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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).

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Concrete

Human Factor in Grinding Optimisation

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Ponnusamy Sampathkumar, Consultant – Process Optimisation and Training, discusses the role of skilled operators as the decisive link between advanced additives, digital control and world-class mill performance.

The industry always tries to reduce the number of operators in the Centre Control Room. (CCR) Though the concept was succeeded to certain extent, still we need a skilled person in the CCR.
In an era where artificial intelligence (AI) grinding aids, performance enhancers, and digital optimisation tools are becoming increasingly sophisticated, it’s tempting to believe that chemistry alone can solve the challenges of mill efficiency. Yet plants that consistently outperform their peers share one common trait: highly skilled operators who understand the mill as a living system, not just a machine.
Additives can improve flowability, reduce agglomeration, and enhance separator efficiency, but they cannot replace the nuanced judgement that comes from experience. Grinding is a dynamic process influenced by raw material variability, moisture, liner wear, ball charge distribution, ventilation, and separator loading. No additive can fully compensate for poor control of these fundamentals.

Operators see what additives cannot
When I joined the cement industry in 1981, not much modernisation was available then. Mostly the equipment was run from the local panel. Once I was visiting the cement mills section. The cement mills were water sprayed over the shell to reduce the temperature to avoid the gypsum disintegration.
The operator stopped the feeding for one of the mills. When I asked the reason, he replied that mill was getting jammed, and he added that he could understand the mill condition by its sound. I also learned that and it was useful throughout my career. In another plant I saw the ‘Electronic Ear,’ which checked the sound of the mill and the signal was looped with feed control!
Whatever modernisation we achieve, it is from the human factor that the development starts.
Additives respond to conditions; operators interpret them.
A skilled operator can detect subtle shifts, like a change in mill sound, a slight variation in circulating load, or a drift in separator cut point. It’s long before instrumentation flags a problem. These micro-observations often prevent major efficiency losses.
Additives work best when the process is stable
I would like to share one real time incident. The mill was running on auto mode looped with the mill outlet bucket elevator kilowatt. (KW)There was a decrease in the KW, and the mill feed was increased by the auto control (PID). After a while, the operator stopped both the feed and the mill. He asked the local operator to check the airslide between mill outlet and the elevator. They found the airslide was jammed and no material flow to the elevator!
The operator deduced the abnormality by his experience by seeing the conditions and the rate of increase of the feed by the auto control.
It’s always the human factor that adds value to the optimisation.

Grinding aids are multipliers,
not magicians.
They deliver maximum benefit only when:
• Mill ventilation is correct
• Ball charge is balanced
• Feed moisture is controlled
• Separator speed and loading are improved
• Blaine targets are realistic
Without these fundamentals, even advanced additives may become costly investments. The operator is responsible for ensuring process stability, whether using a ball mill or a vertical mill. After ensuring the system is stable, the operator observes it briefly before transitioning to automatic control. If there is any anomaly in the system the operator at once takes control of the system, stabilises and bring back to auto control.

Skilled operators adapt in real time
It will be interesting to note that the operators who operate from local panel start to operate from DCS also. They have the experience and the ability to adapt the changes. Operator checks each parameter deeply. Any meagre change in the parameters is also visible to him.
Raw materials change. Weather changes. Wear patterns change.
A skilled operator adjusts:
• Feed rate
• Water injection
• Separator speed
• Grinding pressure (in VRMs)
• Mill load distribution.
These adjustments require intuition built from years of experience, something no additive can replicate.

Human insight prevents over reliance on additives
Plants sometimes increase additive dosage to mask deeper issues like:
• Poor clinker quality
• Inadequate drying capacity
• Incorrect ball gradation
• High residue due to worn separator internals.


A knowledgeable operator finds root causes instead of chasing temporary chemical fixes.
The real optimisation sweet spot is reached when:
• Operators understand how additives interact with their specific mill.
• Additive suppliers collaborate with plant teams.
• Process data is interpreted by humans who know the mill’s behaviour.
This constructive collaboration consistently delivers:
• Lower kWh/t
• Higher throughput
• Better product consistency
• Optimum standard deviation.

