Modeling and Prediction of Wastewater Treatment Process Soft

发布时间：2021-02-11 18:13

　　在污水处理过程中,关键出水参数的实时测量对于出水水质起着至关重要的作用。然而,受现存技术的限制,实际污水处理系统中存在很多难以在线测量的变量,生化需氧量（BOD₅）就是其中之一。软测量技术的出现有效地解决了这类问题。在软测量建模中,传统的机器学习方法得到了广泛的应用,但是,这类方法通常被认为是具有一个隐藏层模型结构的浅层学习方法。浅层学习对于简单的非线性过程逼近发挥了很好的作用,但当面对高度复杂的过程时,此类方法则显得力不从心。鉴于此,本文针对污水处理厂可获取的有限样本、过程的高度非线性和动态特性等问题,基于深度学习研究了BOD₅的软测量预测建模方法。主要工作包括:1)为了解决有限标记样本和变量间的严重非线性的问题,考虑深度神经网络堆叠自动编码器（Stacked Autoencoders,SAE）在复杂非线性方面的强处理能力,以及遗传算法（Genetic Algorithm,GA）在寻优方面的良好性能,将二者结合提出了一种污水处理BOD₅在线监测的SAE+GA软测量预测建模方法。该方法首先根据实验数据,选择与BOD

【文章来源】：兰州理工大学甘肃省

【文章页数】：66 页

【学位级别】：硕士

【文章目录】：
摘要
Abstract
Chapter1 Introduction
    1.1 Research Background and Significance
    1.2 Soft Sensor Technologies and Applications in Wastewater System
        1.2.1 Research Status in World
        1.2.2 Research Status in China
    1.3 Overview of Soft Sensor Modeling Methods
        1.3.1 First Principle Models
        1.3.2 Regression Analysis Modeling Methods
        1.3.3 Artificial Intelligence Modeling Methods
    1.4 Thesis Outline
    1.5 Summary
Chapter2 Model Analysis of Wastewater Treatment Process
    2.1 Introduction
    2.2 Overview of the Wastewater Treatment Process
        2.2.2 Key Effluent Parameters Analysis of the WWTPs
        2.2.3 Limited Characteristics of Label Samples
        2.2.4 Process Nonlinearity
        2.2.5 Process Dynamic Characteristics
    2.3 Deep Learning Application of Soft Sensor in the WWTPs
        2.3.1 Stacked Autoencoders and its Parameter Optimization Algorithm
        2.3.2 Recurrent Neural Network and Long-Short Term Memory
        2.3.3 Model Structure Identification Using a Genetic Algorithm
    2.4 Summary
Chapter3 Soft Sensor Modeling of Key Effluent Parameter in Wastewater Treatment Process Based on SAE and GA
    3.1 Introduction
    3.2 Soft Sensor Modeling of Key Effluent Parameter BOD5 Based on SAE and GA
        3.2.1 Soft Sensor Modeling Based on SAE and GA
        3.2.2 Modeling Steps for Soft Sensor Based on SAE and GA
    3.3 Simulation Study
        3.3.1 Case Description
        3.3.2 Augmentation Processing and Data Preprocessing
        3.3.3 Setting Parameters of the Deep Neural Network
        3.3.4 Simulation Experiment and Result Analysis
    3.4 Summary
Chapter4 Dynamic Soft Sensor Modeling of Key Effluent Parameter in Wastewater Treatment Process Based on Recurrent Neural Network LSTM
    4.1 Introduction
    4.2 Dynamic Soft Sensor Modeling of Key Effluent Parameter BOD5 Based on LSTM
        4.2.1 Soft Sensor Modeling Based on LSTM and GA
        4.2.2 Modeling Steps for Soft Sensor Based on LSTM
    4.3 Simulation Study
        4.3.1 Data Preprocessing
        4.3.2 Hyperparameters Optimization
        4.3.3 Simulation Experiment and Result Analysis
    4.4 Summary
Conclusions and Future Work
    Conclusions
    Future Work
Reference
Acknowledgment
List of Publications



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