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Univariate imputation method for recovering missing data in wastewater treatment process

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Chinese Journal of Chemical Engineering 2023年第1期53卷 201-210页

作者： honggui han Meiting Sun Huayun han Xiaolong Wu Junfei Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

High-quality data play a paramount role in monitoring,control,and prediction of wastewater treatment process(WWTP)and can effectively ensure the efficient and stable operation of system.Missing values seriously degrade the accuracy,reliability and completeness of the data quality due to network collapses,connection errors and data transformation failures.In these cases,it is infeasible to recover missing data depending on the correlation with other variables.To tackle this issue,a univariate imputation method(UIM)is proposed for WWTP integrating decomposition method and imputation algorithms.First,the seasonal-trend decomposition based on loess method is utilized to decompose the original time series into the seasonal,trend and remainder components to deal with the nonstationary characteristics of WWTP data.Second,the support vector regression is used to approximate the nonlinearity of the trend and remainder components respectively to provide estimates of its missing values.A self-similarity decomposition is conducted to fill the seasonal component based on its periodic pattern.Third,all the imputed results are merged to obtain the imputation result.Finally,six time series of WWTP are used to evaluate the imputation performance of the proposed UIM by comparing with existing seven methods based on two indicators.The experimental results illustrate that the proposed UIM is effective for WWTP time series under different missing ratios.Therefore,the proposed UIM is a promising method to impute WWTP time series.

关键词： Univariate Self-similarity Waste water Algorithm Integration

Multivariable identification of membrane fouling based on compacted cascade neural network

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Chinese Journal of Chemical Engineering 2023年第1期53卷 37-45页

作者： Kun Ren Zheng Jiao Xiaolong Wu honggui han Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China

The membrane fouling phenomenon,reflected with various fouling characterization in the membrane bioreactor(MBR)process,is so complicated to distinguish.This paper proposes a multivariable identification model(MIM)based on a compacted cascade neural network to identify membrane fouling accurately.Firstly,a multivariable model is proposed to calculate multiple indicators of membrane fouling using a cascade neural network,which could avoid the interference of the overlap inputs.Secondly,an unsupervised pretraining algorithm was developed with periodic information of membrane fouling to obtain the compact structure of MIM.Thirdly,a hierarchical learning algorithm was proposed to update the parameters of MIM for improving the identification accuracy online.Finally,the proposed model was tested in real plants to evaluate its efficiency and effectiveness.Experimental results have verified the benefits of the proposed method.

关键词： Membrane fouling Permeability Cascade neural networks Model Prediction

Towards IoT-based sustainable digital communities

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Intelligent and Converged Networks 2022年第2期3卷 190-203页

作者： Fangyu Li Hongyan Yang Xuejin Gao honggui han Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemEngineering Research Center of Digital CommunityMinistry of Educationand Beijing Artificial Intelligence InstituteBeijing University of TechnologyBeijing 100124China

Seeking continuous development,a modern community must also be able to adapt to future possible challenges using constrained or limited resources.As a revolutionary communication paradigm,the Internet of Things(IoT)empowers the cutting-edge and emerging applications which enable manifold new intelligent services towards a smart community.The sophisticated ecosystem of a digital community is made feasible by the IoT infrastructure,which also provides community control with access to a wealth of actual data.In addition,IoT platforms empower the ubiquitous computing ability,providing more potentials to the actuators in perception layer in the IoT architecture.With more and more population in the urban areas,sustainability issues have become a key factor to consider in the development of a digital community.We give a modern survey in this study on the most recent developments in IoT for sustainable digital communities.After carefully examining the most recent literature,we specifically highlight the various smart digital community application scenarios,such as smart buildings,energy management,green transportation,trash management,etc.We also look into a number of major issues facing the use of IoT technology in digital communities.Furthermore,we discuss potential future applications and future research areas for IoT,the critical component of sustainable digital communities.

关键词： Internet of Things digital communities sustainability smart systems

Data-driven intelligent monitoring system for key variables in wastewater treatment process

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Chinese Journal of Chemical Engineering 2018年第10期26卷 2093-2101页

作者： honggui han Shuguang Zhu Junfei Qiao Min Guo College of Electronic Information & Control Engineering Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China Engineering Research Center of Digital Community Ministry of Education Beijing 100124 China Beijing Laboratory for Urban Mass Transit Beijing 100124 China

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.

关键词：监视系统水处理过程数据驱动传感器技术传感器模型模糊神经网络分发服务主要部件

A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method

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Chinese Journal of Chemical Engineering 2018年第5期26卷 1071-1077页

作者： honggui han Xiaolong Wu Luming Ge Junfei Qiao College of Automation Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China

In this study, a multivariate local quadratic polynomial regression(MLQPR) method is proposed to design a model for the sludge volume index(SVI). In MLQPR, a quadratic polynomial regression function is established to describe the relationship between SVI and the relative variables, and the important terms of the quadratic polynomial regression function are determined by the significant test of the corresponding coefficients. Moreover, a local estimation method is introduced to adjust the weights of the quadratic polynomial regression function to improve the model accuracy. Finally, the proposed method is applied to predict the SVI values in a real wastewater treatment process(WWTP). The experimental results demonstrate that the proposed MLQPR method has faster testing speed and more accurate results than some existing methods.

