Why cement plants can no longer ignore AI and digital twins
Globally, environmental consciousness has grown tremendously in the new millennium. This has led to an extremely complex environment that puts pressure on cement manufacturers to boost production while curbing costs as well as the ecological impact. While digitalisation can help streamline operations, outdated assets and unconnected data systems hamper efforts to leverage data.
Advantages of digital twins
Digital twins powered by artificial intelligence can address this issue. Since digital twins revolutionise decision-making in factories, forward-looking cement producers are adopting these tools to stay ahead of the disruption curve. Digital twins require making virtual replicas of physical assets and processes that permit cement manufacturers to access massive amounts of operational data from varied sources. By contextualising and connecting segregated data, digital twins can discover real-time insights to diagnose, predict and control the operations and outcomes. The power of digital twins increases manifold when integrated with machine learning algorithms. This allows cement units to simulate several scenarios while learning continuously and prescribing various actions.
Due to the current geopolitical uncertainties, resource and labour constraints triggered by increasing costs and the dearth of requisite talent are hampering cement production schedules. Supply chain disruptions are also compounding these problems. In such scenarios, digital twins have been facilitating speedy scale-ups alongside more durable and efficient operations. This is done by enabling cement producers to make smarter, faster and more cost-efficient decisions. Simplifying the understanding of complex systems and production operations, it helps cement makers in optimising production schedules or simulating potential problem scenarios to decode the impact on new product launches.
In advanced stages, digital twins can be incorporated into real-time decision-making, like production scheduling, either via a manual review and intervention or with complete automation. The use cases may vary as per the operating context of the factory. For instance, while making initial investments and building a greenfield plant, digital twins can validate designs of the layout while optimising footprint and estimating the size of inventory. Based on the degree of detail of the twin, spatial parameters can even be evaluated for assets. This could include clearances, employee movement and ergonomics within a cell.
Leading cement producers in India are capitalising on these advances to enhance operational efficiencies. In established factories, digital twins can predict manufacturing bottlenecks that legacy modelling with spreadsheets fails to do. Using live data, tough-to-predict processes, material movement times, inventory buffers and changeovers can be modelled with high fidelity. Twin insights can be utilised for diverse kinds of decision-making situations. This includes slower decisions, such as prioritising continuous improvement opportunities and line balancing, or for real-time decision-making, like optimising production schedules.
Role in predictive maintenance
The importance of AI-enabled digital twins cannot be overstated in predictive maintenance and allied use cases. Consider a bearing or other piece of key equipment. This could already be in the process of failing. Yet, since microscopic cracks are just developing deep inside its metal casing, these are not discernible to human eyes. Weeks later, the equipment will collapse, stopping production suddenly. The downtime losses can run into lakhs, as production will only restart once this part is replaced. Ideally, the component could have been ordered earlier as part of the predictive maintenance process, immediately after being flagged off by AI-driven maintenance tools.
Typically, AI tools provide four to eight weeks’ lead time in the case of equipment failure. In this way, emergency shutdowns are transformed into repairs and maintenance scheduled in advance at a fraction of the post-incident repair cost. In the highly competitive cement industry, such benefits offer an immense advantage that can make the difference between profit and loss.
Though this statement may seem hyperbolic, it is not. Every bearing or other rotating element is subject to the predictable laws of physics that lead to its inevitable degradation. Yet, human eyes cannot detect the impending wear and tear until it is in an advanced state, but AI-driven tools can schedule predictive maintenance for cement plants weeks before any mishap or downtime occurs.
While predictive maintenance in domestic cement plants is in its early stages, there are common use cases in other geographies. Herein, AI monitoring prioritises assets as per their criticality, the chances of failure and overall business impact. Thereby, the failure that matters most is indicated first. Compared to a reactive approach, the US Department of Energy reveals that predictive maintenance saves up to 40% of costs.
Lowering carbon emissions and more
Therefore, predictive maintenance also has a role in reducing carbon emissions. The World Economic Forum notes that cement manufacturing is responsible for anywhere between 5% and 8% of worldwide CO₂ emissions. Again, the use of AI along with AR (augmented reality) and computer vision is playing a key role in transforming cement manufacturing through greater efficiencies in energy usage that increase workforce productivity while lowering emissions.
Similarly, advanced process control (APC) acts as an extremely powerful AI application in cement making. Kiln operations remain extremely complex, as numerous variables influence energy consumption and clinker quality. But these parameters can be stabilised and measurable gains delivered by using AI-powered APC systems. APC usage delivers up to 5% of energy reduction while leading to alternative fuel substitution of around 50%.
Benefits of computer vision and augmented reality
Coming to computer vision, this is akin to the “eyes” of a system, while AI acts like the brain. Both vision algorithms and cameras ensure continuous monitoring of raw material feeds, emissions, clinker formation and equipment wear. When computer vision is integrated into predictive maintenance, manufacturing units can pinpoint anomalies such as conveyor belt misalignments or micro-cracks in refractory linings before these become expensive downtime.
Notwithstanding AI and computer vision, factories still need skilled operators and technicians. However, augmented reality (AR) fills this gap. By overlaying digital data onto the physical environment, AR provides real-time instructions, equipment insights and safety warnings to workers. An AR platform can even allow engineers in a central control room to watch on-site workers and guide them through mill maintenance or a complex kiln reset without being physically present on the ground.
As the Central Government implements ambitious infrastructure projects across India, the cement sector stands at an inflection point – addressing the challenge of high emissions while ensuring energy efficiency. Here, AI, digital twins, AR and computer vision can offer viable solutions that revolutionise the Indian cement industry via next-gen technologies. Thanks to digital technologies, domestic cement companies could eventually emerge as global leaders in a highly competitive and environmentally responsible realm.
