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学者姓名：汤健
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摘要 ：
The prediction of appliances energy consumption in building belongs to time series forecasting problem, which can be solved by echo state network (ESN). However, due to the randomly initialized inputs and reservoir, some redundant or irrelevant components are inevitably generated in original ESN. To solve this problem, the adaptive sparse deep echo state network (ASDESN) is proposed, in which the information is processed layer by layer. Firstly, the principal component analysis (PCA) layer is inserted to penalize the redundant projection transmitted between sub-reservoirs. Secondly, the coordinate descent based adaptive sparse learning method is proposed to generate the sparse output weights. Particularly, the designed adaptive threshold strategy is able to enlarge the sparsity of output weights as network depth increases. Moreover, the echo state property (ESP) of ASDESN is given to ensure its applications. The experiment results in both simulated benchmark and real appliances energy datasets illustrate that the proposed ASDESN outperforms other ESNs with higher prediction accuracy and stability.
关键词 ：
sparse learning sparse learning hierarchical structure hierarchical structure Echo state network Echo state network appliances energy consumption prediction appliances energy consumption prediction
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| GB/T 7714 | Yang, Cuili , Yang, Sheng , Tang, Jian et al. Design and Application of Adaptive Sparse Deep Echo State Network [J]. | IEEE TRANSACTIONS ON CONSUMER ELECTRONICS , 2024 , 70 (1) : 3582-3592 . |
| MLA | Yang, Cuili et al. "Design and Application of Adaptive Sparse Deep Echo State Network" . | IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 70 . 1 (2024) : 3582-3592 . |
| APA | Yang, Cuili , Yang, Sheng , Tang, Jian , Li, Bing . Design and Application of Adaptive Sparse Deep Echo State Network . | IEEE TRANSACTIONS ON CONSUMER ELECTRONICS , 2024 , 70 (1) , 3582-3592 . |
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摘要 ：
Municipal solid waste incineration (MSWI) is an effective method for waste to energy in the developed and developing countries. However, it also produces multiple flue gas pollutants such as NOx, SO2, HCl, CO, and CO2. Due to differences in MSW components, seasonal and regional factors, the operational control mode and pollution emission level in developed and developing countries are different. In China, manual operational mode is usually used. To reduce emission concentrations of multiple flue gas pollutants often resort to injecting large quantities of cleaning material such as urea, lime water and activated carbon without optimizing the manipulated variable value. Our objective is to obtain the optimal "air and material distribution" values in terms of minimizing pollution emissions and to replace the empirical given values with the manual control mode. An optimization method for multiple flue gas pollutants emission reduction is proposed. Firstly, based on the experience of domain experts, the pollution model inputs dominated by manipulated variables are determined. Then, considering the attributes of various flue gas pollutants, a novel hierarchical incremental learning strategy for the interval type-2 fuzzy broad learning system is devised to establish a multi-input multi-output model. Finally, a new fuzzy adaptive particle swarm optimization (FAPSO) algorithm, incorporating the elite particle splitting (EPS) strategy, i.e., EPS-FAPSO, is introduced to determine the optimal values for primary/secondary air volume. By using a relatively stable operating condition data from an MSWI power plant in Beijing, the effectiveness of the proposed method is validated. And a software system is developed and realized on a hardware-inloop simulation platform, laying a foundation for industrial application.
