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 on-line dosing of reacting chemicals, which preserve reactor temperature close to the desired set-point 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.
COBISS.SI-ID: 24978727
The use of non-parametric Gaussian process model for fault detection is proposed. Detection relies on statistical tests applied to model prediction error. A novelty is model validity index which assesses whether the model is being used in the region in which it was learned and validated. In case this is not true caution is needed prior to releasing an alarm. Hence false alarm rate is reduced.
COBISS.SI-ID: 25017127
The contribution addresses the latest results in the domain of diagnostics and prognostics with particular emphasis on electrical motors and drives. The presented methodology is appropriate for on-line monitoring of the component quality.
COBISS.SI-ID: 25088039