When products take years to roll out, the growing demand for what you offer is a mix of good and challenging news. So, when the sales of spirits increased in 2017 by four percent for an eighth consecutive year, suppliers took note.
This demand increase has required alcohol production companies to maximize their throughput, without negatively impacting end-product quality, recipe tradition or production consistency.
We’ve learned how distilleries can maximize throughput without sacrificing quality and that it’s possible to increase optimization without changing the recipe through model predictive control (MPC) technology. Once MPC is adopted, there are a number of adjustments your operators can complete to improve production, achieve quality product and maximum yield based on analytic trends.
Mixing in Analytics
MPC is responsible for optimizing how plant equipment is performing and for helping operators freely adjust what they’re doing to improve production. When adjustments are made to the process, the software also helps you make needed adjustments downstream.
Analytics are helpful when you begin to experience production and quality issues. When machines fail to perform, batch quality and production will suffer. Analytics help by bringing in predictive maintenance and anomaly detection capabilities. Both look for faults in operations by using advanced machine learning and other data processing techniques.
Diving into Predictive Maintenance
Let’s take a deeper dive into predictive maintenance to understand how analytics, partnered with an MPC plan, allow operators to accomplish tasks in a manner that is time and cost effective.
Predictive maintenance applications recognize when equipment isn’t performing normally, based on predetermined models. When equipment deviates from these norms, plant managers and operators are alerted and can catch issues, avoiding problems before they occur. This also helps reduce equipment costs by enabling a fix early on and reducing the wear and tear on equipment.
Consider this predictive maintenance mixer example. During fermentation, if a mixer fails, the yeast will settle and won’t interact properly with sugars, which is detrimental to the end-product. Without thorough mixing, the fermenter also won’t act the way it’s expected to act. Predictive maintenance applications can alert alcohol producers that the mixer is acting differently and identify where the problem is occurring in the process.
Taken a step further, in the MPC application, a soft sensor model predicts quality in real time. Analytics estimate product quality between manual samples. Soft sensor technology can be directed at any measured quality parameter that operators want to observe closely, which saves time that would otherwise be spent waiting for a lab sample.
Anomaly Detection and Process Behaviors
The other analytics application relevant to MPC is anomaly detection. Similar to predictive maintenance, anomaly detection saves time and money by alerting operators when equipment is not performing the way it should and indicating where the problem originated.
For example, in a distillery, operators clean equipment to prevent build up. If the caustic cleaning solution isn’t removed entirely from the equipment, it will harm the yeast and result in poor end product.
Instead of waiting until the end of the production process to realize the batch is ruined, anomaly detection will notify the operator that the batch is spoiled early on. With this information, the operator can halt the spoiled batch, complete the necessary steps to remedy the condition and restart a recovered batch.
Analytics determine which equipment failed and indicate why, based on the models that know how operations should ideally perform. With this information, operators are better informed and can reduce or avoid problems.
Analytics Will Grow
In summary, MPC in conjunction with analytics results in less unplanned downtime, greater productivity, higher yield, better quality and greater consistency of the end product. This means operators and managers can focus on important tasks instead of chasing down issues or worrying about deviating from traditional recipes.
From a more general standpoint, analytics are beneficial because they allow plant operators and managers to be in the know and understand their equipment to the fullest.
While incorporating analytics in the beverage industry is new and still evolving, I predict analytic growth will skyrocket from here, especially as other companies and industries observe success by early adopters.
As demand for spirits fluctuates over time, alcohol producers will begin to rely more heavily on technologies like MPC to help control throughput. If you want to learn more about the benefits of MPC and analytics, read more about it here.
