This paper aims to present a comprehensive survey on water quality soft-sensing of a wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We mainly present problem formulation of water quality soft-sensing, common soft-sensing models, practical soft-sensing examples and discussion on the performance of soft-sensing models. In details, problem formulation includes characteristic analysis and modeling principle of water quality soft-sensing. The common soft-sensing models mainly include a back-propagation neural network, radial basis function neural network, fuzzy neural network (FNN), echo state network (ESN), growing deep belief network and deep belief network with event-triggered learning (DBN-EL). They are compared in terms of accuracy, efficiency and computational complexity with partial-least-square-regression DBN (PLSR-DBN), growing ESN, sparse deep belief FNN, self-organizing DBN, wavelet-ANN and self-organizing cascade neural network (SCNN). In addition, this paper generally discusses and explains what factors affect the accuracy of the ANNs-based soft-sensing models. Finally, this paper points out several challenges in soft-sensing models of WWTP, which may be helpful for researchers and practitioner to explore the future solutions for their particular applications.

本文旨在对基于人工神经网络 (ANN) 的废水处理过程 (WWTP) 的水质软传感进行全面调查。我们主要介绍水质软测量的问题表述、常见的软测量模型、实际的软测量示例以及对软测量模型性能的讨论。具体来说，问题表述包括水质软测量的特征分析和建模原理。常见的软感知模型主要包括反向传播神经网络、径向基函数神经网络、模糊神经网络（FNN）、回声状态网络（ESN）、增长深度信念网络和事件触发学习深度信念网络（DBN） -EL）。他们在准确性方面进行比较，使用偏最小二乘回归 DBN (PLSR-DBN)、不断增长的 ESN、稀疏深度信念 FNN、自组织 DBN、小波 ANN 和自组织级联神经网络 (SCNN) 来提高效率和计算复杂度。此外，本文一般性地讨论和解释了哪些因素会影响基于人工神经网络的软测量模型的准确性。最后，本文指出了污水处理厂软传感模型中的几个挑战，这可能有助于研究人员和从业人员探索其特定应用的未来解决方案。