Technology

Importance of lubrication

Published

4 years ago

October 19, 2021

admin

Despite the fact that correct lubrication is one of the most crucial aspects of a reliability program for rotating equipment, lubrication is often perceived as a low level job that doesn?? require much experience or skill. Lubrication-related failures are probably the most preventable type of all failures of rotating machinery, yet it?? an area of industry that isn?? always allocated the appropriate level of attention. Lubrication helps in:

Reducing wear of moving parts
Reducing friction between rotating parts and stationery ones
Absorbing shock
Reducing operating temperatures
Minimising corrosion of metal surfaces
Keeping contaminants out of the system
Sealing and protecting components

The incorrect choice and application of lubricants is said to account for around 40 per cent of all machine failures, and so lubrication procedures are a critical factor in maximising your equipment?? reliability. Once lubrication has been applied, the equipment and the lubricant should be tested to see if:

The correct formulation of lubricant was used for the application
Whether the lubricant solved ??or merely masked ??the problem
Whether the amount of lubrication applied was correct

The need for frequent lubrication may well be a symptom of underlying machinery damage (such as wear or damage to bearings, shafts or seals) so the solution isn?? simply to lubricate to stop vibration or excessive noise. In fact, too much lubrication can be just as detrimental as too little lubrication. Under lubrication can cause bearings to wear out before their time, whereas over-lubrication can lead to catastrophic results to the bearings or long-term damage to motor coils and windings.

It is critical to follow the manufacturers??recommendations and use the right type and quantities of lubricant with the appropriate frequency of application that is best suited to the machinery?? optimal functioning. An optimal lubrication program requires vigilance, skill and experience from the operator and should include thorough checking and testing procedures using quality equipment. Ultrasound technology will not only improve machine reliability and help a production line run more smoothly, the big picture is that it can help to decrease the cost of production and the cost of maintenance, enhance safety and improve quality control. The key focus should be on finding the best technology that meets organisational needs and making sure that it delivers both financial and operational benefits.

Best practice lubrication regimes will ensure world-class machinery reliability, so it?? worth talking to an experienced supplier of technical equipment to ensure your testing and inspection procedures are up to the task.

OIL ANALYSIS ??THE BASICS

Much like doctors assess our health though blood analysis, critical plant equipment must be monitored in much the same manner. Chronic lubricant or equipment symptoms show up as indicators in oil analysis samples and, if left uncorrected, can lead to equipment degradation and lost productivity. Therefore, the goal of a proactive oil analysis program is to trend gradual changes in fluid properties, contaminants and wear debris so that corrective action can be initiated in a controlled, planned manner.

Oil analysis benefits include:

Optimum equipment life
Extended oil life
Reduced downtime
Improved safety
Environmental awareness

Oil analysis assists maintenance personnel in two primary ways:

First ??Determining the physical condition and contamination of the oil. Lubricant serviceability can be impacted by either reaching the lubricant?? life span OR contamination levels have reached a point requiring a drain and refill, unless purification is an option. When talking about contaminants, the objective is to detect the presence of foreign components and to ask ??hat are they? Where did they come from (built-in, generated, ingressed, introduced)? How can I prevent further entry or generation???Contaminants act as a catalyst for wear.

This generated wear debris further acts as a catalyst for additional component wear. If the cycle is not broken, wear accelerates and downgraded serviceability results.

Second ??Monitoring wear metals for abnormal machinery distress conditions. Wear debris analysis relates specifically to the health of your equipment. As you know, the main function of a lubricant is to separate two surfaces, in relative motion to each other, from making contact. However, its is generally impractical to maintain a lubricant film that will keep those same surfaces totally separated from each other. Thus, metal-to-metal contact can occur, even in today?? high-tech equipment. In addition, keep in mind that boundary lubrication will always be present during start-up. At that critical point in the machine?? operation, some normal and/or abnormal wear metals will be generated, with the amount depending on equipment design and whether or not it has proper lubrication.

