公开(公告)号：US12002014B2

公开(公告)日：2024-06-04

申请号：US16967408

申请日：2019-12-02

申请人： BEIJING UNIVERSITY OF TECHNOLOGY

发明人： Jian Tang ,  Junfei Qiao ,  Zihao Guo ,  Haijun He

IPC分类号： G06Q10/30 ,  G05B13/02 ,  G05B13/04 ,  G06N20/10

CPC分类号： G06Q10/30 ,  G05B13/027 ,  G05B13/042 ,  G05B13/048 ,  G06N20/10

摘要： Disclosed is a soft measurement method of DXN emission concentration based on multi-source latent feature selective ensemble (SEN) modeling. First, MSWI process data is divided into subsystems of different sources according to industrial processes, and principal component analysis (PCA) is used to separately extract the subsystems' latent features and conduct multi-source latent feature primary selection according to the threshold value of the principal component contribution rate preset by experience. Using mutual information (MI) to evaluate the correlation between the latent features of the primary selection and DXN, and adaptively determine the upper and lower limits and thresholds of the latent feature reselection; finally, based on the reselected latent features, a least squares-support vector machine (LS-SVM) algorithm with a hyperparameter adaptive selection mechanism is used to establish DXN emission concentration sub-models for different subsystems, and based on branch and bound (BB) and prediction error information entropy weighting algorithm to optimize the selection of sub-models and calculation weights coefficient, a SEN soft measurement model of DXN emission concentration is constructed.

公开(公告)号：US20170185892A1

公开(公告)日：2017-06-29

申请号：US15186260

申请日：2016-06-17

申请人： Beijing University of Technology

发明人： Honggui Han ,  Yanan Guo ,  Junfei Qiao

IPC分类号： G06N3/08 ,  G05B19/406

CPC分类号： G05B19/406 ,  G06N3/0445 ,  G06N3/082 ,  Y02P80/114

摘要： Under conventional techniques, wastewater treatment has many problems such as poor production conditions, serious random interference, strong nonlinear behavior, large time-varying, and serious lagging. These problem cause difficult detection of various wastewater treatment parameter such as biochemical oxygen demand (BOD) values that are used to monitor water quality. To solve problems associated with monitoring BOD values in real-time, the present disclosure utilizes a self-organizing recurrent RBF neural network designed for intelligent detecting of BOD values. Implementations of the present disclosure build a computing model of BOD values based on the self-organizing recurrent RBF neural network to achieve real-time and more accurate detection of the BOD values (e.g., a BOD concentration). The implementations herein quickly and accurately obtain BOD concentrations and improve the quality and efficiency of wastewater treatment.

公开(公告)号：US09633307B2

公开(公告)日：2017-04-25

申请号：US14668836

申请日：2015-03-25

申请人： BEIJING UNIVERSITY OF TECHNOLOGY

发明人： Junfei Qiao ,  Ying Hou ,  Honggui Han ,  Wenjing Li

IPC分类号： G06N3/08 ,  G01N33/18 ,  G06N3/04 ,  C02F3/00 ,  C02F101/16

CPC分类号： G06N3/088 ,  C02F3/006 ,  C02F2101/16 ,  C02F2209/001 ,  C02F2209/006 ,  C02F2209/04 ,  C02F2209/10 ,  C02F2209/14 ,  C02F2209/18 ,  C02F2209/22 ,  C02F2209/40 ,  G01N33/18 ,  G01N33/188 ,  G06N3/0445

摘要： An intelligent method is designed for predicting the effluent ammonia-nitrogen concentration in the urban wastewater treatment process (WWTP). The technology of this invention is part of advanced manufacturing technology, belongs to both the field of control engineering and environment engineering. In order to improve the predicting efficiency, a recurrent self-organizing neural network, which can adjust the structure and parameters concurrently to train the parameters, is developed to design this intelligent method. This intelligent method can predict the effluent ammonia-nitrogen concentration with acceptable accuracy and solve the problem that the effluent ammonia-nitrogen concentration is difficult to be measured online. Moreover, the online information of effluent ammonia-nitrogen concentration, predicted by this intelligent method, can enhance the quality monitoring level and alleviate the current situation of wastewater to strengthen the whole management of WWTP.

