Let's talk about Model Predictive Control (MPC)
Welcome to post 25! While AI dominates today's tech conversations, MPC optimizes manufacturing processes with predictive capabilities, meriting a closer look :)
In today's tech talks, it seems like Artificial Intelligence (AI) steals the show. We're talking about machine learning, big language models, and the like. And we're all for it! We see the magic in math and the power of algorithms that turn the data in your data platform into actionable insights, making businesses smarter and more efficient.
Indeed, AI ranks high on many executives' investment lists, including in the manufacturing sector. But as we've explored before, diving into data science isn't without its challenges - data quality headaches, scaling issues, and ensuring your solutions work as intended are just the start.
If you've followed our discussions on navigating the world of data-driven projects in manufacturing, you're familiar with the hurdles and the strategies to overcome them. If not, it's worth catching up on our previous insights for a solid foundation:
Now, let's shift the spotlight to something a bit under the radar but equally important: Model Predictive Control (MPC), also known as Advanced Process Control (APC). While MPC might not be a household name in the industry, its impact in both continuous and batch processes is significant.
What is MPC?
Imagine you're on a road trip, and you have the technology to predict traffic, weather, and road conditions ahead of time: no traffic jams for you today. MPC works similarly for manufacturing processes. It's a smart algorithm that looks into the future of a production line or system, adjusting its course based on predictions to optimize performance. Unlike basic control methods, MPC can think ahead, making it ideal for complex, interconnected systems. Take for example a hot summer day: the MPC algorithm sees the rising temperatures and can adjust the cooling capacity at the right time (not too early, not too late) to make sure the plant can keep producing at maximum efficiency.
Originally developed for petrochemical and power plants, MPC's foresight and adaptability made it a standout choice. Now, it's found across various industries, from food production to assembly lines. Thanks to advancements in technology, MPC has become faster and more versatile, capable of making split-second adjustments.
How MPC Works
In simple terms, MPC takes into account the current state of a process, predicts future outcomes, and adjusts inputs accordingly to meet desired results. It's all about balance: managing what you control (controlled variables or CVs), what you adjust (manipulated variables or MVs), and unforeseen factors (disturbance variables or DVs), like sudden temperature changes or unmodeled dynamics.
The beauty of MPC lies in its forward-thinking approach. It doesn't just react; it plans, optimizing the entire process over time and anticipating future changes. This capability allows for tighter control, reduced variability, and the ability to push systems to their limits safely.
The existence of these DVs is the reason why control systems with feedback should be used. The algorithm will calculate a trajectory of MVs and associated CVs over a horizon of several hours (for slow systems). Only the first set of MVs will be constructed to the system, after which another full trajectory will be calculated.
Conclusion
MPC's advantages are clear:
It can handle complex goals and constraints, making optimal decisions over extended periods.
It easily accounts for delays and dead times in the system.
It preempts future shifts in production conditions, prices, etc.
It allows for more precise operation, closer to operational limits.
It can incorporate both strict and flexible constraints.
It models interactions with other processes for a holistic approach.
However, MPC isn't without its drawbacks:
Developing and updating the necessary models can be time-consuming (and thus costly). It also requires in-depth process know-how (chemical/physical).
Operators need to adapt to and trust the algorithm managing their processes.
So, don’t forget APC when you face an optimization problem ;)
Have you seen these previous posts on data in manufacturing?
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