Advanced additives are powerful tools, but they are not substitutes for human ability. Grinding optimisation is ultimately a human driven discipline, where skilled operators make the difference between average performance and world class efficiency. Additives enhance the process but operators
control it.

About the author:
Ponnusamy Sampathkumar, Consultant – Process Optimisation and Training, is a seasoned cement process consultant with 43+ years of global experience in plant operations, process optimisation, refractory management, safety systems and training multicultural teams across international cement plants.

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Concrete

Redefining Efficiency with Digitalisation

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Professor Procyon Mukherjee discusses how as the cement industry accelerates its shift towards digitalisation, data-driven technologies are becoming the mainstay of sustainability and control across the value chain.

The cement industry, long perceived as traditional and resistant to change, is undergoing a profound transformation driven by digital technologies. As global infrastructure demand grows alongside increasing pressure to decarbonise and improve productivity, cement manufacturers are adopting data-centric tools to enhance performance across the value chain. Nowhere is this shift more impactful than in grinding, which is the energy-intensive final stage of cement production, and in the materials that make grinding more efficient: grinding media and grinding aids.

The imperative for digitalisation
Cement production accounts for roughly 7 per cent to 8 per cent of global CO2 emissions, largely due to the energy intensity of clinker production and grinding processes. Digital solutions, such as AI-driven process controls and digital twins, are helping plants improve stability, cut fuel use and reduce emissions while maintaining consistent product quality. In one deployment alongside ABB’s process controls at a Heidelberg plant in Czechia, AI tools cut fuel use by 4 per cent and emissions by 2 per cent, while also improving operational stability.
Digitalisation in cement manufacturing encompasses a suite of technologies, broadly termed as Industrial Internet of Things (IIoT), AI and machine learning, predictive analytics, cloud-based platforms, advanced process control and digital twins, each playing a role in optimising various stages of production from quarrying to despatch.

Grinding: The crucible of efficiency and cost
Of all the stages in cement production, grinding is among the most energy-intensive, historically consuming large amounts of electricity and representing a significant portion of plant operating costs. As a result, optimising grinding operations has become central to digital transformation strategies.
Modern digital systems are transforming grinding mills from mechanical workhorses into intelligent, interconnected assets. Sensors throughout the mill measure parameters such as mill load, vibration, mill speed, particle size distribution, and power consumption. This real-time data, fed into machine learning and advanced process control (APC) systems, can dynamically adjust operating conditions to maintain optimal throughput and energy usage.
For example, advanced grinding systems now predict inefficient conditions, such as impending mill overload, by continuously analysing acoustic and vibration signatures. The system can then proactively adjust clinker feed rates and grinding media distribution to sustain optimal conditions, reducing energy consumption and improving consistency.

Digital twins: Seeing grinding in the virtual world
One of the most transformative digital tools applied in cement grinding is the digital twin, which a real-time virtual replica of physical equipment and processes. By integrating sensor data and
process models, digital twins enable engineers to simulate process variations and run ‘what-if’
scenarios without disrupting actual production. These simulations support decisions on variables such as grinding media charge, mill speed and classifier settings, allowing optimisation of energy use and product fineness.
Digital twins have been used to optimise kilns and grinding circuits in plants worldwide, reducing unplanned downtime and allowing predictive maintenance to extend the life of expensive grinding assets.