公开(公告)号：US20240310350A1

公开(公告)日：2024-09-19

申请号：US18334725

申请日：2023-06-14

申请人： BEIJING UNIVERSITY OF TECHNOLOGY

发明人： Cuili Yang ,  Sheng Yang ,  Junfei Qiao

IPC分类号： G01N33/18

CPC分类号： G01N33/188 ,  C02F1/00 ,  C02F2101/16

摘要： The present disclosure provides a method and system for predicting effluent ammonia nitrogen (NH4—N) and an electronic device. The method includes: obtaining data to be tested; and inputting the data to be tested into a trained deep echo state network, to obtain predicted NH4—N concentration. A method for establishing the deep echo state network includes: establishing an original network, where the original network includes a plurality of input variables and reservoirs, and a principal component analysis (PCA) mapping layer is added between adjacent ones of the reservoirs; initializing the original network to obtain an initialized network; performing parameter optimization on the initialized network by a matrix generation method of singular value decomposition and a competitive swarm optimizer (CSO) algorithm, to obtain an optimized network; and training and testing the optimized network, to obtain the trained deep echo state network.

公开(公告)号：US11976817B2

公开(公告)日：2024-05-07

申请号：US17038723

申请日：2020-10-26

申请人： Beijing University of Technology

发明人： Junfei Qiao ,  Zihao Guo ,  Jian Tang

IPC分类号： F23G5/00 ,  F23G5/50 ,  G01N33/00

CPC分类号： F23G5/50 ,  G01N33/0036 ,  F23G2207/10 ,  F23G2208/00

摘要： A method for detecting a dioxin emission concentration of a municipal solid waste incineration process based on multi-level feature selection. A grate furnace-based MSWI process is divided into a plurality of sub-processes. A correlation coefficient value, a mutual information value and a comprehensive evaluation value between each of original input features of the sub-processes and the DXN emission concentration are obtained, thereby obtaining first-level features. The first-level features are selected and statistically processed by adopting a GAPLS-based feature selection algorithm and according to redundancy between different features, thereby obtaining second-level features. Third-level features are obtained according to the first-level features and statistical results of the second-level features. A PLS algorithm-based DXN detection model is established based on model prediction performance and the third-level features. The obtained PLS algorithm-based DXN detection model is applied to detect the DXN emission concentration of the MSWI process.

公开(公告)号：US20220194830A1

公开(公告)日：2022-06-23

申请号：US17691096

申请日：2022-03-09

申请人： BEIJING UNIVERSITY OF TECHNOLOGY

发明人： Honggui Han ,  Lu Zhang ,  Junfei Qiao

IPC分类号： C02F3/00 ,  G05B13/04 ,  C02F3/02

摘要： In a cooperative optimal control system, firstly, two-level models are established to capture the dynamic features of different time-scale performance indices. Secondly, a data-driven assisted model based cooperative optimization algorithm is developed to optimize the two-level models, so that the optimal set-points of dissolved oxygen and nitrate nitrogen can be acquired. Thirdly, a predictive control strategy is designed to trace the obtained optimal set-points of dissolved oxygen and nitrate nitrogen. This proposed cooperative optimal control system can effectively deal with the difficulties of formulating the dynamic features and acquiring the optimal set-points.

公开(公告)号：US20180029900A1

公开(公告)日：2018-02-01

申请号：US15389755

申请日：2016-12-23

申请人： Beijing University of Technology

发明人： Honggui Han ,  Yanan Guo ,  Junfei Qiao

IPC分类号： C02F1/00 ,  G06N3/08 ,  G06N3/04

CPC分类号： C02F1/008 ,  C02F3/006 ,  C02F2101/16 ,  C02F2209/003 ,  C02F2209/08 ,  C02F2209/14 ,  C02F2209/15 ,  C02F2209/16 ,  C02F2209/18 ,  G06N3/0445 ,  G06N3/0481 ,  G06N3/08 ,  G06N3/082 ,  G06N3/088

摘要： In this present disclosure, a computing implemented method is designed for predicting the effluent total nitrogen concentration (TN) in an urban wastewater treatment process (WWTP). The technology of this present disclosure is part of advanced manufacturing technology and belongs to both the field of control engineer and environment engineer. To improve the predicting efficiency, a recurrent self-organizing RBF neural network (RSORBFNN) can adjust the structure and parameters simultaneously. This RSORBFNN is developed to implement this method, and then the proposed RSORBFNN-based method can predict the effluent TN with acceptable accuracy. Moreover, online information of effluent TN may be predicted by this computing implemented method to enhance the quality monitoring level to alleviate the current situation of wastewater and to strengthen the management of WWTP.

