Aromatic nitrations by mixed acid have been selected as a specific case of a heterogeneous liquid-liquid reaction. An extensive experimental programme has been followed using adiabatic and heat-flow calorimetry and pilot reactor experiments, supported by chemical analysis. A series of nitration experiments has been carried out to study the influences of different initial and operating conditions such as temperature, stirring speed and sulphuric acid concentration. In parallel, a mathematical model to predict the overall conversion rate has been developed. In this paper the mathematical modelling and the implementation and experimental validation for benzene, toluene and chlorobenzene mononitration in the kinetic control regime (slow liquid-liquid reaction) are presented and discussed.

Aromatic nitration by mixed acid was selected as a specific case of heterogeneous liquid-liquid reaction. An extensive experimental programme was followed using adiabatic and heat flow calorimetry and pilot reactor experiments, supported by chemical analysis. A series of nitration experiments was carried out to study the influence of different initial and operating conditions, such as temperature, stirring speed, feed rate and sulphuric acid concentration. In parallel, a mathematical model to predict the overall conversion rate was developed. In this paper, the mathematical modelling, implementation and experimental validation for mononitrations of benzene, toluene and chlorobenzene in the mass transfer controlled regime of fast liquid-liquid reactions are presented and discussed.

Abstract-In this paper the use of neural networks for fitting complex kinetic data is discussed. To assess the validity of the approach two different neural network architectures are compared with the traditional kinetic identification methods for two cases: the homogeneous esterification reaction between propionic anhydride and 2-butanol. catalysed by sulphuric acid. and tbe heterogeneous Iiquid-liquid toluene mononitration by mixed acid. A large set of experimental data obtained by adiabatic and heat flux calorirnetry and by gas chromatography is used for the training of the neural networks. The results indicate tbat tbe neural network approach can be used to deal with tbe fitting of complex kinetic data to obtain an approximate reaction rale function in a Iimited amount of time. which can be used for design improvement or optimisation when. owing to small production levels or time constraints. it is not possible to develop a detailed kinetic anaIysis. Copyight

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