Comparison of Unscented Kalman Filter Design for Agricultural Anaerobic Digestion Model

TL;DR

This paper compares various Unscented Kalman Filter variants for nonlinear state estimation in anaerobic digestion models, highlighting trade-offs between accuracy and computational efficiency for practical process monitoring.

Contribution

It provides a comprehensive comparison of unconstrained and constrained UKF variants, emphasizing implementation details and proposing methods to reduce computational costs.

Findings

Constrained UKF with additive noise yields most accurate estimates.

Pre-calculated gradients and reformulated cost functions significantly reduce run time.

Unconstrained UKF variants offer faster computation with competitive accuracy.

Abstract

Dynamic operation of biological processes, such as anaerobic digestion (AD), requires reliable process monitoring to guarantee stable operating conditions at all times. Unscented Kalman filters (UKF) are an established tool for nonlinear state estimation, and there exist numerous variants of UKF implementations, treating state constraints, improvements of numerical performance and different noise cases. So far, however, a unified comparison of proposed methods emphasizing the algorithmic details is lacking. The present study thus examines multiple unconstrained and constrained UKF variants, addresses aspects crucial for direct implementation and applies them to a simplified AD model. The constrained UKF considering additive noise delivered the most accurate state estimations. The long run time of the underlying optimization could be vastly reduced through pre-calculated gradients and Hessian of the associated cost function, as well as by reformulation of the cost function as a quadratic program. However, unconstrained UKF variants showed lower run times at competitive estimation accuracy. This study provides useful advice to practitioners working with nonlinear Kalman filters by paying close attention to algorithmic details and modifications crucial for successful implementation.