Batch-to-Batch Adaptive Iterative Learning Control-Explicit Model Predictive Control Two-Tier Framework for the...

Area of Science:

• Chemical Engineering
• Process Control
• Renewable Energy

Background:

• Biodiesel, or fatty acid methyl esters, is a key renewable fuel produced via batch transesterification.
• Batch processes for biodiesel face challenges with uncertainty and unstable behavior, requiring robust control.
• Developing effective control strategies is crucial for reliable and efficient biodiesel production.

Purpose of the Study:

• To introduce a novel two-tier control framework for fatty acid methyl ester production.
• To integrate constrained batch-to-batch iterative learning control (ILC) with explicit model predictive control (MPC).
• To achieve precise control over fatty acid methyl ester concentration despite process uncertainties and disturbances.

Main Methods:

• A two-tier control framework combining constrained batch-to-batch ILC and explicit MPC.
• ILC generates optimal reactor temperature set-points.
• Explicit MPC determines optimal coolant flow rate by minimizing a quadratic cost function.

Main Results:

• The proposed framework converges to the desired fatty acid methyl ester concentration trajectory within six batches.
• Effective control is maintained even with uncertainties in activation energy and triglyceride inlet concentration.
• The approach demonstrates superior performance compared to heuristic and constraint ILC methods.

Conclusions:

• The novel two-tier control framework offers robust and efficient control for biodiesel production.
• This method successfully addresses uncertainties inherent in batch transesterification processes.
• The integration of ILC and MPC provides a promising solution for optimizing renewable fuel manufacturing.