关键词 ：
Municipal solid waste incineration Municipal solid waste incineration Interval type-2 fuzzy adaptive particle swarm optimization Interval type-2 fuzzy adaptive particle swarm optimization Hardware-in-loop simulation platform Hardware-in-loop simulation platform Broad learning system Broad learning system Emission reduction optimization Emission reduction optimization Multiple flue gas pollutants Multiple flue gas pollutants
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| GB/T 7714 | Wang, Tianzheng , Tang, Jian , Aljerf, Loai et al. Emission reduction optimization of multiple flue gas pollutants in Municipal solid waste incineration power plant [J]. | FUEL , 2024 , 381 . |
| MLA | Wang, Tianzheng et al. "Emission reduction optimization of multiple flue gas pollutants in Municipal solid waste incineration power plant" . | FUEL 381 (2024) . |
| APA | Wang, Tianzheng , Tang, Jian , Aljerf, Loai , Qiao, Junfei , Alajlani, Muaaz . Emission reduction optimization of multiple flue gas pollutants in Municipal solid waste incineration power plant . | FUEL , 2024 , 381 . |
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摘要 ：
Furnace temperature (FT) is a key variable in municipal solid waste incineration (MSWI) processes, influenced by many manipulated variables and directly impacting pollutant concentrations in exhaust gas. Domain experts cannot achieve the optimal FT setpoint value under manual control, leading to abnormal pollutant emission concentrations. To address this, we propose an intelligent optimal control framework for FT aiming to minimize pollutant emission concentration. First, the FT controlled object model is established using the Tikhonov regularization-least regression decision tree (TR-LRDT) algorithm. Then, based on the experience of domain experts, a multi-loop controller is developed using an improved single neuron adaptive PID (ISNA-PID) algorithm to stabilize FT. Next, after establishing NOx and CO2 indicator models, the particle swarm optimization (PSO) algorithm is employed to determine the FT setpoint value in terms of minimum pollutant emission concentration. Finally, the FT intelligent optimal control framework is verified. Experimental results indicate that the optimal FT setpoint value can reduce NOx and CO2 emission concentrations by 19.93 % and 6.99 %, respectively.
关键词 ：
sion tree (TR-LRDT) sion tree (TR-LRDT) Intelligent optimal control Intelligent optimal control Single neuron adaptive PID (SNA-PID) Single neuron adaptive PID (SNA-PID) Municipal solid waste incineration (MSWI) Municipal solid waste incineration (MSWI) Pollution emission reduction Pollution emission reduction Particle swarm optimization (PSO) Particle swarm optimization (PSO) controller controller Tikhonov regularization-least regression deci- Tikhonov regularization-least regression deci- Furnace temperature (FT) Furnace temperature (FT)
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| GB/T 7714 | Wang, Tianzheng , Tang, Jian , Xia, Heng et al. Intelligent optimal control of furnace temperature for the municipal solid waste incineration process using multi-loop controller and particle swarm optimization [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 257 . |
| MLA | Wang, Tianzheng et al. "Intelligent optimal control of furnace temperature for the municipal solid waste incineration process using multi-loop controller and particle swarm optimization" . | EXPERT SYSTEMS WITH APPLICATIONS 257 (2024) . |
| APA | Wang, Tianzheng , Tang, Jian , Xia, Heng , Aljerf, Loai , Zhang, Runyu , Tian, Hao et al. Intelligent optimal control of furnace temperature for the municipal solid waste incineration process using multi-loop controller and particle swarm optimization . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 257 . |
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摘要 ：
Municipal solid waste incineration (MSWI) is the most widely used waste treatment technology worldwide. Dioxin (DXN), one of the by-products of the MSWI process, is by far the most toxic contaminant. Labeled samples are extremely limited for constructing its soft sensor measurement model because offline DXN detection takes considerable amount of time and cost. In addition, the number of pseudo-label samples and the optimization of hyperparameters in semi-supervised models is a challenging problem. A multi-objective particle swarm optimization (PSO) semi-supervised random forest (RF) algorithm is proposed in this paper for DXN emission concentration measurement. First, the coding design of the selected hyperparameter value and pseudo-labeled samples is realized for the semi-supervised algorithm oriented to hybrid optimization. Subsequently, the particles are initialized and decoded to evaluate the fitness of the model-oriented generalization performance and the number of pseudo-labeled samples. The termination condition of optimization is then assessed. If the condition is unsatisfied, then the decision variable of multi-objective PSO is updated. Otherwise, the Pareto solution set is used to determine the optimal solution. Finally, the RF model is constructed on the basis of optimal mixed samples. The effectiveness of the proposed method is verified by using benchmark and actual MSWI process datasets.
