Key points
Backed by operational data, APM strategies help engineers in the Food & Beverage industry make data-driven decisions.
In the world of sugar production, the heart of the refinement process beats within the crystallisation vessels. It’s during this phase that succulent sugar crystals first come to light. Refined sugar starts as fresh sugar beets that are harvested from the field.
After cutting the beets and soaking them in hot water inside diffusion towers, they go through a vacuum and heating process that removes most of the water and creates a sticky solution. The solution then goes through a crystalliser where it becomes tiny sugar grains.
To achieve the best quality sugar crystals with the highest yield, the sugary solution has to stay in each of the vessels for the same amount of time. Doing so prevents issues such as sieve blockages or additional consumption of steam.
A way to ensure better and more efficient sugar batches is to use Asset Performance Management (APM) strategies. These include predictive maintenance, real-time asset monitoring, and a number of other data-driven strategies that greatly improve operations in the Food & Beverage industry.
What Is Asset Performance Management?
APM is a structured strategy to enhance performance and extend the life of physical assets. The goals of APM are to improve operational efficiency, reduce costs, and keep production safe. Engineers use industry-leading best practices along with advanced technologies to ensure timely and top operation of production assets.
To achieve APM strategies, engineers make decisions based on insights created from leveraging operational data. The cornerstone techniques of APM include:
- Real-Time Monitoring: Engineers visualise the status of operations and overall asset health. They use real-time monitoring to find anomalies or make forecasts about the assets.
- Predictive Maintenance: Determining when equipment is likely to fail helps engineers schedule maintenance proactively, which avoids unexpected downtime.
- Reliability-Centered Maintenance (RCM): This helps engineers select the most suitable maintenance approach that considers how critical each asset is to production.
- Enterprise Asset Management (EAM): Through EAM strategies, engineers oversee the entire lifecycle of physical assets, from inception and acquisition to retirement.
For example, the application of real-time monitoring helps engineers identify not only irregularities and potential failures but also declining asset conditions. An in-depth analysis of operational data provides the information that engineers need to find the root cause. Once they know what caused the anomaly, they can prevent it from happening in the future. Asset performance also improves as a result.
The same information also could be used for more advanced anomaly detection. By applying Machine Learning techniques to operational data, an anomaly detection model complements real-time monitoring with a comprehensive overview of asset performance.
Refining Sugar Batches
In fact, engineers at one sugar refinery were able to reduce batch cycle time by 12% using APM strategies. When engineers began to notice that batches in the crystallisation phase began to take longer than they should, they used TrendMiner to find out why.
Engineers created a colour-coded chart that easily allowed them to see which of the batches were longer during the crystallisation phase of sugar production. They then created a live dashboard monitor that helped them find out why some of the runs lasted too long.
First, they searched for periods of time when the reactors in the vessels ran for longer than expected, which was 1.5 hours. They used this information to create a Gantt chart view of the agitation anomalies. Then, they colour-coded the longer batches to distinguish them from shorter runs. They quickly saw that most of the 14 quality issues started to happen during the past month.
So, they created a live monitor tile in a dashboard to continuously track the quality of the sugary solution through production and find an ideal batch of refined sugar. They could see when the stirrer speed fell below 60% at the pivotal production point, or 40 liters. This led them to determine that a lack of consistency in the reactor phase made a big difference when it was time for crystallisation.
Engineers adjusted the reactor, which resulted in shorter cycle times and more efficient runs. They then saved this information to use for future batches.
A Sweet Case for Operational Data
The journey to sugar crystallisation begins well before the first sugar beet is cured. Through the strategic use of operational data and Asset Performance Management strategies, engineers keep sugar production going while preventing unexpected stops. Furthermore, their efforts ensure that everyone can enjoy a sprinkle of sugar in their everyday life.
