The problem of prediction in chaotic environments based on identifying analog situations in arrays of retrospective data are considered. Traditional recognition schemes are ineffective and form weak classifiers in cases where the system component of the observed process is represented by a non-periodic oscillatory time series (realization of chaotic dynamics). The objective is to develop a system of such classifiers, which allows for improvements in the quality of forecasts for non-stationary dynamics in flow processes. The introduced technique can be applied for the prediction of oscillatory non-periodic processes with non-stationary noise, i.e., dependence of different relay frequencies, external electric potential and microchannel width in an electrokinetic micromixer.

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Microreactors intensify chemical processes due to improved flow regimes, mass and heat transfer. In the present study, the effect of the volume flow rate on reactor performance in different reactors (the T-shaped reactor, the interdigital microreactor and the chicane microreactor) was investigated. For this purpose, the saponification reaction in these reactor systems was considered. Experimental results were verified using the obtained kinetic model. The reactor system with a T-shaped reactor shows good performance only at high flow rates, while the experimental setups with the interdigital and the chicane microreactors yield good performance throughout the whole range of volume flow rates. However, microreactors exhibit a higher pressure drop, indicating higher mechanical flow energy consumption than seen using a T-shaped reactor.

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