Grinding media and grinding aids in a digital era
While digital technologies improve control and prediction, materials science innovations in grinding media and grinding aids have become equally crucial for achieving performance gains.
Grinding media, which comprise the balls or cylinders inside mills, directly influence the efficiency of clinker comminution. Traditionally composed of high-chrome cast iron or forged steel, grinding media account for nearly a quarter of global grinding media consumption by application, with efficiency improvements translating directly to lower energy intensity.
Recent advancements include ceramic and hybrid media that combine hardness and toughness to reduce wear and energy losses. For example, manufacturers such as Sanxin New Materials in China and Tosoh Corporation in Japan have developed sub-nano and zirconia media with exceptional wear resistance. Other innovations include smart media embedded with sensors to monitor wear, temperature, and impact forces in real time, enabling predictive maintenance and optimal media replacement scheduling. These digitally-enabled media solutions can increase grinding efficiency by as much as 15 per cent.
Complementing grinding media are grinding aids, which are chemical additives that improve mill throughput and reduce energy consumption by altering the surface properties of particles, trapping air, and preventing re-agglomeration. Technology leaders like SIKA AG and GCP Applied Technologies have invested in tailored grinding aids compatible with AI-driven dosing platforms that automatically adjust additive concentrations based on real-time mill conditions. Trials in South America reported throughput improvements nearing 19 per cent when integrating such digital assistive dosing with process control systems.
The integration of grinding media data and digital dosing of grinding aids moves the mill closer to a self-optimising system, where AI not only predicts media wear or energy losses but prescribes optimal interventions through automated dosing and operational adjustments.

Global case studies in digital adoption
Several cement companies around the world exemplify digital transformation in practice.
Heidelberg Materials has deployed digital twin technologies across global plants, achieving up to 15 per cent increases in production efficiency and 20 per cent reductions in energy consumption by leveraging real-time analytics and predictive algorithms.
Holcim’s Siggenthal plant in Switzerland piloted AI controllers that autonomously adjusted kiln operations, boosting throughput while reducing specific energy consumption and emissions.
Cemex, through its AI and predictive maintenance initiatives, improved kiln availability and reduced maintenance costs by predicting failures before they occurred. Global efforts also include AI process optimisation initiatives to reduce energy consumption and environmental impact.

Challenges and the road ahead
Despite these advances, digitalisation in cement grinding faces challenges. Legacy equipment may lack sensor readiness, requiring retrofits and edge-cloud connectivity upgrades. Data governance and integration across plants and systems remains a barrier for many mid-tier producers. Yet, digital transformation statistics show momentum: more than half of cement companies have implemented IoT sensors for equipment monitoring, and digital twin adoption is growing rapidly as part of broader Industry 4.0 strategies.
Furthermore, as digital systems mature, they increasingly support sustainability goals: reduced energy use, optimised media consumption and lower greenhouse gas emissions. By embedding intelligence into grinding circuits and material inputs like grinding aids, cement manufacturers can strike a balance between efficiency and environmental stewardship.
Conclusion
Digitalisation is not merely an add-on to cement manufacturing. It is reshaping the competitive and sustainability landscape of an industry often perceived as inertia-bound. With grinding representing a nexus of energy intensity and cost, digital technologies from sensor networks and predictive analytics to digital twins offer new levers of control. When paired with innovations in grinding media and grinding aids, particularly those with embedded digital capabilities, plants can achieve unprecedented gains in efficiency, predictability and performance.
For global cement producers aiming to reduce costs and carbon footprints simultaneously, the future belongs to those who harness digital intelligence not just to monitor operations, but to optimise and evolve them continuously.

About the author:
Professor Procyon Mukherjee, ex-CPO Lafarge-Holcim India, ex-President Hindalco, ex-VP Supply Chain Novelis Europe,
has been an industry leader in logistics, procurement, operations and supply chain management. His career spans 38 years starting from Philips, Alcan Inc (Indian Aluminum Company), Hindalco, Novelis and Holcim. He authored the book, ‘The Search for Value in Supply Chains’. He serves now as Visiting Professor in SP Jain Global, SIOM and as the Adjunct Professor at SBUP. He advises leading Global Firms including Consulting firms on SCM and Industrial Leadership and is a subject matter expert in aluminum and cement. An Alumnus of IIM Calcutta and Jadavpur University, he has completed the LH Senior Leadership Programme at IVEY Academy at Western University, Canada.

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Concrete

Digital Pathways for Sustainable Manufacturing

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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.

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