Kinetic parameters estimation in an anaerobic digestion process using successive quadratic programming

Area of Science:

• Environmental Engineering
• Biochemical Engineering
• Process Optimization

Background:

• Anaerobic digestion is crucial for wastewater treatment and biogas production.
• Accurate kinetic parameter estimation is vital for optimizing anaerobic digestion processes.
• Existing methods for parameter estimation can be time-consuming and less accurate with noisy data.

Purpose of the Study:

• To develop and validate an advanced optimization method for estimating kinetic parameters and yield coefficients in anaerobic digestion models.
• To improve the speed and accuracy of parameter estimation using state estimation and nonlinear programming.
• To compare the proposed method with traditional techniques like the Marquardt method.

Main Methods:

• Implementation of an asymptotic observer for estimating unmeasured variables and initial conditions.
• Application of the successive quadratic programming (SQP) technique for parameter estimation.
• Testing the model with experimental data from a pilot-scale fixed bed reactor treating distillery wastewater.

Main Results:

• The proposed method, combining an asymptotic observer and SQP, achieved fast and accurate kinetic parameter and yield coefficient estimation.
• The method demonstrated robustness against highly noisy experimental data and time-varying input variables.
• Statistical analysis validated the combined estimation method's effectiveness.

Conclusions:

• The developed optimization method offers a significant improvement in speed and accuracy for anaerobic digestion model parameterization.
• The SQP technique combined with an asymptotic observer is a powerful tool for real-time process monitoring and control.
• This approach provides a more efficient alternative to traditional parameter estimation techniques like the Marquardt method.