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 r...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.展开更多
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 ...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.展开更多
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 ob...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.展开更多
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)base...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.展开更多
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 degrad...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.展开更多
In wastewater treatment systems,extracting meaningful features from process data is essential for effective monitoring and control.However,the multi-time scale data generated by different sampling frequencies pose a c...In wastewater treatment systems,extracting meaningful features from process data is essential for effective monitoring and control.However,the multi-time scale data generated by different sampling frequencies pose a challenge to accurately extract features.To solve this issue,a multi-timescale feature extraction method based on adaptive entropy is proposed.Firstly,the expert knowledge graph is constructed by analyzing the characteristics of wastewater components and water quality data,which can illustrate various water quality parameters and the network of relationships among them.Secondly,multiscale entropy analysis is used to investigate the inherent multi-timescale patterns of water quality data in depth,which enables us to minimize information loss while uniformly optimizing the timescale.Thirdly,we harness partial least squares for feature extraction,resulting in an enhanced representation of sample data and the iterative enhancement of our expert knowledge graph.The experimental results show that the multi-timescale feature extraction algorithm can enhance the representation of water quality data and improve monitoring capabilities.展开更多
Active fault-tolerant control utilizes information obtained from fault diagnosis to reconfigure the control law to compensate for faults in the wastewater treatment process. However, since the similarity of fault char...Active fault-tolerant control utilizes information obtained from fault diagnosis to reconfigure the control law to compensate for faults in the wastewater treatment process. However, since the similarity of fault characteristic in the incipient stage can result in misdiagnosis, it is a challenge for fault-tolerant control to ensure system safety and reliability. Therefore, to address this issue, a fault diagnosis and fault-tolerant control with a knowledge transfer strategy(KT-FDFTC) is proposed in this paper. First, a knowledge reasoning diagnosis strategy using multi-source transfer learning is designed to distinguish the similar characteristic of incipient faults. Then, the multi-source knowledge can assist in the diagnosis strategy to strengthen the fault information for fault-tolerant control. Second, a knowledge adaptive compensation mechanism, which makes knowledge and data coupled into the output trajectory regarded as an objective function, is employed to dynamically compute the control law. Then, KT-FDFTC can ensure the stable operation to adapt to various fault conditions. Third, the Lyapunov function is established to demonstrate the stability of KT-FDFTC. Then, the theoretical basis can offer the successful application of KTFDFTC. Finally, the proposed method is validated through a real WWTP and a simulation platform. The experimental results confirm that KT-FDFTC can provide good diagnosis performance and fault tolerance ability.展开更多
The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches of...The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches often overlook practical input constraints or rely on centralized information. To address these issues, a novel edge-based double-layer adaptive control framework is proposed. Specifically, adaptive scaling parameters are embedded into the edge weights of the communication graph, enabling a fully distributed scheme that avoids dependence on centralized or global knowledge. Every participant modifies its strategy by exclusively utilizing local information and communicating with its neighbors to iteratively approach the NE. By incorporating damping terms into the design of the adaptive parameters, the proposed approach effectively suppresses unbounded parameter growth and consequently guarantees the boundedness of the adaptive gains. In addition, to account for actuator saturation, the proposed distributed NE seeking approach incorporates a saturation function, which ensures that control inputs do not exceed allowable ranges. A rigorous Lyapunov-based analysis guarantees the convergence and boundedness of all system variables. Finally, the presentation of simulation results aims to validate the efficacy and theoretical soundness of the proposed approach.展开更多
Fault diagnosis techniques,which are crucial in the field of industrial intelligent manufacturing,are capable of equipment performance maintenance and productivity improvement.In fault diagnosis,multi-type sensors are...Fault diagnosis techniques,which are crucial in the field of industrial intelligent manufacturing,are capable of equipment performance maintenance and productivity improvement.In fault diagnosis,multi-type sensors are commonly used for monitoring because a single data source fails to provide sufficient information to support the comprehensive analysis and accurate diagnosis.Hidden information between modes can be mined using data fusion techniques,enabling more effective decision-making and condition analysis.However,the data measured by multiple sensors are subject to issues such as varying types,an imbalanced ratio of positive to negative samples,and significant differences in data structure,making multi-source data fusion and inter-feature information acquisition challenging.To address these problems,we propose a fault diagnosis method based on dynamic convolution and polarized self-attention(DC-PSA)feature fusion networks.Given that unimodal features are not utilized comprehensively enough,we propose a dynamic convolution-based feature self-convergence model.The ability of the model is improved by attentively aggregating multiple convolution kernels,which are combined in a form dynamically adjusted according to different inputs to fully utilize the features.To enable effective feature-level integration across modalities,we establish a cross-attention-based multimodal fusion model,where each modal branch learns multiscale spatial information independently and forms cross-channel interactions in a localized manner,which can realize the information interactions between local and global channel attention.Empirical results on the Paderborn benchmark dataset validate that the proposed method captures the complementary characteristics across signal types more effectively than existing methods,leading to a notable boost in diagnostic accuracy following the fusion process.The accuracy of the proposed model reached 98.6%,representing an improvement of 8.74%compared to the baseline model.展开更多
