This paper presents the design of a hybrid model of a wastewater treatment plant (WWTP), which is meant to improve the quality of effluent prediction. By combining mechanistic, i.e. activated sludge model, and data-driven model it is expected to retain physical transparency and achieve good prediction accuracy. For the data-driven model, a state-of-the-art machine learning approach based on Gaussian process (GP) model was applied. GP models systematically address model uncertainty when lacking identification data and are applicable also for small data-sets, which both are encountered in WWTP modelling. Serial and parallel hybrid structures were designed to address the challenges of missing input data, insufficient mechanistic model accuracy and demanding model parameter estimation. Results of full-scale effluent predictions show that, by applying hybrid models, the accuracy of the model is improved. Good results were obtained also for default values of activated sludge model parameters, which significantly simplifies the model design process.

本文介绍了废水处理厂（WWTP）的混合模型的设计，旨在提高废水预测的质量。通过结合机械（即活性污泥模型）和数据驱动模型，可以保持物理透明性并达到良好的预测精度。对于数据驱动模型，应用了基于高斯过程（GP）模型的最新机器学习方法。GP模型在缺少识别数据时系统地解决模型不确定性，并且也适用于小型数据集，这在WWTP建模中都会遇到。设计串行和并行混合结构以解决缺少输入数据，机械模型精度不足和模型参数估计要求高的挑战。全面的废水预测结果表明，通过应用混合模型，可以提高模型的准确性。活性污泥模型参数的默认值也获得了良好的结果，这大大简化了模型设计过程。