Artificial neural networks (ANNs) are considered efficient tools for modeling complex, non-linear processes with uncertain dynamic models. ANNs were originally applied as effective predictors of diverse processes with static dependence on the input-output information. However, when the ANN must be applied to characterize an approximate model of time-dependent input-output relationships, then it is necessary to introduce the time effect as part of the ANN, yielding to the construction of dynamic ANN or DNN. This review establishes the variants of recurrent and differential forms of DNN, their mathematically formulation as well as the methods to adjust the network weights. The characteristics of DNNs motivate their use to represent the dynamics of decontamination processes. This review details recent findings on the DNN application for the modeling and control of treatment systems based on either biological and chemical processes. The modeling application of DNN for some common methods used in the treatment of wastewater, contaminated soil and atmosphere is described. The major benefits of using the approximate DNN-based model instead of designing the complex mathematical description for each treatment are analyzed in the context of enhancing the efficiency of the decontamination treatment. This review also highlights the remarkable efficiency of DNNs as a keystone tool for modeling and control sequence of treatments. The last section in the review introduces several open researching areas for the application of DNN for decontamination systems based on biochemical and chemical treatments.

人工神经网络（ANN）被认为是用不确定的动力学模型对复杂的非线性过程进行建模的有效工具。人工神经网络最初被用作各种过程的有效预测器，并且静态依赖于输入输出信息。但是，当必须将ANN应用于刻画时间相关的输入-输出关系的近似模型时，则有必要将时间效应作为ANN的一部分引入，从而构建动态ANN或DNN。这篇综述建立了DNN的递归形式和差分形式的变体，其数学公式以及调整网络权重的方法。DNN的特性促使他们使用它来代表去污过程的动态。这篇综述详细介绍了DNN在基于生物和化学过程的处理系统建模和控制中的应用的最新发现。描述了DNN在废水，污染土壤和大气处理中常用方法的建模应用。在提高去污处理效率的背景下，分析了使用基于DNN的近似模型而不是为每种处理设计复杂的数学描述的主要好处。这篇综述还强调了DNN作为建模和控制治疗顺序的关键工具的非凡效率。评论的最后一部分介绍了几个开放的研究领域，这些领域将DNN应用于基于生化和化学处理的去污系统。