关键词 ：
Dioxin (DXN) Dioxin (DXN) Semi-supervised learning Semi-supervised learning Pseudo-labeled samples Pseudo-labeled samples Random forest Random forest Multi-objective particle swarm optimization Multi-objective particle swarm optimization Municipal solid waste incineration (MSWI) Municipal solid waste incineration (MSWI)
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| GB/T 7714 | Xu, Wen , Tang, Jian , Xia, Heng et al. Multi-objective PSO semi-supervised random forest method for dioxin soft sensor [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 135 . |
| MLA | Xu, Wen et al. "Multi-objective PSO semi-supervised random forest method for dioxin soft sensor" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 135 (2024) . |
| APA | Xu, Wen , Tang, Jian , Xia, Heng , Yu, Wen , Qiao, Junfei . Multi-objective PSO semi-supervised random forest method for dioxin soft sensor . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 135 . |
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摘要 ：
Carbon monoxide (CO) is a toxic gas emitted during municipal solid waste incineration (MSWI). Its emission prediction is conducive to pollutant reduction and optimized control of MSWI. The variables of MSWI exhibit redundant and interdependent correlations with CO emissions. Furthermore, the mapping relationship is difficult to characterize. Therefore, the work proposed a CO emission prediction method based on reduced depth features and long short-term memory (LSTM) optimization. The particle design for reduced depth feature and LSTM optimization was initially developed-incorporating an adaptive threshold range for feature selection based on the inherent characteristics of modeling data. Secondly, the nonlinear depth features were extracted using ultra-one-dimensional convolution and subsequently fed into an LSTM model for prediction construction. The hyperparameters of the convolutional layer and LSTM were updated based on the loss function. The generalization performance of the model was used as the fitness function of the optimization. Finally, the particle swarm optimization (PSO) was used to adaptively reduce depth features and model's hyperparameters. The rationality and effectiveness of the proposed method were validated using the benchmark dataset and CO dataset of MSWI. R2 of the testing datasets for RB and CO were 0.9097 +/- 3.64E-04 and 0.7636 +/- 3.19E-03, respectively, by repeating 30 times.
关键词 ：
Particle swarm optimization (PSO) Particle swarm optimization (PSO) Reduced depth features Reduced depth features Long short-term memory (LSTM) Long short-term memory (LSTM) Municipal solid waste incineration (MSWI) Municipal solid waste incineration (MSWI) Emission concentration of carbon monoxide Emission concentration of carbon monoxide
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| GB/T 7714 | Zhang, Runyu , Tang, Jian , Xia, Heng et al. CO emission predictions in municipal solid waste incineration based on reduced depth features and long short-term memory optimization [J]. | NEURAL COMPUTING & APPLICATIONS , 2024 . |
| MLA | Zhang, Runyu et al. "CO emission predictions in municipal solid waste incineration based on reduced depth features and long short-term memory optimization" . | NEURAL COMPUTING & APPLICATIONS (2024) . |
| APA | Zhang, Runyu , Tang, Jian , Xia, Heng , Pan, Xiaotong , Yu, Wen , Qiao, Junfei . CO emission predictions in municipal solid waste incineration based on reduced depth features and long short-term memory optimization . | NEURAL COMPUTING & APPLICATIONS , 2024 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Carbon monoxide (CO) is a hazardous gas discharged during municipal solid waste incineration (MSWI). Its emission concentration serves as a vital indicator for assessing the stability of the MSWI process. Therefore, accurate prediction of CO emissions is crucial. While existing research predominantly relies on historical real data -driven models, it often overlooks the effective utilization of the combustion mechanism. This article introduced a novel approach: a heterogeneous ensemble prediction model that integrates virtual and real data. Firstly, virtual mechanism data was obtained through a multi -condition mechanism model constructed using coupled numerical simulation software of FLIC and Aspen Plus. Secondly, based on this virtual mechanism data, a linear regression decision tree (LRDT) algorithm was employed to establish the mechanism mapping model. Simultaneously, a real historical data -driven model based on a long short-term memory (LSTM) neural network algorithm was developed. In the offline training verification phase, the heterogeneous models were combined using an inequality -constrained random weighted neural network (CIRWNN) after aligning virtual and real samples representing operating conditions based on the k -nearest neighbor (KNN) approach. Subsequently, in the online testing verification stage, CO online prediction was achieved by ensemble the LRDT-based mechanism mapping model and. the LSTM-based historical data -driven model. The proposed method's effectiveness and rationality were validated through an industrial case study of MSWI process in Beijing.
