The ability to perform advanced data analytics and smart optimization powered by AI is the gamechanger. It will allow cement producers to hit key performance indicators around sustainability, process performance, asset performance, connected workers and operational excellence. Here are some examples.
AI can play a vital role in reaching environmental sustainability targets, and not just around reducing emissions, but also energy optimization and management. This creates immediate benefits for operating costs and margins, also enabling new business models for high-tech low-CO2 cements.
For instance, ABB's AI-based system anomaly detection app learns your plant and equipment "normal" states and uses adaptive setpoints to detect unusual patterns, anomalous behaviors. By triggering alerts, it reduces the effort to identify and rectify energy consumption deviations. No more hassle of setting manual setpoints or alarms, no more notification overload. The same way an AI app can learn from your energy usage, production schedules and other factors to deliver accurate forecasts, letting reduce peak demand charges on electricity bills.
A cement plant is constantly worried about deviating from daily SO2 emissions limits and associated hydrate consumption, juggling numerous process constraints. Due to variability in feed and fuel sources, coupled with complex dynamics, manual operators with PID control tend to remain at “safe distances” from process constraints, at the cost of plant profitability.
Today, ABB's advanced process control (APC) solution is successfully used to achieve zero violations of SO2 emissions while reducing hydrate consumption by 11%. Operator can select either normal or aggressive optimization models, the multiple feeder points of lime hydrate get automatically adjusted.
Similar process performance solutions are addressing thermal efficiency, fuel switching, reduction of the clinker-to-cement ratio and letting the plant run more profitably, e.g.
- increasing feed by over 3 tph while reducing specific energy by 20 kcal/kg.
- delivering overall productivity increase of 4% with better and more consistent cement quality at the same time.
Traditionally, cement strength can be measured after 28 days – by then it is obviously too late to make corrections in the process. Therefore, plants usually “overdeliver” on product specs. ABB is leveraging machine learning (ML) with data-driven soft sensors to predict 28-day strength on the day of sampling, allowing for process corrections - setting new daily CaCO3 / blaine targets. More cement will be sold at correct specification, reducing additives (lime).
With analytics, AI, reinforcement learning from neural networks, APC apps can be further automated in a way that the performance and accuracy of the models are continually monitored. Analytics can re-tune the models, simulate and remodel processes, optimize additional variables - moving towards adaptive APC and autonomous operations. This will reduce engineering time and allow the system to stay at peak performance, delivering benefits.
Leveraging AI for asset performance management is a step change in the way maintenance and reliability people can collaborate with other functions, ensuring assets are available at the time and at the performance level required by the operations - depending on changing production goals. It’s harder to predict how assets react and respond to various triggers like age or operating condition, because complex systems interact in unexpected ways and are always evolving. It’s harder to make sense of things, because degree of complexity may be “beyond the human eye”. To provide accurate target parameter predictions in near real-time and prevent failures, AI / ML models need to be continuously trained with relevant dataset – requiring deep understanding of both cement processes and asset behavior. Think of AI-enabled APM as the most cost-effective way to extend the life of the aging (and newer) assets, to decide on the optimal timing for scheduled maintenance turnarounds (one of the biggest costs in a plant) and plan better.
Without question, AI detection systems and video analytics can be used to create a more connected workforce and improve safety. e.g. scanning employees and equipment, identifying potential risks, such as a worker who has forgotten to wear the appropriate safety gear.
More and more cement producers on the digitalization path would like to take a more proactive approach to cyber security. ABB's analytics solution and services let continuously monitor, diagnose and resolve security issues, helping safeguard people, assets and reputation. And because technology and cyber threats can both change unpredictably, the strategy needs to be reviewed periodically, including performing simulations under different circumstances, like a major ransomware incident. Data analytics can be used to test various what-if scenarios.
Enterprise grade AI-based solutions also have enormous potential for operational excellence, helping understand the reasons behind the differences in cement plant performance levels. Transferring knowledge or process methodology from the higher-performing to the lower-performing plants will optimize production and uncover best operating points to meet and even exceed KPIs. With less cement required in the future for modular pre-fabricated buildings, AI will play an important role in restructuring operations to retain profitability at reduced cement demand. By analyzing how procurement was done in the past, it can also assist with better planning for supply chain management.