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The influences of parameters on the low-temperature combustion synthesis of alumina particles from reactant mixture of aluminum nitrate and combustion fuel were studied using the particle size of as-synthesized alumina particles as a performance index. First, when urea was used as combustion fuel, it produced a higher combustion temperature and a larger particle size than the case when carbohydrazide was the fuel. Next, the combustion in air yielded a flame propagating through the reactant mixture, in contrast to a flame simultaneously ruptured from the entire reactant when the combustion was conducted in nitrogen. The particle size of the product obtained in nitrogen was 40% smaller than that obtained in air. Increasing the heating temperature could increase the alumina particle size due to the sintering effect, while combustion failed if the heating temperature was too small. The addition of diluent, excess fuel, and gas-releasing agents reduced the particle size. The increase of stirring speed also reduced the particle size. Next, if the reactant density (the amount of reactant mixture in the reacting container) was below a certain threshold value, the combustion failed to ignite. Increasing the reactant density was found to reduce the particle size due to the simultaneous reduction of combustion time and temperature. Finally, a liquid-gas reaction model was proposed and solved to study the threshold of combustion parameters.

Unsteady‐state periodic operations can improve the optimal steady‐state performance of nonlinear chemical processes. To examine if the optimal periodic operation is proper and to obtain the optimal forcing functions subject to various control and state constraints it is suggested in this paper to convert the problems into a form which is suitable for constrained nonlinear programming. The adopted numerical optimization method is based on employing the control parametrization technique and is thus capable of dealing with the problem of multiple input forcings and obtaining optimal forcing functions and/or parameters while subject to general constraints. Besides, it provides information about to what extent the process performance can be improved by adopting the optimal periodic control.