Abstract: The increase of technogenic and natural accidents,
accompanied by air pollution, for example, by combustion products,
leads to the necessity of respiratory protection. This work is devoted to the development of a calorimetric method
and a device which allows investigating quickly the kinetics of
carbon dioxide sorption by chemisorbents on the base of potassium
superoxide in order to assess the protective properties of respiratory
protective closed circuit apparatus. The features of the traditional approach for determining the
sorption properties in a thin layer of chemisorbent are described, as
well as methods and devices, which can be used for the sorption
kinetics study. The authors developed an approach (as opposed to the traditional
approach) based on the power measurement of internal heat sources
in the chemisorbent layer. The emergence of the heat sources is a
result of exothermic reaction of carbon dioxide sorption. This
approach eliminates the necessity of chemical analysis of samples
and can significantly reduce the time and material expenses during
chemisorbents testing. Error of determining the volume fraction of adsorbed carbon
dioxide by the developed method does not exceed 12%. Taking into
account the efficiency of the method, we consider that it is a good
alternative to traditional methods of chemical analysis under the
assessment of the protection sorbents quality.
Abstract: This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. Such a process usually controlled by conventional cascade control, it provides a robust operation, but often lacks accuracy concerning the required strict temperature tolerances. The predictive control strategy based on the RBF neural model is applied to solve this problem to achieve set-point tracking of the reactor temperature against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the desired reaction temperature.