Technology

High-efficiency SNCR Injection Systems

Published

11 years ago

February 28, 2015

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Dr Ullrich Speer elaborates on the Selective Non-Catalytic Reduction (SNCR) technology as a fundamental technique to reduce NOx emissions.
To comply with current and future emissions requirements, it is important that equipment suppliers remain ?in-the-know,? so that they can offer the most appropriate solutions. The new emission norms are already announced with due date as June 1, 2015 for new plants and January 1, 2016 for the plants currently in operation to comply with NO2 emissions in India.

NOx reduction and ammonia slip
When fuel is burned pollutants are emitted in the flue gas. One of the main pollutants is NOx. Once emitted, NOx reacts with other atmospheric components to produce ozone (O3). Other products generated during combustion, such as nitric acid (HNO3), react in the atmosphere and fall as acid rain, which negatively affects people, plants and animals.

SNCR technology currently involves the injection of ammonia (NH3) or urea (CH4N2O) solutions. The reaction of ammonia or urea with gaseous nitrous oxide (NOx) is transformed by thermal decomposition into steam (H2O) and nitrogen (N2).

When ammonia is used, a solution is injected directly into the duct in several positions/levels, at approximately 900-1,000?C. The ammonia reacts with nitrogen monoxide (NO) to produce nitrogen (N2) and steam:
4NO + 4NH3 + O2 ? 4N2 + 6H2O
Adding urea solution is simpler and safer and you don?t need explosion protection. In the SNCR application, urea reacts like ammonia but with carbon dioxide (CO2) as a byproduct:

NH2CONH2 + H2O ? 2NH3 + CO2
During the injection of ammonia or urea, ammonia slip will appear in the exhaust gas. The amount can be reduced via process adjustments, but it cannot be eliminated. At high temperatures, ammonia creates NH2 radicals. These are a result of the reaction between ammonia with hydroxyl radicals and oxygen radicals, which are usually created in hot gas streams by other reactions. The ?NH2 radicals reduce nitrogen monoxide to dinitrogen:

NH2 + NO ? N2 + H2O
In the overall reaction, the radical formation reactions appear twice and the reduction reaction four times. This results in the following overall equation:

4NO + 4NH3 + O2 ? 4N2 + 6H2O
When urea is used, it forms ?NH2 and the resulting carbon monoxide (CO) is oxidised by oxygen.
The reduction of NOx by ammonia or urea is based on many partial reactions, the balance of which is determined by the temperature and concentration of the reagents. Therefore, with a theoretical over-stoichiometric injection relationship between ammonia and NOx, the NO cannot be completely removed. Additionally, some of the reducing agent is regenerated as NH3 from the reaction.

For a maximum reduction rate of NO leading to low ammonia and NOx emissions, a temperature window must be complied with (Figure 1). In addition, nozzles are often installed at several levels throughout the whole duct. Based on temperature measurements and calculations, those nozzles closest to the injection point with the optimum reaction temperature will be activated.

Sophisticated single-nozzle control systems that offer independent injection-level sprays already exist. If they could be combined with a local and timely highly-resolving temperature calculation (like an online computational fluid dynamics – CFD), the best NOx removal results could be achieved. Furthermore, minimum ammonia slip will be achieved.

Challenges to be managed by the plant
There must be a clear strategy to meet changing NOx and ammonia emission limits, with different requirements for different plants. Some plants can proceed step-by-step with multiple small investments, but others find it better to invest in a full package.

How are such decisions made?
a)Emission limits are different in different jurisdictions. Could production costs be optimised even with tighter values?
b)Plants must determine (and understand) the complexity of the influencing variables of NO production and NOx reduction such as: flue gas temperature in the injection area; flue gas speed in the injection area; flue gas speed in other parts of the system; fuel properties; raw NOx load from the sintering zone and possibly from the calciner.
c)The process choice will influence current and future investments. There are many options at different prices with varying future adaptability. Some of Lechler?s solutions will be discussed later.
Unfortunately, the parameters listed under Point b must be controlled as well as known. Each of the influencing factors must be controlled separately and considered in the final calculation that will decide on the type of control system. Within the plant there are three different influencing groups:

Unknown variables: These include the raw NOx value, temperature, gas speed and gas composition in the injection area and must be measured to be known.

