What is Model Predictive Control (MPC)?

MPC is an advanced control strategy that uses a mathematical model of your process to predict future behavior and optimize control actions. It excels at handling multi-variable systems with constraints, delivering better performance than traditional PID control.

How long does an APC implementation take?

Timelines vary by scope. A diagnostic phase typically takes 2-4 weeks. A full proof-of-concept can be completed in 2-3 months. We work in agile 2-week sprints with deliverables at each stage.

What ROI can you expect from APC?

Typical results include 5-15% energy savings, 10-30% throughput increase, and significant waste reduction. ROI is often achieved within 6-12 months depending on the application.

Do you work remotely or on-site?

Both. Many phases can be done remotely, including data analysis, model development, and simulation. On-site presence is valuable for commissioning, operator training, and initial diagnosis.

What industries do you work with?

Process industry (chemicals, pharma, food & beverage), manufacturing, machine builders, and energy generation. Any industry with complex, multi-variable processes can benefit.

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MPC in 5 Minutes

Posted on January 15, 2024 by Dr. Rafał Noga

MPCAPCProcess Control

What is Model Predictive Control?

Model Predictive Control (MPC) is an advanced control strategy that uses a mathematical model of the process to predict future behavior and optimize control actions in real-time.

Unlike traditional PID controllers that react to errors after they occur, MPC anticipates disturbances and constraints, making proactive adjustments to keep your process running optimally.

Why Does It Matter?

1. Handles Multiple Variables Simultaneously

Real processes have many interacting variables. MPC naturally handles these interactions, optimizing the entire system rather than fighting between competing controllers.

2. Respects Constraints

Every process has limits - equipment capacities, safety boundaries, quality specifications. MPC incorporates these constraints directly into its optimization, ensuring you operate as close to optimal as possible without violations.

3. Predicts and Prevents Problems

By looking ahead, MPC can prevent quality issues before they occur. This is especially valuable when dealing with:

Changing raw material quality
Varying demand requirements
Equipment aging and degradation

Real-World Results

In typical implementations, companies see:

2-5% improvement in throughput
5-15% reduction in energy costs
Significantly fewer quality deviations
Faster grade transitions

Getting Started

The journey to MPC doesn’t have to be overwhelming. A typical path includes:

Process assessment - Understanding your current operations and identifying opportunities
Model development - Building mathematical models from historical data or first principles
Simulation studies - Validating the approach before touching the real process
Pilot implementation - Starting with one unit or section
Rollout and optimization - Expanding and fine-tuning based on results

Conclusion

MPC represents a significant step forward from traditional control strategies. While it requires investment in modeling and implementation, the returns in quality, efficiency, and reduced variability make it worthwhile for most continuous processes.

Ready to explore MPC for your process? Contact me for a free initial consultation.