1.

Multi-criteria analyses of wastewater treatment bio-processes under an uncertainty and a multiplicity of steady states

A multi-criteria evaluation methodology was proposed for determining the operating strategies for bio-chemical, wastewater treatment plants based on a model analysis under an uncertainty that can present multiple steady states. The method is based on Monte Carlo (MC) simulations and the expected utility theory in order to deal with the analysis of choices among risky operating strategies with multi-dimensional outcomes. The motivation is given by a case study using an anaerobic digestion model (ADM) adapted for multiple co-substrates. It is shown how the multi-criteria analyses’ computational complexity can be reduced within an approximation based on Gaussian-process regression and how a reliability map can be built for a bio-process model under uncertainty and multiplicity. In our uncertainty-analyses case study, the reliability map shows the probability of a biogas-production collapse for a given set of substrates mixture input loads.

COBISS.SI-ID: 26152231

2.

Explicit output-feedback nonlinear predictive control based on black-box models

This paper describes an approximate multi-parametric nonlinear programming approach to explicitly solve output-feedback NMPC problems for constrained nonlinear systems described by black-box models like for example artificial neural networks or Gaussian process models. A dual-mode control strategy is employed in order to achieve an offset-free closed-loop response in the presence of bounded disturbances and/or model errors.

COBISS.SI-ID: 24397351

3.

Dynamical systems identification using Gaussian process models with incorporated local models

The paper describes the validation of the method for identification of Gaussian process model with incorporated prior knowledge in the form of local linear dynamic models. These are the first ever published results of Gaussian process model with incorporated local models method used to identify a higher order system and also the first results where the method was used to identify a system using measurement data.

COBISS.SI-ID: 24397095

4.

On-line Gaussian process model for the prediction of the ozone concentration in the air

On-line Gaussian process modelling method has been implemented for the study of the ozone pollution in the air. The selected method is suitable in particular because it adapts model according to streaming measurement data to the complex dynamics and not entirely known mechanism in the background of ozone generation.

COBISS.SI-ID: 24443431

5.

Fixed-structure Gaussian process model

Algorithm for the modelling of discrete dynamic system's model with fixed structure and varying parameters that are modelled with Gaussian process models is proposed and described in the paper. This model structure represents the basis for gain-scheduling control design.

COBISS.SI-ID: 23224615