The world of industrial manufacturing is constantly evolving, and companies are always looking for ways to improve their operations and stay ahead of the competition. One of the key areas of focus is the use of advanced technology, such as Artificial Intelligence (AI), to drive efficiency and optimize performance. In this post, we will explore how combining Model Predictive Control (MPC) with forecast models from machine learning can help manufacturers take their operations to the next level.
What is Model Predictive Control (MPC)?
Model Predictive Control (MPC) is a type of control system that uses mathematical models to predict future system behavior and make control decisions based on those predictions. MPC systems are commonly used in industrial manufacturing to control complex processes, such as production and maintenance schedules, in real-time.
What are forecast models from machine learning?
Forecast models from machine learning use historical data and advanced algorithms to predict future system behavior, such as quality, demand or production levels. These models are built using machine learning techniques, such as regression analysis and time-series forecasting, and are considered to be highly accurate and scalable. With Intelecy, creating an accurate high-performance forecast model is done by the customers through our no-code interface. This means going from a hypothesis, to having a model in production is a matter of minutes, not months. Connecting the forecast model with a local MPC is also made simple, as the model can stream the predictions as OPC-UA tags.
Combining MPC and forecast models from machine learning
By combining MPC with forecast models from machine learning, manufacturers can leverage the accuracy and insights provided by the machine learning models to make better control decisions. The forecast model generates predictions of future system behavior, which are then used by the MPC system to determine the optimal control actions to take in real-time.
For example, if the forecast model predicts an increase in temperature, the MPC system can adjust steam accordingly to meet future demand. This results in improved efficiency, lower costs, and reduced waste.
The benefits of combining MPC and forecast models from machine learning
MPC is a control technique used in industrial manufacturing processes to optimize production outcomes such as efficiency, quality, and cost. Machine learning, on the other hand, is a rapidly growing field of AI that has shown remarkable success in forecast modeling. By combining these two technologies, manufacturers can create more accurate and sophisticated forecast models, enabling them to make better decisions about production processes and ultimately achieve sustainable production outcomes.
Intelecy provides a no-code AI platform that enables manufacturers to build, deploy, and optimize machine learning algorithms, and to easily integrate this into industrial production. With Intelecy, manufacturers can create accurate forecast models in a matter of minutes, without the need for specialized coding skills. This makes it easier and faster for manufacturers to implement MPC in their production processes and optimize outcomes in real-time.
In summary, the combination of MPC and machine learning provides manufacturers with more accurate and sophisticated forecast models, which can lead to better decisions about production processes and ultimately result in sustainable production outcomes. Companies like Intelecy is at the forefront of this technology and offer a no-code AI platform that enables manufacturers to easily build, deploy, and optimize machine learning algorithms. By leveraging these tools, manufacturers can implement MPC in their production processes and optimize outcomes in real-time, giving them a competitive edge in the constantly evolving world of industrial manufacturing.
Get in touch if you want to learn more about how Model Predictive Control and Machine learning can optimize your production.