Abstract: A system and a method for exhaust emission modeling and analysis of a municipal solid waste incineration process are provided.

Abstract: The present disclosure provided a RNA polymerase from a psychrophilic bacteriophage VSW-3. The RNA polymerase retains its RNA polymerase activity at low temperature (e.g., as low as 4° C.), is not sensitive to Class II transcription terminator, does not produce 3? cis extension in the transcripts, and generates transcripts without detectable dsRNA contamination.

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.

Abstract: An online soft measurement method for dioxin emission concentration in MSWI process includes: performing principal component analysis on process data according to a historical process data set of the MSWI process to obtain a drift index control limit; constructing an offline model based on FTBL, and inputting the process data and historical DXN true value data of the MSWI into an off-line model; performing principal component analysis according to online data, judging whether the online data is drift data according to the drift index control limit; if the typical sample pool is the drift data, constructing an online model based on FTBL, and inputting the process data and the drift data of the typical sample pool and output data of the incremental layer of the offline model into the online model; and determining a DXN emission concentration predicted value according to an offline calculation result and an online calculation result.

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.

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.

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.

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-inv

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.

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.

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.

Abstract: Provided are a method, apparatus and system for detecting obstacle collision in an automatic parking path. The method includes: geometrically performing an obstacle collision detection in a circular arc path and/or an obstacle collision detection in a straight line path by using a path contour, a vehicle contour and an obstacle contour. The system includes a collision detection module for geometrically performing detection of obstacle collision in a circular arc path and/or detection of obstacle collision in a straight line path by using a path contour, a vehicle contour and an obstacle contour. The apparatus includes: a memory and a processor configured for geometrically performing detection of obstacle collision in a circular arc path and/or detection of obstacle collision in a straight line path by using a path contour, a vehicle contour and an obstacle contour.

Abstract: The present disclosure provided a RNA polymerase from a psychrophilic bacteriophage VSW-3. The RNA polymerase retains its RNA polymerase activity at low temperature (e.g., as low as 4° C.), is not sensitive to Class II transcription terminator, does not produce 3? cis extension in the transcripts, and generates transcripts without detectable dsRNA contamination.

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.

Abstract: Provided are a method, apparatus and system for detecting obstacle collision in an automatic parking path. The method includes: geometrically performing an obstacle collision detection in a circular arc path and/or an obstacle collision detection in a straight line path by using a path contour, a vehicle contour and an obstacle contour. The system includes a collision detection module for geometrically performing detection of obstacle collision in a circular arc path and/or detection of obstacle collision in a straight line path by using a path contour, a vehicle contour and an obstacle contour. The apparatus includes: a memory and a processor configured for geometrically performing detection of obstacle collision in a circular arc path and/or detection of obstacle collision in a straight line path by using a path contour, a vehicle contour and an obstacle contour.