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1.发明公开公开(公告)号：US20240143872A1
公开(公告)日：2024-05-02
申请号：US18407170
申请日：2024-01-08
Applicant: BEIJING UNIVERSITY OF TECHNOLOGY
Inventor： Jian TANG , JiaKun Chen , Heng XIA , Junfei QIAO
IPC: G06F30/20 , G06F111/10 , G06F119/08
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.
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2.发明申请公开(公告)号：US20250045489A1
公开(公告)日：2025-02-06
申请号：US18796867
申请日：2024-08-07
Applicant: BEIJING UNIVERSITY OF TECHNOLOGY
Inventor： Xi MENG , Qizheng HOU , Junfei QIAO
Abstract: Provided is an intelligent operational optimization method in the municipal solid waste incineration process, which belongs to both the field of municipal solid waste treatment and the field of intelligent optimization. The method includes: constructing a sample data set by collecting historical data in the municipal solid waste incineration process; with optimization objectives of nitrogen oxide emissions and combustion efficiency, establishing an SORBF neural network-based indicator model to characterize the mapping relationship between operational variables and optimization objectives in the municipal solid waste incineration process; and setting the established indicator model as evaluation functions of a multi-objective optimization algorithm, and obtaining optimal setting values of the operational variables by the multi-objective particle swarm optimization algorithm, so as to improve combustion efficiency while reducing concentrations of nitrogen oxide emissions.
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3.发明公开公开(公告)号：US20230259075A1
公开(公告)日：2023-08-17
申请号：US18136812
申请日：2023-04-19
Applicant: BEIJING UNIVERSITY OF TECHNOLOGY
Inventor： Honggui HAN , Lu ZHANG , Junfei QIAO
CPC classification number: G05B13/0265 , C02F3/302 , G05B13/041
Abstract: A dynamic multi-objective particle swarm optimization based optimal control method is provided to realize the control of dissolved oxygen (SO) and the nitrate nitrogen (SNO) in wastewater treatment process. In this method, dynamic multi-objective particle swarm optimization was used to optimize the operation objectives of WWTP, and the optimal solutions of SO and SNO can be calculated. Then PID controller was introduced to trace the dynamic optimal solutions of SO and SNO. The results demonstrated that the proposed optimal control strategy can address the dynamic optimal control problem, and guarantee the efficient and stable operation. In addition, this proposed optimal control method in this present invention can guarantee the effluent qualities and reduce the energy consumption.
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公开(公告)号：US20220112108A1
公开(公告)日：2022-04-14
申请号：US17392100
申请日：2021-08-02
Applicant: BEIJING UNIVERSITY OF TECHNOLOGY
Inventor： Honggui HAN , Shijia FU , Xiaolong WU , Junfei QIAO
Abstract: A hierarchical model predictive control (HMPC) method based on fuzzy neural network for wastewater treatment process (WWTP) is designed to realize hierarchical control of dissolved oxygen (DO) concentration and nitrate nitrogen concentration. In view of the difference of time scales in WWTP, it is difficult to accurately control the concentration of DO and nitrate nitrogen. The disclosure establishes a HMPC structure according to different time scales. Then, the concentration of DO and nitrate nitrogen is controlled with different frequencies. It not only conforms to the operation characteristics of WWTP, but also solves the problem of poor operation performance of multivariable model predictive control. The experimental results show that the HMPC method can achieve accurate on-line control of DO concentration and nitrate nitrogen concentration with different time scales.
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5.发明申请公开(公告)号：US20220082545A1
公开(公告)日：2022-03-17
申请号：US17472433
申请日：2021-09-10
Applicant: BEIJING UNIVERSITY OF TECHNOLOGY
Inventor： Honggui HAN , Chenxuan SUN , Junfei QIAO
Abstract: 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.
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Patent Agency Ranking
公开(公告)号：US20240419872A1
公开(公告)日：2024-12-19
申请号：US18727294
申请日：2023-04-26
Applicant: Beijing University of Technology
Inventor： Jian TANG , Heng XIA , CanLin CUI , Junfei QIAO
IPC: G06F30/27 , G06F111/10
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.
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公开(公告)号：US20240302341A1
公开(公告)日：2024-09-12
申请号：US18276179
申请日：2022-10-27
Applicant: BEIJING UNIVERSITY OF TECHNOLOGY
Inventor： Jian TANG , Heng XIA , Canlin CUI , Junfei QIAO
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.
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公开(公告)号：US20220027706A1
公开(公告)日：2022-01-27
申请号：US17382237
申请日：2021-07-21
Applicant: BEIJING UNIVERSITY OF TECHNOLOGY
Inventor： Honggui HAN , Zheng LIU , Junfei QIAO
Abstract: An intelligent warning method based on knowledge-fuzzy learning algorithm is designed for membrane fouling with high accuracy. A multi-step prediction strategy, using the least-squares linear regression model, is developed to predict the characteristic variables of membrane fouling Meanwhile, the knowledge of membrane fouling category, which is extracted from the real wastewater treatment process, can be expressed as the form of fuzzy rules. Moreover, a knowledge-based fuzzy neural network is designed to establish the membrane fouling warning model, thus deal with the problem of difficult warning of membrane fouling. The results reveal that the intelligent warning method can improve the ability to solve the membrane fouling, mitigate the deleterious effect on the process performance and ensure the safety operation of the wastewater treatment process.
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9.发明申请公开(公告)号：US20200024168A1
公开(公告)日：2020-01-23
申请号：US16143409
申请日：2018-09-26
Applicant: BEIJING UNIVERSITY OF TECHNOLOGY
Inventor： Honggui HAN , Hongxu LIU , Jiaming LI , Junfei QIAO
Abstract: An intelligent identification method of sludge bulking based on type-2 fuzzy-neural-network belongs to the field of intelligent detection technology. The sludge volume index (SVI) in wastewater treatment plant is an important index to measure the sludge bulking of activated sludge process. However, poor production conditions and serious random interference in sewage treatment process are characterized by strong coupling, large time-varying and serious hysteresis, which makes the detection of SVI concentration of sludge volume index extremely difficult. At the same time, there are many types of sludge bulking faults, which are difficult to identify effectively. Due to the sludge volume index (SVI) is unable to online monitoring and the fault type of sludge bulking is difficult to determined, the invention develop soft-computing model based on type-2 fuzzy-neural-network to complete the real-time detection of sludge volume index (SVI). Combined with the target-related identification algorithm, the fault type of sludge bulking is determined. Results show that the intelligent identification method can quickly obtain the sludge volume index (SVI), accurate identification fault type of sludge bulking, improve the quality and ensure the safety operation of the wastewater treatment process.
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10.发明公开公开(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
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.
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