公开(公告)号：US20230297736A1

公开(公告)日：2023-09-21

申请号：US18201578

申请日：2023-05-24

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Jian TANG ,  TianZheng WANG ,  Heng XIA ,  Junfei QIAO

IPC: G06F30/27 ,  G06Q10/30

CPC classification number: G06F30/27 ,  G06Q10/30 ,  G06F2111/10

Abstract: A hardware-in-loop simulation experiment platform of multiple input and multiple output loop control for MSWI process includes a real equipment layer and a virtual object layer, where in the real equipment layer and the virtual object layer realize communication through hard wirings and data acquisition cards, the real equipment layer and virtual object layer realize communication in OPC mode through Ethernet; the real equipment layer comprises monitoring equipment and control equipment, and the virtual object layer comprises an MSWI actuator model, an MSWI instrument device model and an MSWI process object model which are respectively operated in different industrial personal computers. The hardware-in-loop simulation experiment platform of multiple input and multiple output loop control for MSWI process provided by the invention is used for providing a reliable engineering verification environment for MSWI process control.

公开(公告)号：US20220092482A1

公开(公告)日：2022-03-24

申请号：US17544213

申请日：2021-12-07

Applicant: Beijing University of Technology

Inventor： Jian TANG ,  Heng XIA ,  Junfei QIAO ,  Zihao GUO

IPC: G06N20/20 ,  G06N5/02

Abstract: A method for predicting dioxin (DXN) emission concentration based on hybrid integration of random forest (RF) and gradient boosting decision tree (GBDT). A random sampling of a training sample and an input feature is performed on a modeling data with a small sample size and a high-dimensional characteristic to generate a training subset. J RF-based DXN sub-models based on the training subset are established. J×I GBDT-based DXN sub-models are established by performing I iterations on each of the RF-based DXN sub-models. Predicted outputs of the RF-based DXN sub-model and the GBDT-based DXN sub-model are combined by a simple average weighting method to obtain a final output.

公开(公告)号：US20210233039A1

公开(公告)日：2021-07-29

申请号：US16967408

申请日：2019-12-02

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Jian TANG ,  Junfei QIAO ,  Zihao GUO ,  Haijun HE

IPC: G06Q10/00 ,  G05B13/02 ,  G05B13/04 ,  G06N20/10

Abstract: 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：US20210033282A1

公开(公告)日：2021-02-04

申请号：US17038723

申请日：2020-10-26

Applicant: Beijing University of Technology

Inventor： Junfei QIAO ,  Zihao GUO ,  Jian TANG

IPC: F23G5/50 ,  G01N33/00

Abstract: 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.