The biodegradability evaluation of petrochemical wastewater is vital for regulating the petrochemical wastewater treatment process.Nevertheless,the essential datasets derived by instruments with different sampling sca...The biodegradability evaluation of petrochemical wastewater is vital for regulating the petrochemical wastewater treatment process.Nevertheless,the essential datasets derived by instruments with different sampling scales are characterized by multiple time scales,making it challenging for the existing data-driven biodegradability evaluation methods to achieve feasible results.In this paper,an intelligent evaluation method is proposed based on multiple time-scale analyses to ensure realtime and accurate biodegradability evaluation of the petrochemical wastewater treatment process.Firstly,a multiple time-scale reconfiguration method is introduced to regularize the datasets consistently by regulating the time-series characteristics of the collected variables.Moreover,missing data for large time-scale variables are supplemented by linear interpolation.Secondly,a multi-scale feature extraction algorithm based on partial least squares is designed to obtain biodegradability feature variables and remove noise and redundant information.Thirdly,an intelligent evaluation model based on a dynamic fuzzy min-max neural network is established to realize the classification of biodegradability.Finally,the proposed evaluation method is applied to the practical petrochemical wastewater treatment process.The experimental results demonstrate that the proposed method can provide real-time and accurate evaluation of the petrochemical wastewater biodegradability.展开更多
Transfer learning algorithms can transform prior knowledge into linearization knowledge to model nonlinear systems.However,the linearization knowledge-based models tend to diverge in the process of knowledge lineariza...Transfer learning algorithms can transform prior knowledge into linearization knowledge to model nonlinear systems.However,the linearization knowledge-based models tend to diverge in the process of knowledge linearization due to the neglected information of higher-order terms.To overcome this problem,a second-order knowledge filter transfer learning algorithm(SOFTLA)is developed for modeling nonlinear systems.First,a knowledge transformation strategy is introduced to transform the linearization source knowledge into comprehensive knowledge containing first-order and second-order terms.Compared with the original knowledge,the transformed source knowledge with second-order term can prevent information loss during the knowledge linearization.Second,a knowledge filter algorithm is proposed to eliminate the useless information in the source knowledge.Subsequently,a suitable filter gain is designed to reduce the cumulative error in knowledge updating process.Third,a model adaptation mechanism is designed to enable effective knowledge transfer by updating the structure and parameters of the target model simultaneously.Subsequently,the adaptability of the source knowledge is enhanced to facilitate learning tasks in the target domain.Finally,a benchmark problem and several practical industrial applications are presented to validate the superiority of SOFTLA.The experimental discussions illustrate that SOFTLA can obtain obvious advantages over contrastive methods.展开更多
In response to escalating environmental protection standards,enhancing effluent quality(EQ)and process efficiency within wastewater treatment processes(WWTP)has become paramount.Effluent scheduling is a crucial part o...In response to escalating environmental protection standards,enhancing effluent quality(EQ)and process efficiency within wastewater treatment processes(WWTP)has become paramount.Effluent scheduling is a crucial part of WWTPs as it regulates the effluent residence time by adjusting the flow rate,which significantly impacts the biochemical reaction process.However,the discrete regulation and time-varying nature of WWTPs present crucial challenges in achieving effective effluent scheduling.In this study,sampling-based particle swarm optimization is proposed to solve the dynamic effluent scheduling for WWTPs.First,priority-based encoding and decoding methods are proposed to map the relationship between the decision variables and schedules.Second,the Wasserstein distance is introduced to design an initialization strategy to track the new global optimum in the dynamic environment of WWTPs.Third,a velocity update method is designed to improve the search efficiency by sampling the elitist neighbor solution.Fourth,a dynamic constraint handling method is developed to ensure solution feasibility in WWTPs.Finally,the proposed algorithm is tested in Benchmark Simulation Model No.1 to demonstrate its solving ability for the dynamic effluent scheduling problem of WWTPs.Computational experiments with state-of-the-art methods show that the proposed algorithm can achieve superior performance in terms of EQ and process efficiency.展开更多
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 b...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.展开更多
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)em...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.展开更多
基金Supported by the National Natural Science Foundation of China(61622301,61533002)Beijing Natural Science Foundation(4172005)Major National Science and Technology Project(2017ZX07104)
文摘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.