Difficulty factors: These include temperature fluctuations in the injection area, high dust loads within the system, up to five minute delays between the measurement point(s) and stack, the riser duct refractory, the gas flow and speed, fouling at the tip of the nozzles and the residence time of the gas.

Permanent process changes: Most European cement plants (many elsewhere) use alternative fuels and each of these changes the gas composition. Ongoing modifications to the kiln line, or even existing changes within the process while the kiln is running, will also permanently affect the process.

One factor that will affect NOx production is build-up in the calciner. This is because the whole process of the production line is based on theoretical calculations of an optimized new plant. With increasing build-ups in the tower, the internal diameter of the tower reduces. Assuming the same volume of gas, but travelling through a smaller diameter, we will see a higher gas velocity. A specific residence time at the optimum temperature is required to achieve the best possible NOx reduction. However, increased velocity will shorten the residence time, resulting in an incomplete reaction and higher NOx levels. To prevent this, it is necessary to have online control of the build-ups and to be able to predict the next occurrence ahead of time.

3D-temperature simulation and online CFD
Steag Powitec GmbH (Powitec) from Essen, Germany, has developed a high-efficiency SNCR (heSNCR) software system for NOx reduction in cement plants in cooperation with Lechler GmbH, due to the fact that primary measures like staged combustion will not be able to meet the 200 mg/Nm3 NOx limit. It is available as a stand-alone solution or as an upgrade to an existing SNCR plant.

The heSNCR technology enables low NOx emissions while maintaining tight limits for the ammonia slip and reduced reagent consumption. Upgrading to the heSNCR from standard SNCR is attractive because this approach almost always makes investment in an SCR system obsolete. The total costs of the heSNCR system are also lower than those of SCR technology. The system can also be supported by the advanced sintering process control system to reduce primary NOx. The system software comprises:

Online CFD for continuous generation of a highly-resolved time and spatial model of the flue gas in the rising duct between kiln and pre-heater (or calciner);
Estimation of the build-up thickness in relevant duct walls that dominate airstream issues;
Online calculation of the ideal spray amount (considering current and future levels of NOx, O2, temperature, deposition rate and slip)
Permanent adaptation of control to process changes.

An additional special characteristic of the process is that the NOx reduction efficiency and slip depend strongly on temperature and O2 distribution. To achieve the targets, the temperature window must be determined for spraying the right amount of reducing agent at the right time to the right area. However, this poses another challenge as the optimal temperature window permanently changes, influenced by:

current cement production volumes; Local fuel loads, fuel types and qualities; build-ups; local gas flow and velocity. To meet these challenges, SteagPowitec follows the sense, analyse, predict, control (SAPC) approach:

Sense: Additional temperature sensors are used to gain detailed knowledge of the conditions in the area where reagent is injected. Sensors are installed in the refractory material of influential ducts, in positions where build-ups tend to occur. At each position, two temperature sensors are used to improve the understanding of the current build-up of the deposits at this specific point. Because the sensors are of different lengths, they can measure a specific temperature difference. In the case of build-ups or a reduction of refractory wall thickness due to wear, the changes in temperature difference give information about gas flow velocity.

Analyse: The current build-up deposit situation in the rising duct is estimated using the data from the temperature sensors together with the process control system data.

Data is continuously analysed and noise removed.
Predict: The temperature distribution in the rising duct is calculated by dividing the duct into many small segments. For each segment, the physical parameters of the flue gas (mass, density, velocity and temperature) are modelled. Mutual interactions are described by mathematical equations as used in CFD analysis.

The calculated values are calibrated online with the values from the process control system. The temperature distribution is continuously calculated online with update rates of 10-30s. The permanent online CFD allows the calculation (prediction) of the load and fuel-dependent change of temperature. This enables efficient and intelligent system control.