关键词 ：
Municipal solid waste incineration (MSWI) Municipal solid waste incineration (MSWI) Heterogeneous ensemble model Heterogeneous ensemble model Hybrid -drive Hybrid -drive Virtual mechanism data Virtual mechanism data Carbon monoxide (CO) Carbon monoxide (CO) Real historical data Real historical data Coupled numerical simulation Coupled numerical simulation
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| GB/T 7714 | Zhang, Runyu , Tang, Jian , Xia, Heng et al. Heterogeneous ensemble prediction model of CO emission concentration in municipal solid waste incineration process using virtual data and real data hybrid-driven [J]. | JOURNAL OF CLEANER PRODUCTION , 2024 , 445 . |
| MLA | Zhang, Runyu et al. "Heterogeneous ensemble prediction model of CO emission concentration in municipal solid waste incineration process using virtual data and real data hybrid-driven" . | JOURNAL OF CLEANER PRODUCTION 445 (2024) . |
| APA | Zhang, Runyu , Tang, Jian , Xia, Heng , Chen, Jiakun , Yu, Wen , Qiao, Junfei . Heterogeneous ensemble prediction model of CO emission concentration in municipal solid waste incineration process using virtual data and real data hybrid-driven . | JOURNAL OF CLEANER PRODUCTION , 2024 , 445 . |
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摘要 ：
Artificial intelligence (AI) has found widespread application across diverse domains, including residential life and product manufacturing. Municipal solid waste incineration (MSWI) represents a significant avenue for realizing waste-to-energy (WTE) objectives, emphasizing resource reuse and sustainability. Theoretically, AI holds the potential to facilitate optimal control of the MSWI process in terms of achieving minimal pollution emissions and maximal energy efficiency. However, a noticeable shortage exists in the current research of the review literature concerning AI in the field of WTE, particularly MSWI, hindering a focused understanding of future development directions. Consequently, this study conducts an exhaustive survey of AI applications for optimal control, categorizing them into four fundamental aspects: modeling, control, optimization, and maintenance. Timeline diagrams depicting the evolution of AI technologies in the MSWI process are presented to offer an intuitive visual representation. Each category undergoes meticulous classification and description, elucidating the shortcomings and challenges inherent in current research. Furthermore, the study articulates the future development trajectory of AI applications within the four fundamental categories, underscoring the contribution it makes to the field of MSWI and WTE.