TESTING

The following tests are used by ExxonMobil (depending on application) to determine changes in physical properties of the oil, oil contamination and equipment wear debris:

Viscosity by ASTM D 445: indicates changes in fluid?? resistance to flow. Viscosity results can indicate either physical changes or contamination by other fluids.
Oxidation by FTIR (Fourier Transform Infrared Spectrometer): identifies harmful by-products of thermal degradation. Lubrication oxidation represents a physical change.
Nitration by FTIR: identifies harmful by-products of fuel combustion. Nitration is a physical lubricant change, much like oxidation.
Glycol by FTIR/ASTM D 2982: identifies the presence of engine coolants.
Soot by FTIR: identifies the by-products of unburned fuel.
This is also contamination.
Water by FTIR/hot plate/Karl Fisher ASTM D1744: identifies the presence of water, a common and potentially harmful fluid contaminant that can accelerate physical lubricant change and rapidly degrade metal surfaces.
Total acid number (TAN) by ASTM D 664: measures/identifies acid by-products of oxidation and contamination. TAN is a physical change.
Fuel dilution by gas chromatography: identifies the presence of fuel, another contaminant.
Elemental Analysis by ICP (Inductively Coupled Plasma Spectroscopy): identifies both additive and wear debris metals.
Total base number (TBN) by ASTM 4739: identifies acid neutralising capacity. This is a physical lubricant change.
ISO particle count: identifies the size and amount of solid contaminants.

Where to start

It?? easy to see the importance of a proactive oil analysis program, but knowing where to start can often be overwhelming. Don?? let the vast array of analysis hardware and test results keep you from getting started. Here?? a simple seven-step process to get you off and running:

Step 1: Identify ??ission critical??equipment: It?? not necessary to perform oil analysis on every single lubricated system in your plant. Identify critical applications that would seriously jeopardise production if they were to shut down unexpectedly.

Step 2: Register your equipment: It is important to have your equipment properly registered with the lab. This supports routine trending and plays a key role in early detection of lubricant or equipment problems. There?? no need for you to decide which tests are appropriate for a particular application because the lab has already established test slates for specific applications.

Step 3: Establish Best practice: Establish a consistent ??ow-to??practice for taking oil samples from your equipment and train your maintenance personnel to use this practice. Correct sampling practices are critical to the value received from the analysis data. This extremely important step rarely gets the attention it needs.

Step 4: Sample: Retrieve samples in accordance with your best practice and send them to the lab as soon as possible. Samples that are set aside may deteriorate and give non-representative results. For further guidance on Steps 3 and 4, please see Signum Oil Analysis technical information guide titled ??ondition-Monitoring Fundamentals.??This guide can be found on www.signumoilanalysis.com.

Step 5: Analyse: A thorough analysis, keyed on trends, helps determine your systems??conditions. General laboratories may have a pretty good idea of what they are measuring, but often know little about specific formulations and don?? always understand whether or not the differences they see are significant.

Step 6: Interpret: Reviewing the results and determining what, if any, action is required can make or break a successful program. ExxonMobil has an extensive database of test results and has developed recommended control limits based on years of testing. In any case, it?? important to remember that an alert sample does not necessarily mean imminent failure. Seek consultation on alert samples and re-sample to confirm present data before taking massive corrective action.

Step 7: Take corrective action and document!: As always, documentation is the key to knowing where you??e been and where you??e going. Document corrective actions resulting from oil analysis. Historical oil analysis and corrective action documentation is difficult for an OEM to dispute when you call for warranty work.

why sampling programmes fail

When sampling programmes fail, the reason is usually one of five major factors:

Lack of understanding: Knowing what to expect from your analysis program helps determine the right units to sample and the best sampling frequencies. Before starting a sampling program, you??l find it useful to define why you need a sample program and how the test data relates to the productivity of your equipment.
Lack of interpretation knowledge: It is important to know the equipment well enough to quickly relate the analysis results to the noted performance of the equipment being tested.
Lack of commitment: There must be a mutual commitment from all involved, from the highest levels of management down to the person taking the sample. For the program to succeed all parties must be willing to devote time and training to the program.
Poor sampling procedures and practices: Improper sampling procedures cause erroneous results.
Irregular sampling frequencies: Monitoring fluid condition and system performance by trending is far superior to hit-or-miss sampling methods. Lubrication scheduling and oil analysis software, such as Mobil Monitor LMS and Mobil Monitor Lubrication Technician can assist in the implementation and tracking of routine sampling. Oil analysis is a useful, predictive and proactive too that can help prevent equipment breakdowns, determine the root cause of failures and aid in locating operational and contamination problems. If you need help implementing an oil analysis program, our ExxonMobil field team is ready and willing to help. We can help develop your program and provide on-site technical support and training to properly administer this seven step process.

References

??rotecting Your Assets With Oil Analysis??By Jon Sewell, Mobil Periodical ????he Engineered Difference??/p>

– VIKAS DAMLE

Concrete

Redefining Efficiency with Digitalisation

Published

6 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

6 days ago

February 20, 2026

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.

Concrete

Turning Downtime into Actionable Intelligence

Published

1 week 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.