公开(公告)号：US12105075B2

公开(公告)日：2024-10-01

申请号：US17472433

申请日：2021-09-10

申请人： BEIJING UNIVERSITY OF TECHNOLOGY

发明人： Honggui Han ,  Chenxuan Sun ,  Junfei Qiao

IPC分类号： G06N3/043 ,  C02F1/00 ,  G01N33/18 ,  G06N3/08

CPC分类号： G01N33/1806 ,  G06N3/043 ,  G06N3/08 ,  C02F1/008 ,  C02F2209/16

摘要： A total nitrogen intelligent detection system based on multi-objective optimized fuzzy neural network belongs to both the field of environment engineer and control engineer. The total nitrogen in wastewater treatment process is an important index to measure the quality of effluent. However, it is extremely difficult to detect the total nitrogen concentration due to the long detection time and the low prediction accuracy in the wastewater treatment process. To solve the problem, multi-objective optimized fuzzy neural network with global optimization capability may be established to optimize the structure and parameters to solve the problem of the poor generalization ability of fuzzy neural network. The experimental results show that total nitrogen intelligent detection system can automatically collect the variables information of wastewater treatment process and predict total nitrogen concentration. Meanwhile, in this system, the detection method can improve the prediction accuracy, as well as ensure the total nitrogen concentration be obtained in real-time and low-cost.

公开(公告)号：US20220383062A1

公开(公告)日：2022-12-01

申请号：US17678949

申请日：2022-02-23

申请人： BEIJING UNIVERSITY OF TECHNOLOGY

发明人： Honggui Han ,  Xing Bai ,  Ying Hou ,  Hongyan Yang ,  Junfei Qiao

IPC分类号： G06N3/00 ,  G05B19/05

摘要： An optimal control method for wastewater treatment process (WWTP) based on a self-adjusting multi-task particle swarm optimization (SA-MTPSO) algorithm belongs to the field of WWTP. To balance the relationship between the effluent water quality (EQ) and energy consumption (EC) and achieve optimization online quickly, the invention establishes a data-based multi-task optimization model for WWTP to describe the relationship between the control variables and EQ, EC. Then, the SA-MTPSO algorithm is adopted to solve the optimal set-points of the nitrate nitrogen and dissolved oxygen concentration for WWTP. The PID controller is used to track the optimal set-points, so as to reduce EC while ensuring EQ, and realize the online optimal control of WWTP.

公开(公告)号：US11346831B2

公开(公告)日：2022-05-31

申请号：US15186260

申请日：2016-06-17

申请人： Beijing University of Technology

发明人： Honggui Han ,  Yanan Guo ,  Junfei Qiao

IPC分类号： G01N33/18 ,  G06N3/04 ,  G06N3/08 ,  C02F3/00 ,  C02F3/12 ,  G05B19/406

摘要： Under conventional techniques, wastewater treatment has many problems such as poor production conditions, serious random interference, strong nonlinear behavior, large time-varying, and serious lagging. These problems cause difficulty in detecting wastewater treatment parameters such as biochemical oxygen demand (BOD) values that are used to monitor water quality. To solve problems associated with monitoring BOD values in real-time, the present disclosure utilizes a self-organizing recurrent RBF neural network designed for intelligent detecting of BOD values. Implementations of the present disclosure build a computing model of BOD values based on the self-organizing recurrent RBF neural network to achieve real-time and more accurate detection of the BOD values (e.g., a BOD concentration). The implementations herein quickly and accurately obtain BOD concentrations and improve the quality and efficiency of wastewater treatment.