Modeling of naphtha pyrolysis with using genetic algoritm

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In operation of industrial pyrolysis furnaces, the main task is the selection of the optimal mode of thermal decomposition of the feedstock, depending on the yield of the desired products under conditions of technological limitations on the process. To solve this problem for an operating reactor, this paper considers the SRT-VI Large-Capacity industrial Furnace, the mathematical model of the pyrolysis process was constructed, using a kinetic scheme which consists of primary reaction of decomposition of raw materials and secondary elementary reactions of interaction of the considered mixture components, the heat balance equation and hydrodynamics of flow in the coil. The raw material for the selected installation type is naphtha (straight-run petrol). Output parameters of the model are the molar costs of marketable hydrocarbons. The reactor is described by the equation of ideal displacement in the static mode of operation. It is assumed that all reactions have a temperature dependence that follows the Arrhenius law. The activation energies of chemical processes were estimated using the Polanyi-Semenov equation and identification of pre-exponential factors was carried out using a genetic algorithm (GA). This task requires solving simultaneous system of differential equations describing the pyrolysis process and a search for a large number of unknown parameters, and therefore it is proposed to modify the GA. Optimal scheme includes Gray encoding arithmetic operators, tournament selection, with tournament ranking more than 4, crossover with partial random choice of alleys, mutations with a high probability of occurring and elitism with competitive global competition. Using the proposed approach, the parametric identification of model process is accomplished. The analysis of the simulation results with the data of operating reactor showed its suitability for use in order to control the pyrolysis process.

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Короткий адрес: https://sciup.org/14040493

IDR: 14040493

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