The article presents a Gaussian process (GP) model for the control of sequencing batchreactor (SBR) for wastewater treatment. The GP model is a probabilistic, nonparametric model with uncertainty predictions. In the case of SBR control, it is used for the online optimisation of the batchphases duration. The control algorithm follows the course of the indirect process variables (pH, redox potential and dissolved oxygen concentration) and recognises the characteristic patterns in their time profile. The control algorithm uses GP-based regression to smooth the signals and GP-based classification for the pattern recognition. When tested on the signals from an SBR laboratory pilot plant, the control algorithm provided a satisfactory agreement between the proposed completion times and the actual termination times of the biodegradation processes.
COBISS.SI-ID: 26698535
The article, published in one of the most distinguished journals in the field of energy, presents a simulation study on the extended model of a fuel cell power unit. It consists of fuel cell stack, power converter and battery models and predicts unit’s key values. The possibility of using the battery as intermediate energy storage is discussed, and a control strategy for improving the operation parameters is proposed. The main contributions are a) extension of the fuel cell stack model with models of power converter and battery, b) a control strategy based on supervisory control automaton, which monitors load consumption and battery state of charge, and calculates optimal operating point in terms of the stack efficiency, the impact of working conditions on stack degradation and the battery state of charge, c) calculation of the cumulative effect of working conditions to stack degradation according to the specifications of the real system, and d) evaluation of the control system according to the above parameters in comparison with conventional control approaches.
COBISS.SI-ID: 24858151
A new methodology for determining the operating strategies for biochemical, wastewater treatment plants was proposed. 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 multidimensional outcomes. The motivation is given by a case study using an anaerobic digestion model (ADM) adapted for multiple cosubstrates. It is shown how the multicriteria analyses' computational complexity can be reduced within an approximation based on Gaussian process regression and how a reliability map can be built for a bioprocess 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. Results of this research were published in the most important journal (it has the highest impact factor) in the field of Water Resources.
COBISS.SI-ID: 26152231
A mathematical model was designed for an industrial, semi-batch polymerization reactor, which describes the chemical reactions and heat balances in polymerization process. The model predicts the course of temperature in the reactor as a function of adding reagents, and key output parameters of the final product, such as conversion, solids content and viscosity. The main contributions are the integration of the two models the chemical reaction and the energy balance model, the validation of the model on real plant data from industrial operation, and the analysis and design of control algorithms for online dosing of reacting chemicals, which preserve reactor temperature close to the desired setpoint and so contribute to uniform product quality. The designed reactants dosing control represents an original solution for polymerization reactors, where reactor cooling is performed only through evaporative cooling. The desired control performance was proved by simulation based on real plant data and also experimentally on an industrial polymerization reactor. The paper received distinction from Computers and Chemical Engineering journal as one of the most downloaded articles in 2012.
COBISS.SI-ID: 24978727
The proposed condition indicator for PEM fuel cells is constructed from a set of fuel cell impedance. The impedance values are treated as dependent complex random variables and the copula function are employed for aggregation of these values into the condition indicator. The approach provides an online condition indicator, which in an universal and unambiguous manner describes the diagnostics information of the PEM fuel cell.
COBISS.SI-ID: 27816231