文摘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.
基金Supported by the National Science Foundation of China(61622301,61533002)Beijing Natural Science Foundation(4172005)Major National Science and Technology Project(2017ZX07104)
文摘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.
基金supports by National Key Research and Development Project(2018YFC1900800-5)National Natural Science Foundation of China(61890930-5,62021003,61903010 and 62103012)+1 种基金Beijing Outstanding Young Scientist Program(BJJWZYJH01201910005020)Beijing Natural Science Foundation(KZ202110005009 and 4214068).
文摘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.
基金the National Key Research and Development Project(No.2018YFC1900800-5)the National Natural Science Foundation of China(Nos.61890930-5,61903010,6202100)+1 种基金the Beijing Outstanding Young Scientist Program(No.BJJWZYJH01201910005020)the Beijing Natural Science Foundation(No.KZ202110005009).
文摘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.
基金the National Key Research and Development Program of China(2022YFB3305800-5)the National Natural Science Foundation of China(62125301,62021003)+2 种基金the Beijing Outstanding Young Scientist Program(BJJWZYJH01201910005020)the Natural Science Foundation of Beijing Municipality(KZ202110005009)Youth Beijing Scholar(037).
文摘In wastewater treatment systems,extracting meaningful features from process data is essential for effective monitoring and control.However,the multi-time scale data generated by different sampling frequencies pose a challenge to accurately extract features.To solve this issue,a multi-timescale feature extraction method based on adaptive entropy is proposed.Firstly,the expert knowledge graph is constructed by analyzing the characteristics of wastewater components and water quality data,which can illustrate various water quality parameters and the network of relationships among them.Secondly,multiscale entropy analysis is used to investigate the inherent multi-timescale patterns of water quality data in depth,which enables us to minimize information loss while uniformly optimizing the timescale.Thirdly,we harness partial least squares for feature extraction,resulting in an enhanced representation of sample data and the iterative enhancement of our expert knowledge graph.The experimental results show that the multi-timescale feature extraction algorithm can enhance the representation of water quality data and improve monitoring capabilities.
基金supported by the National Natural Science Foundation of China (Grant Nos.62125301,62021003,62303024,U24A20275,62522302,62473011,92467205)the National Key Research and Development Project (Grant Nos.2022YFB3305800-5,2024YFE0212400)+2 种基金the Youth Beijing Scholars Program (Grant No.037)the Beijing Nova Program (Grant Nos.20240484694,20250484938)the Beijing Natural Science Foundation (Grant No.L253010)。
文摘Active fault-tolerant control utilizes information obtained from fault diagnosis to reconfigure the control law to compensate for faults in the wastewater treatment process. However, since the similarity of fault characteristic in the incipient stage can result in misdiagnosis, it is a challenge for fault-tolerant control to ensure system safety and reliability. Therefore, to address this issue, a fault diagnosis and fault-tolerant control with a knowledge transfer strategy(KT-FDFTC) is proposed in this paper. First, a knowledge reasoning diagnosis strategy using multi-source transfer learning is designed to distinguish the similar characteristic of incipient faults. Then, the multi-source knowledge can assist in the diagnosis strategy to strengthen the fault information for fault-tolerant control. Second, a knowledge adaptive compensation mechanism, which makes knowledge and data coupled into the output trajectory regarded as an objective function, is employed to dynamically compute the control law. Then, KT-FDFTC can ensure the stable operation to adapt to various fault conditions. Third, the Lyapunov function is established to demonstrate the stability of KT-FDFTC. Then, the theoretical basis can offer the successful application of KTFDFTC. Finally, the proposed method is validated through a real WWTP and a simulation platform. The experimental results confirm that KT-FDFTC can provide good diagnosis performance and fault tolerance ability.