Control: As clinker production is a non-linear process with significant reaction times and Constantly changing correlations, controlling a heSNCR system is a complex task. Different operating conditions generate different emission loads and different temperatures.

The PiT Navigator SNCR technology, part of the heSNCR system, continuously uses conventional process data, the additional temperature sensor data and the results of the online CFD calculations to find and evolve process models automatically over time. The technique is a system of neural networks, which are used to estimate important process results. Thus, the PiT Navigator automatically evaluates the presently valid model to determine the effect of certain activities. For example, it simulates slight modifications to the amount of reagent injected through the nozzles to determine the effect on NOx reduction and the ammonia slip at the stack. The best result derived from these simulations is used for the control of the lances in the actual plant.

Unlike standard control systems, the PiT Navigator SNCR system is self-calibrating and auto-optimising closed-loop control software. Consequentially, extensive and permanent manual reconfigurations are not necessary. Additionally, statistical models do not rely on subjective expert knowledge; they learn from existing process data automatically and select the best control strategy. The system is also fault-tolerant: If a single measurement fails, it will rely on others.

The heSNCR technology is equipped with a self-learning adaptive process controller that adjusts itself automatically to process changes and thus injects the optimal quantity of reagent, at the right time, in the right area. This has the effect of continuously achieving significantly lower NOx levels with the lowest possible reducing agent consumption at the lowest possible slip. In places where NOx limits are not yet low, the system still offers significantly lower reagent consumption rates and protects against further investment costs when NOx limits are lowered.

SNCR solutions
Lechler GmbH and Powitec provide a variety of NOx reduction systems. The differences between each system and the anticipated NOx and ammonia reagent reductions are outlined.

SmartNOx
?: The Lechler SmartNOx system is a standard valve skid for de-NOx using ammonia. Customisation options are limited and the lances (Figure 2) are not individually controllable. The system was designed for those that want to gain experience with de-NOx and is also useful for meeting more relaxed NOx emission limits.

Basic level SCNR: Basic SNCR is recommended for customers seeking long-term equipment that are willing to upgrade later on. It includes a twin fluid valve skid with a conventional control system and four Laval nozzles and lances on one injection level. It is possible to individually adjust the volume and droplet size delivered by each lance. Typical reductions in NOx emission levels are from 700 mg/Nm? to 500 mg/Nm?.

Efficient SNCR (eSNCR): This includes two additional lances with Laval nozzles, giving six lances on two levels, as well as a second small control rack. Beside the existing control system, the eSNCR system offers a special NOx prediction. The system can reduce NOx emissions from 1,000 mg/Nm? to 500 mg/Nm?, using 15 per cent less ammonia than the basic SNCR.

High-efficiency SNCR (heSNCR): The heSNCR consists of the eSNCR system, the build-up detection and the online CFD. Two additional Lechler twin fluid Laval nozzles are included and the injection takes place on three levels in the calciner. All outstanding and currently available technologies are included, like the NOx prediction, the PiT deposit detectors and the PiT online CFD tool. A reduction from 1,000 mg/Nm? to 200 mg/Nm? NOx is typically achieved, as well as a saving of approximately 30 per cent of ammonia reagent.

The author is Global Division Leader (Environmental Division) at Lechler GmbH.

Concrete

Redefining Efficiency with Digitalisation

Published

2 days ago

February 20, 2026

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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.

Concrete

Digital Pathways for Sustainable Manufacturing

Published

2 days ago

February 20, 2026

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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.

Concrete

Turning Downtime into Actionable Intelligence

Published

2 days ago

February 19, 2026

admin

Stoppage Insights instantly identifies root causes and maps their full operational impact.