关键词：多项式回归模型基回归方法索引体积污泥评价方法废水处理

Wastewater treatment control method based on a rule adaptive recurrent fuzzy neural network

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International Journal of Intelligent Computing and Cybernetics 2017年第2期10卷 94-110页

作者： Junfei Qiao Gaitang han honggui han Wei Chai Faculty of Information Technology Beijing University of TechnologyBeijingChina Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of TechnologyBeijingChina

Purpose-The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.Design/methodology/approach-A control strategy based on rule adaptive recurrent neural network(RARFNN)is proposed in this paper to control the dissolved oxygen(DO)concentration and nitrate nitrogen(SNo)concentration.The structure of the RARFNN is self-organized by a rule adaptive algorithm,and the rule adaptive algorithm considers the overall information processing ability of neural network.Furthermore,a stability analysis method is given to prove the convergence of the proposed RARFNN.Findings-By application in the control problem of wastewater treatment process(WWTP),results show that the proposed control method achieves better performance compared to other methods.Originality/value-The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic WWTP.The RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant concentrations.And,the rule adaptive mechanism considers the overall information processing ability judgment of the neural network,which can ensure that the neural network contains the information of the biochemical reactions.

关键词： Information processing ability Recurrent fuzzy neural network Rule adaptive Wastewater treatment

A data-derived soft-sensor method for monitoring effluent total phosphorus

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Chinese Journal of Chemical Engineering 2017年第12期25卷 1791-1797页

作者： Shuguang Zhu honggui han Min Guo Junfei Qiao College of Electronic Information & Control Engineering Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China Engineering Research Center of Digital Community Ministry of Education Beijing 100124 China Beijing Laboratory for Urban Mass Transit Beijing 100124 China

The effluent total phosphorus(ETP) is an important parameter to evaluate the performance of wastewater treatment process(WWTP). In this study, a novel method, using a data-derived soft-sensor method, is proposed to obtain the reliable values of ETP online. First, a partial least square(PLS) method is introduced to select the related secondary variables of ETP based on the experimental data. Second, a radial basis function neural network(RBFNN) is developed to identify the relationship between the related secondary variables and ETP. This RBFNN easily optimizes the model parameters to improve the generalization ability of the soft-sensor. Finally, a monitoring system, based on the above PLS and RBFNN, named PLS-RBFNN-based soft-sensor system, is developed and tested in a real WWTP. Experimental results show that the proposed monitoring system can obtain the values of ETP online and own better predicting performance than some existing methods.

关键词：导出数据软传感器自河全部的磷废水处理过程光线的基础功能神经网络部分最不方形的方法

数据驱动的污水处理曝气过程低碳优化控制方法

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北京工业大学学报 2024年第2期50卷 131-139页

作者：韩红桂赵雅倩杨宏燕伍小龙北京工业大学信息学部北京100124 北京工业大学计算智能与智能系统北京市重点实验室北京100124 北京工业大学环境与生命学部北京100124

针对现有的污水处理过程存在碳排放机理不清且难以评估,无法通过有效的调控方式降低碳排放总量的问题,设计了一种数据驱动的污水处理曝气过程低碳优化控制方法。首先,通过深入分析碳排放影响因素及其与水质指标的相互关系,获得了曝气过... 详细信息

针对现有的污水处理过程存在碳排放机理不清且难以评估,无法通过有效的调控方式降低碳排放总量的问题,设计了一种数据驱动的污水处理曝气过程低碳优化控制方法。首先,通过深入分析碳排放影响因素及其与水质指标的相互关系,获得了曝气过程各水质指标和碳排放之间的关联关系;其次,采用数据驱动的方法,设计了曝气过程能耗与碳排放的优化模型,以获取曝气过程最优化的控制策略;最后,将获取的曝气过程低碳优化控制方法应用于基准仿真模型。测试结果说明该方法能够有效地跟踪控制曝气过程,降低能耗与碳排放量总量。

关键词：污水处理温室气体排放曝气能耗碳排放模型优化控制数据驱动

基于多元特征异构集成深度学习的图像识别模型及其应用

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北京工业大学学报 2024年第1期50卷 27-37页

作者：汤健田昊夏恒王子轩徐喆韩红桂北京工业大学信息学部北京100124 智慧环保北京实验室北京100124

随着城市矿产资源循环利用技术的不断发展,废旧手机回收已成为当前研究热点。受限于计算资源和数据资源的相对缺乏,目前基于线下智能回收装备的废旧手机识别精度难以达到实际应用。针对上述问题,提出一种基于多元特征异构集成深度学习... 详细信息

随着城市矿产资源循环利用技术的不断发展,废旧手机回收已成为当前研究热点。受限于计算资源和数据资源的相对缺乏,目前基于线下智能回收装备的废旧手机识别精度难以达到实际应用。针对上述问题,提出一种基于多元特征异构集成深度学习的图像识别模型。首先,利用字符级文本检测算法(character region awareness for text detection,CRAFT)提取手机背部字符区域,再利用ImageNet预训练的VGG19模型作为图像特征嵌入模型,利用迁移学习理念提取待回收手机的局部字符特征和全局图像特征;然后,利用局部特征构建神经网络模式光学字符识别(optical character recognition,OCR)模型,利用全局和局部特征构建非神经网络模式深度森林分类(deep forest classification,DFC)模型;最后,将异构OCR和DFC识别模型输出的结果与向量组合后输入Softmax进行集成,基于权重向量得分最大准则获取最终识别结果。基于废旧手机回收装备的真实图像验证了所提方法的有效性。

关键词：废旧手机图像识别迁移学习多元特征 OCR 深度森林异构集成