基金supported by the National Natural Science Foundation of China (Grant No.62173009)the National Key Research and Development Program of China (Grant No.2021ZD0112302)。
文摘The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches often overlook practical input constraints or rely on centralized information. To address these issues, a novel edge-based double-layer adaptive control framework is proposed. Specifically, adaptive scaling parameters are embedded into the edge weights of the communication graph, enabling a fully distributed scheme that avoids dependence on centralized or global knowledge. Every participant modifies its strategy by exclusively utilizing local information and communicating with its neighbors to iteratively approach the NE. By incorporating damping terms into the design of the adaptive parameters, the proposed approach effectively suppresses unbounded parameter growth and consequently guarantees the boundedness of the adaptive gains. In addition, to account for actuator saturation, the proposed distributed NE seeking approach incorporates a saturation function, which ensures that control inputs do not exceed allowable ranges. A rigorous Lyapunov-based analysis guarantees the convergence and boundedness of all system variables. Finally, the presentation of simulation results aims to validate the efficacy and theoretical soundness of the proposed approach.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFB3307300)the National Natural Science Foundation of China(Grant Nos.62125301,62021003,62373014,92467205)+1 种基金the Beijing Nova Program(Grant No.20240484694)the Beijing Youth Scholar(Grant No.037)。
文摘Fault diagnosis techniques,which are crucial in the field of industrial intelligent manufacturing,are capable of equipment performance maintenance and productivity improvement.In fault diagnosis,multi-type sensors are commonly used for monitoring because a single data source fails to provide sufficient information to support the comprehensive analysis and accurate diagnosis.Hidden information between modes can be mined using data fusion techniques,enabling more effective decision-making and condition analysis.However,the data measured by multiple sensors are subject to issues such as varying types,an imbalanced ratio of positive to negative samples,and significant differences in data structure,making multi-source data fusion and inter-feature information acquisition challenging.To address these problems,we propose a fault diagnosis method based on dynamic convolution and polarized self-attention(DC-PSA)feature fusion networks.Given that unimodal features are not utilized comprehensively enough,we propose a dynamic convolution-based feature self-convergence model.The ability of the model is improved by attentively aggregating multiple convolution kernels,which are combined in a form dynamically adjusted according to different inputs to fully utilize the features.To enable effective feature-level integration across modalities,we establish a cross-attention-based multimodal fusion model,where each modal branch learns multiscale spatial information independently and forms cross-channel interactions in a localized manner,which can realize the information interactions between local and global channel attention.Empirical results on the Paderborn benchmark dataset validate that the proposed method captures the complementary characteristics across signal types more effectively than existing methods,leading to a notable boost in diagnostic accuracy following the fusion process.The accuracy of the proposed model reached 98.6%,representing an improvement of 8.74%compared to the baseline model.
基金supported by the National Key Research and Development Project(Grant No.2018YFC1900800-5)the National Natural Science Foundation of China(Grant Nos.61890930-5,61622301,61903010,62021003,62103012)Beijing Nova Program(Grant No.20240484694)。
文摘The biodegradability evaluation of petrochemical wastewater is vital for regulating the petrochemical wastewater treatment process.Nevertheless,the essential datasets derived by instruments with different sampling scales are characterized by multiple time scales,making it challenging for the existing data-driven biodegradability evaluation methods to achieve feasible results.In this paper,an intelligent evaluation method is proposed based on multiple time-scale analyses to ensure realtime and accurate biodegradability evaluation of the petrochemical wastewater treatment process.Firstly,a multiple time-scale reconfiguration method is introduced to regularize the datasets consistently by regulating the time-series characteristics of the collected variables.Moreover,missing data for large time-scale variables are supplemented by linear interpolation.Secondly,a multi-scale feature extraction algorithm based on partial least squares is designed to obtain biodegradability feature variables and remove noise and redundant information.Thirdly,an intelligent evaluation model based on a dynamic fuzzy min-max neural network is established to realize the classification of biodegradability.Finally,the proposed evaluation method is applied to the practical petrochemical wastewater treatment process.The experimental results demonstrate that the proposed method can provide real-time and accurate evaluation of the petrochemical wastewater biodegradability.