In cement, mining and minerals processing operations, every unplanned stoppage equals lost production and reduced profitability. Yet identifying what caused a stoppage remains frustratingly complex. A single motor failure can trigger cascading interlocks and alarm floods, burying the root cause under layers of secondary events. Operators and maintenance teams waste valuable time tracing event chains when they should be solving problems. Until now.
Our latest innovation to our ECS Process Control Solution(1) eliminates this complexity. Stoppage Insights, available with the combined updates to our ECS/ControlCenter™ (ECS) software and ACESYS programming library, transforms stoppage events into clear, actionable intelligence. The system automatically identifies the root cause of every stoppage – whether triggered by alarms, interlocks, or operator actions – and maps all affected equipment. Operators can click any stopped motor’s faceplate to view what caused the shutdown instantly. The Stoppage UI provides a complete record of all stoppages with drill-down capabilities, replacing manual investigation with immediate answers.

Understanding root cause in Stoppage Insights
In Stoppage Insights, ‘root cause’ refers to the first alarm, interlock, or operator action detected by the control system. While this may not reveal the underlying mechanical, electrical or process failure that a maintenance team may later discover, it provides an actionable starting point for rapid troubleshooting and response. And this is where Stoppage Insights steps ahead of traditional first-out alarm systems (ISA 18.2). In this older type of system, the first alarm is identified in a group. This is useful, but limited, as it doesn’t show the complete cascade of events, distinguish between operator-initiated and alarm-triggered stoppages, or map downstream impacts. In contrast, Stoppage Insights provides complete transparency:

Comprehensive capture: Records both regular operator stops and alarm-triggered shutdowns.
Complete impact visibility: Maps all affected equipment automatically.
Contextual clarity: Eliminates manual tracing through alarm floods, saving critical response time.

David Campain, Global Product Manager for Process Control Systems, says, “Stoppage Insights takes fault analysis to the next level. Operators and maintenance engineers no longer need to trace complex event chains. They see the root cause clearly and can respond quickly.”

Driving results
1.Driving results for operations teams
Stoppage Insights maximises clarity to minimise downtime, enabling operators to:
• Rapidly identify root causes to shorten recovery time.
• View initiating events and all affected units in one intuitive interface.
• Access complete records of both planned and unplanned stoppages

Driving results for maintenance and reliability teams
Stoppage Insights helps prioritise work based on evidence, not guesswork:
• Access structured stoppage data for reliability programmes.
• Replace manual logging with automated, exportable records for CMMS, ERP or MES.(2)
• Identify recurring issues and target preventive maintenance effectively.
A future-proof and cybersecure foundation
Our Stoppage Insights feature is built on the latest (version 9) update to our ACESYS advanced programming library. This industry-leading solution lies at the heart of the ECS process control system. Its structured approach enables fast engineering and consistent control logic across hardware platforms from Siemens, Schneider, Rockwell, and others.
In addition to powering Stoppage Insights, ACESYS v9 positions the ECS system for open, interoperable architectures and future-proof automation. The same structured data used by Stoppage Insights supports AI-driven process control, providing the foundation for machine learning models and advanced analytics.
The latest releases also respond to the growing risk of cyberattacks on industrial operational technology (OT) infrastructure, delivering robust cybersecurity. The latest ECS software update (version 9.2) is certified to IEC 62443-4-1 international cybersecurity standards, protecting your process operations and reducing system vulnerability.

What’s available now and what’s coming next?
The ECS/ControlCenter 9.2 and ACESYS 9 updates, featuring Stoppage Insights, are available now for:

Greenfield projects.
ECS system upgrades.
Brownfield replacement of competitor systems.
Stoppage Insights will also soon integrate with our ECS/UptimeGo downtime analysis software. Stoppage records, including root cause identification and affected equipment, will flow seamlessly into UptimeGo for advanced analytics, trending and long-term reliability reporting. This integration creates a complete ecosystem for managing and improving plant uptime.

(1) The ECS Process Control Solution for cement, mining and minerals processing combines proven control strategies with modern automation architecture to optimise plant performance, reduce downtime and support operational excellence.
(2) CMMS refers to computerised maintenance management systems; ERP, to enterprise resource planning; and MES to manufacturing execution systems.