document: Robust model-order reduction of complex biological processes This paper addresses robust model-order reduction of a high dimensional nonlinear partial differential equation -LRB- PDE -RRB- model of a complex biological process . Based on a nonlinear , distributed parameter model of the same process which was validated against experimental data of an existing , pilot-scale biological nutrient removal -LRB- BNR -RRB- activated sludge plant , we developed a state-space model with 154 state variables . A general algorithm for robustly reducing the nonlinear PDE model is presented and , based on an investigation of five state-of-the-art model-order reduction techniques , we are able to reduce the original model to a model with only 30 states without incurring pronounced modelling errors . The singular perturbation approximation balanced truncating technique is found to give the lowest modelling errors in low frequency ranges and hence is deemed most suitable for controller design and other real-time applications
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extractive_keyphrases: complex biological processes singular perturbation approximation balanced truncating technique modelling errors controller design
abstractive_keyphrases: robust model-order reduction high dimensional nonlinear partial differential equation model nonlinear distributed parameter model pilot-scale bnr activated sludge plant state-space model hankel singular values biological nutrient removal activated sludge processes