An unconventional modelling methodology based on artificial neural networks is proposed to rapidly develop a model from data obtained during different batch experiments.

The objective of the global model is to predict time evolution of concentrations of all species present in the reaction medium. For this, different recurrent neural networks are elaborated to estimate a particular species as a function of operating parameters and concentrations of all species and then assembled in a complex global model.

To validate the approach, the esterification reaction of methanol by acetic acid, which presents equilibrium, has been chosen. The kinetic evolution of the chemical species during experiments conducted in batch mode are correctly represented whatever the operating conditions. Finally, the global model based on neural networks is integrated in a hybrid model. This permits to transpose the reaction to a semi-batch chemical reactor which has not been considered during the learning phase.

提出了一种基于人工神经网络的非常规建模方法，可以根据在不同批处理实验中获得的数据快速开发模型。

全局模型的目的是预测反应介质中存在的所有物质的浓度随时间的变化。为此，精心设计了不同的递归神经网络，以根据操作参数和所有物种的浓度估算特定物种，然后将其组装成一个复杂的全局模型。

为了验证该方法，选择了甲醇与乙酸的酯化反应，该反应呈现平衡状态。无论操作条件如何，在批处理模式下进行的实验中，化学物质的动力学演化都可以正确表示。最后，将基于神经网络的全局模型集成到混合模型中。这允许将反应转移到半间歇式化学反应器中，该反应器在学习阶段并未考虑。