Multivariable identification of membrane fouling based on compacted cascade neural network

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.

Univariate imputation method for recovering missing data in wastewater treatment process

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.

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

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.

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

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.

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

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.

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

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.

Towards IoT-based sustainable digital communities

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.