Technology
Transforming cement industry by using AI in Blaine prediction
Published
5 years agoon
By
admin
Delivering high-quality products, while keeping production costs low and plant efficiency high, is an ongoing challenge for all process industries. Traditional industries such as cement, face numerous challenges to reduce the cost of operations while maximising the yield and improving quality at the same time. With the global pandemic leading to unprecedented changes around the world, it could not have been more disruptive for the cement industry to embrace digital technologies and harness big data to improve productivity, availability, and quality across the value chain at this time, but for many operators, the results are already showing.
Advanced process control and analytics
Cement is an energy-intensive industry in which the grinding circuits use more electrical energy and account for most of the manufacturing cost. Advanced process control (APC) and related optimisation strategies can help cement manufacturers to reap the real efficiency benefits of digital technology, without sacrificing stability or quality, even as business changes and grows.
Digital APC solutions control, stabilize and optimise various cement processes, helping plant managers achieve profitability and drive towards sustainability targets. These solutions enable manufacturers to optimise coal, raw material, and finished cement grinding by increasing throughput and securing consistent output quality while lowering energy consumption. Digital advanced data analytics offer tremendous opportunities to increase efficiency and further optimise production processes. Now with the emergence of new digital technologies, machine learning models can provide productivity improvements in addition to APC solutions.
Making way for Artificial Intelligence
When planning and implementing a digital strategy, it is important to take a holistic approach. This means moving the process from typically siloed and discrete functions to one in which all processes are connected, via developments in the Internet of Things (IoT) technologies, and then automated. It is then possible to move towards autonomous operations-optimisation and asset management functions happening largely without human intervention, and within a secure environment. The key to successful digitalization is data, collected directly from connected equipment and processes or derived from soft sensor models.
APC and analytics provide the ability to make predictions and estimations about process performance, even in the absence of reliable measurement data, for example, when real-world measurement would be too expensive or to increase the frequency of data input and provide backup for unreliable measurements.
In such cases, analytic models can be deduced from either first principles or process data. Analytic models include graphical (first principles), linear regression, non-linear regression, principal component analysis, artificial neural networks, and support vector machines. Users can test various models and choose the one with either the best fit or performance statistic, thereby leveraging state-of-the-art advanced analytics.

Use of Artificial Intelligence in Blaine prediction
The quality of cement is determined by the Blaine number. The Blaine of cement refers to the measure of the specific surface area or the fineness of the cement. Since process adjustments are made based on this quality measurement, infrequent sampling may result in production loss and inconsistent product quality.
Predictive Quality Analytics makes it possible to accurately forecast cement quality in real-time at any point in the production process, thereby reducing the overspending that is typical in efforts to meet quality targets.
Blaine is measured in a laboratory at a frequency of every one to two hours and is used in the control system to maintain consistent quality and high levels of production. Although the data can be utilised for process control, it does not provide real time insight into the process. This manual approach has limitations and ideally requires a predictive modeling technology that can predict Blaine every few minutes to maintain consistent quality, improve operational stability and reduce variability.

Evolution of the soft sensor
To drive the need to apply predictive quality analytics to Blaine, a soft sensor is developed using data-driven machine learning algorithms to predict Blaine at desired intervals using relevant production parameters such as fresh feed, separator speed, grinding pressure, mill DP, etc.
This can be accomplished by following the steps:
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Collect historical data from the control system for model training (production parameters and lab data)
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Data cleansing (e.g. removal of data during mill stoppages, etc.)
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Create a fullyautomatic regression training model selecting the best fit from the library of models
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Deploy the model and test the model accuracy using the real-time online data
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Automatic data pull and retraining of the model if the accuracy is not met
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Predicted Blaine output is used for further control
As a result, a prediction model transforms cement quality ??Blaine from an output process parameter to an input parameter which helps in sustaining the benefits via adaptive re-modeling and tuning. The efficiency of the process can be improved considerably through this approach since Blaine lacks continuous measurement in real-time and can be prone to infrequent sampling. Hence, operators can make more informed decisions using the information available.
Recent developments in advanced analytics have made machine learning models more easily accessible to users. But the true power of a machine learning algorithm can be harnessed only when domain knowledge is applied along with these algorithms. Data cleansing, anomaly removal, analysing the correlation of parameters, result interpretation can be carried out efficiently with expertise in domain knowledge.
Having served the cement industry for more than a century, building up knowledge and know-how of electrification and process control, ABB specialises in increasing plant performance and improving energy efficiency. Using ABB?? proven analytical and process modelling tools, along with our in-depth industry specific knowledge, we can provide a clear path for plants to achieve operational excellence.
Sandeep Ramprasad is the Global Service Product Manager for Cement at ABB Ltd. Actively involved in the fields of engineering, technology management, strategy and product management, he is responsible for driving product and portfolio management, business development and marketing in cement services.
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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.
Concrete
Redefining Efficiency with Digitalisation
Published
3 weeks agoon
February 20, 2026By
admin
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.
Concrete
Digital Pathways for Sustainable Manufacturing
Published
3 weeks agoon
February 20, 2026By
admin
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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