Our works frequently examine the emission of pollutants and the prediction of the thermal efficiency of boilers from power plants. Power plant systems are strongly coupled. Thus, multi-objective modelling and prediction is always a difficult problem. Artificial neural network (ANN) modelling is one of the methods used to meet this challenge. With the increasing requirements of environmental protection, the classical shallow neural network can no longer meet the needs of high precision. In recent years, deep neural networks have gradually demonstrated their powerful capabilities. However, can deep neural networks be used to improve model prediction performance? After many experiments, we successfully construct a sophisticated and stable deep hybrid neural network model to achieve this requirement. The experimental results show that the performance of the hybrid model is superior to that of the classical model; we diagram the detailed structure of the model and provide the corresponding parameter settings in this paper.

我们的工作经常检查污染物的排放和发电厂锅炉热效率的预测。发电厂系统紧密耦合。因此，多目标建模和预测始终是一个难题。人工神经网络（ANN）建模是用来解决这一挑战的方法之一。随着环保要求的提高，传统的浅层神经网络已不能满足高精度的需求。近年来，深度神经网络已逐渐证明其强大的功能。但是，可以使用深度神经网络来提高模型预测性能吗？经过多次实验，我们成功构建了一个复杂且稳定的深度混合神经网络模型来满足这一要求。实验结果表明，混合模型的性能优于经典模型。我们绘制了模型的详细结构，并在本文中提供了相应的参数设置。