Simulation Analysis System and Method for Dioxin Concentration in Furnace of Municipal Solid Waste Incineration Process

    公开(公告)号:US20240143872A1

    公开(公告)日:2024-05-02

    申请号:US18407170

    申请日:2024-01-08

    CPC classification number: G06F30/20 G06F2111/10 G06F2119/08

    Abstract: A simulation analysis system for dioxin concentration in furnace of municipal solid waste incineration process includes an area division module, the area division module is connected with a numerical simulation module, the numerical simulation module is connected with a single-factor analysis module, the single-factor analysis module includes an orthogonal test analysis module, and the orthogonal test analysis module is connected with a control module; the area division module is used for dividing areas in the incinerator, the numerical simulation module is used for conducting modeling simulation on the divided areas, the single-factor analysis module is used for conducting single-factor analysis according to the output of the numerical simulation module, and the orthogonal test analysis module is used for conducting orthogonal test analysis according to the output of the numerical simulation module.

    RAPID DEMAGNETIZATION METHOD BASED ON CHARACTERISTICS OF MAGNETIC MEDIA

    公开(公告)号:US20220093307A1

    公开(公告)日:2022-03-24

    申请号:US17541903

    申请日:2021-12-03

    Abstract: A rapid demagnetization method based on characteristics of magnetic media. In the method, basic information is obtained by a recognition module of magnetic media by means of multi-source sensing collaboration. The magnetic medium is identified by using a data processing technology and a magnetic medium identification algorithm, and then the characteristic information is extracted. Optimized set values of demagnetization parameters are obtained by a demagnetization parameter optimizing and setting module based on a demagnetization optimizing model. Demagnetization parameter set values are tracked by a closed-loop control module of a demagnetization magnetic field in combination with domain expert knowledge by using a closed-loop control mechanism integrated with a magnetic field control algorithm, a charging-discharging device, a magnetic field generating device, a magnetic field sensor and an environmental sensor, completing the rapid demagnetization of the magnetic medium.

    A Soft Measurement Method For Dioxin Emission Of Grate Furnace MSWI Process Based On Simplified Deep Forest Regression Of Residual Fitting Mechanism

    公开(公告)号:US20240419872A1

    公开(公告)日:2024-12-19

    申请号:US18727294

    申请日:2023-04-26

    Abstract: The invention provides a soft measurement method for dioxin emission of grate furnace MSWI process based on simplified deep forest regression of residual fitting mechanism. The highly toxic pollutant dioxin (DXN) generated in the solid waste incineration process is a key environmental index which must be subjected to control. The rapid and accurate soft measurement of the DXN emission concentration is an urgent affair for reducing the emission control of the pollutants. The method comprises the following steps: firstly, carrying out feature selection on a high-dimensional process variable by adopting mutual information and significance test; then, constructing a simplified deep forest regression (SDFR) algorithm to learn a nonlinear relationship between the selected process variable and the DXN emission concentration; and finally, designing a gradient enhancement strategy based on a residual error fitting (REF) mechanism to improve the generalization performance of a layer-by-layer learning process. The method is superior to other methods in the aspects of prediction precision and time consumption.

    BROAD HYBRID FOREST REGRESSION (BHFR)-BASED SOFT SENSOR METHOD FOR DIOXIN (DXN) EMISSION IN MUNICIPAL SOLID WASTE INCINERATION (MSWI) PROCESS

    公开(公告)号:US20240302341A1

    公开(公告)日:2024-09-12

    申请号:US18276179

    申请日:2022-10-27

    CPC classification number: G01N33/0075 G06N20/20

    Abstract: A broad hybrid forest regression (BHFR)-based soft sensor method for DXN emission in a municipal solid waste incineration (MSWI) process, including: based on a broad learning system (BLS) framework, constructing a BHFR soft sensor model for small sample high-dimensional data by replacing a neuron with a non-differential base learner, where the BHFR soft sensor model includes a feature mapping layer, a latent feature extraction layer, a feature incremental layer and an incremental learning layer, and the method includes: mapping a high-dimensional feature; extracting a latent feature from a feature space of a fully connected hybrid matrix, and reducing model complexity and computation consumption based on an information measurement criterion; enhancing a feature representation capacity by training the feature incremental layer based on an extracted latent feature; and constructing the incremental learning layer based on an incremental learning strategy, obtaining a weight matrix with a Moore-Penrose pseudo-inverse, and implementing high-precision modeling.

    METHOD FOR ERASING INFORMATION FROM ELECTRONIC SCRAP BASED ON DUAL-SECURITY MECHANISM

    公开(公告)号:US20210150041A1

    公开(公告)日:2021-05-20

    申请号:US16822154

    申请日:2020-03-18

    Abstract: According to aspects of the inventive concepts, provided is a method for erasing information based on a dual-security mechanism. A storage medium feature database, an information erasure feature database, and a firmware system feature database are built to match cases for to-be-erased electronic scrap. An erasure solution and a native system data package are generated based on the matching results. The information is erased and an erasure result is evaluated; and the information is recovered on the erased electronic scrap, and a recovery result is evaluated, to implement comprehensive double security evaluation. The information erasure validity of the electronic scrap is checked based on the evaluation results. If an erasure result is invalid, erasure solutions are corrected online based on the evaluation result, until the erasure result is valid and the electronic scrap with a native system recovered is obtained.

    SOFT-SENSING METHOD FOR DIOXIN EMISSIONS OF MSWI PROCESS BASED ON ENSEMBLE T-S FUZZY REGRESSION TREE

    公开(公告)号:US20250117675A1

    公开(公告)日:2025-04-10

    申请号:US18856902

    申请日:2023-04-27

    Abstract: The provided is a Soft-sensing method for dioxin emissions of MSWI process based on ensemble T-S fuzzy regression tree. The highly toxic pollutant dioxins (DXN) generated in the municipal solid waste incineration (MSWI) process based on a grate furnace is a key environment index for realizing operation optimization control of the process. The method comprises the following steps: firstly, constructing a dioxin emission TSFRT model based on a screening layer and a fuzzy reasoning layer; then, a plurality of parameter updating learning algorithms aiming at the fuzzy reasoning antecedent part and the fuzzy reasoning consequent part are provided, and five dioxin emission TSFRT models including TSFRT-I, TSFRT-II, TSFRT-III, TSFRT-IV and TSFRT-V are obtained; finally, by taking the dioxin emission TSFRT-III model as an example, constructing an integrated TSFRT (EnTSFRT) model taking the TSFRT-III as a base learner so as to realize high-precision modeling of the dioxin emission concentration.

    Hardware-in-loop simulation experiment platform of multiple input and multiple output loop control for MSWI process

    公开(公告)号:US20230297736A1

    公开(公告)日:2023-09-21

    申请号:US18201578

    申请日:2023-05-24

    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.

    METHOD FOR PREDICTING DIOXIN EMISSION CONCENTRATION

    公开(公告)号:US20220092482A1

    公开(公告)日:2022-03-24

    申请号:US17544213

    申请日:2021-12-07

    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.

    Soft Measurement Method for Dioxin Emission Concentration In Municipal Solid Waste Incineration Process

    公开(公告)号:US20210233039A1

    公开(公告)日:2021-07-29

    申请号:US16967408

    申请日:2019-12-02

    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.

    METHOD FOR DETECTING A DIOXIN EMISSION CONCENTRATION OF A MUNICIPAL SOLID WASTE INCINERATION PROCESS BASED ON MULTI-LEVEL FEATURE SELECTION

    公开(公告)号:US20210033282A1

    公开(公告)日:2021-02-04

    申请号:US17038723

    申请日:2020-10-26

    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.

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