基金supported by the National Natural Science Foundation of China(Grant Nos.62125301,62021003,62103012)the National Key Research and Development Project(Grant No.2022YFB3305800-05)+1 种基金the Beijing Nova Program(Grant No.K7058000202402)Youth Beijing Scholar(Grant No.037).
文摘Transfer learning algorithms can transform prior knowledge into linearization knowledge to model nonlinear systems.However,the linearization knowledge-based models tend to diverge in the process of knowledge linearization due to the neglected information of higher-order terms.To overcome this problem,a second-order knowledge filter transfer learning algorithm(SOFTLA)is developed for modeling nonlinear systems.First,a knowledge transformation strategy is introduced to transform the linearization source knowledge into comprehensive knowledge containing first-order and second-order terms.Compared with the original knowledge,the transformed source knowledge with second-order term can prevent information loss during the knowledge linearization.Second,a knowledge filter algorithm is proposed to eliminate the useless information in the source knowledge.Subsequently,a suitable filter gain is designed to reduce the cumulative error in knowledge updating process.Third,a model adaptation mechanism is designed to enable effective knowledge transfer by updating the structure and parameters of the target model simultaneously.Subsequently,the adaptability of the source knowledge is enhanced to facilitate learning tasks in the target domain.Finally,a benchmark problem and several practical industrial applications are presented to validate the superiority of SOFTLA.The experimental discussions illustrate that SOFTLA can obtain obvious advantages over contrastive methods.
基金supported by the National Key Research and Development Project(Grant No.2022YFB3305800-05)the National Natural Science Foundation of China(Grant Nos.92467205,62021003,62125301)+1 种基金the Beijing Nova Program(Grant No.K7058000202402)the Youth Beijing Scholar(Grant No.037)。
文摘In response to escalating environmental protection standards,enhancing effluent quality(EQ)and process efficiency within wastewater treatment processes(WWTP)has become paramount.Effluent scheduling is a crucial part of WWTPs as it regulates the effluent residence time by adjusting the flow rate,which significantly impacts the biochemical reaction process.However,the discrete regulation and time-varying nature of WWTPs present crucial challenges in achieving effective effluent scheduling.In this study,sampling-based particle swarm optimization is proposed to solve the dynamic effluent scheduling for WWTPs.First,priority-based encoding and decoding methods are proposed to map the relationship between the decision variables and schedules.Second,the Wasserstein distance is introduced to design an initialization strategy to track the new global optimum in the dynamic environment of WWTPs.Third,a velocity update method is designed to improve the search efficiency by sampling the elitist neighbor solution.Fourth,a dynamic constraint handling method is developed to ensure solution feasibility in WWTPs.Finally,the proposed algorithm is tested in Benchmark Simulation Model No.1 to demonstrate its solving ability for the dynamic effluent scheduling problem of WWTPs.Computational experiments with state-of-the-art methods show that the proposed algorithm can achieve superior performance in terms of EQ and process efficiency.
基金supported by the National Natural Science Foundation of China Grant Numbers(61622301,61533002)Beijing Municipal Education Commission Science and Technology Development Program Grant Numbers(KZ201410005002,201410005001)the PhD Programs Foundation of Ministry of Education of China Grant Number(20131103110016).
文摘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.
基金This work was supported by the National Key Research and Development Project(No.2018YFC1900800-5)National Science Foundation of China(Nos.61890930-5,61903010,62021003,and 62125301)+2 种基金Beijing Outstanding Young Scientist Program(No.BJJWZYJH01201910005020)Beijing Natural Science Foundation(No.KZ202110005009)CAAI-Huawei MindSpore Open Fund(No.CAAIXSJLJJ-2021-017A).
文摘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.
