improved Grinding Efficiency
Digital Pathways for Sustainable Manufacturing
Refractory demands in our kiln have changed
Digital supply chain visibility is critical
Redefining Efficiency with Digitalisation
Cement Additives for Improved Grinding Efficiency
Digital Pathways for Sustainable Manufacturing
-
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
-
Concrete3 weeks agoJK Cement Commissions 3 MTPA Buxar Plant, Crosses 31 MTPA