关键词 ：
maintenance maintenance optimal control optimal control municipal solid waste incineration municipal solid waste incineration artificial intelligence artificial intelligence optimization optimization modeling modeling control control
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| GB/T 7714 | Tang, Jian , Wang, Tianzheng , Xia, Heng et al. An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process [J]. | SUSTAINABILITY , 2024 , 16 (5) . |
| MLA | Tang, Jian et al. "An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process" . | SUSTAINABILITY 16 . 5 (2024) . |
| APA | Tang, Jian , Wang, Tianzheng , Xia, Heng , Cui, Canlin . An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process . | SUSTAINABILITY , 2024 , 16 (5) . |
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摘要 ：
Establishing an accurate model of dynamic systems poses a challenge for complex industrial processes. Due to the ability to handle complex tasks, modular neural networks (MNN) have been widely applied to industrial process modeling. However, the phenomenon of domain drift caused by operating conditions may lead to a cold start of the model, which affects the performance of MNN. For this reason, a multisource transfer learning-based MNN (MSTL-MNN) is proposed in this study. First, the knowledge-driven transfer learning process is performed with domain similarity evaluation, knowledge extraction, and fusion, aiming to form an initial subnetwork in the target domain. Then, the positive transfer process of effective knowledge can avoid the cold start problem of MNN. Second, during the data-driven fine-tuning process, a regularized self-organizing long short-term memory algorithm is designed to fine-tune the structure and parameters of the initial subnetwork, which can improve the prediction performance of MNN. Meanwhile, relevant theoretical analysis is given to ensure the feasibility of MSTL-MNN. Finally, the effectiveness of the proposed method is confirmed by two benchmark simulations and a real industrial dataset of a municipal solid waste incineration process. Experimental results demonstrate the merits of MSTL-MNN for industrial applications.
关键词 ：
Computational modeling Computational modeling Dynamic system Dynamic system Task analysis Task analysis multisource transfer learning multisource transfer learning Mathematical models Mathematical models modular neural network (MNN) modular neural network (MNN) Multi-layer neural network Multi-layer neural network Prediction algorithms Prediction algorithms Dynamical systems Dynamical systems Neurons Neurons long short-term memory (LSTM) long short-term memory (LSTM)
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| GB/T 7714 | Duan, Haoshan , Meng, Xi , Tang, Jian et al. Dynamic System Modeling Using a Multisource Transfer Learning-Based Modular Neural Network for Industrial Application [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (5) : 7173-7182 . |
| MLA | Duan, Haoshan et al. "Dynamic System Modeling Using a Multisource Transfer Learning-Based Modular Neural Network for Industrial Application" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 5 (2024) : 7173-7182 . |
| APA | Duan, Haoshan , Meng, Xi , Tang, Jian , Qiao, Junfei . Dynamic System Modeling Using a Multisource Transfer Learning-Based Modular Neural Network for Industrial Application . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (5) , 7173-7182 . |
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摘要 ：
本发明提供了一种面向颗粒物生成的城市固废焚烧3D数值建模分析方法，该方法首先从实际设计图纸获取焚烧厂信息后构建焚烧炉与余热锅炉的3D模型，结合机理和经验确定影响颗粒物生成的关键因素为固体MSW燃烧温度、壁面颗粒碰撞方式和第二挡板长度，接着描述3D模型的求解方法，然后对所述关键因素基于3D模型进行单因素分析，最后基于正交实验分析所述关键因素对出口处颗粒物浓度的影响，获得最佳参数组合。本发明提供的面向颗粒物生成的城市固废焚烧3D数值建模分析方法，能够实现城市固废燃烧3D模型搭建，能够针对颗粒物进行数值建模分析，进而研究影响颗粒物浓度特性的因素。
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| GB/T 7714 | 汤健 , 梁永琪 , 夏恒 et al. 面向颗粒物生成的城市固废焚烧3D数值建模分析方法 : CN202310620061.7[P]. | 2023-05-29 . |
| MLA | 汤健 et al. "面向颗粒物生成的城市固废焚烧3D数值建模分析方法" : CN202310620061.7. | 2023-05-29 . |
| APA | 汤健 , 梁永琪 , 夏恒 , 梁文林 , 乔俊飞 . 面向颗粒物生成的城市固废焚烧3D数值建模分析方法 : CN202310620061.7. | 2023-05-29 . |
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摘要 ：
本发明提供了一种MSWI过程CO排放预测方法，该方法首先进行面向约简深度特征和LSTM优化的粒子设计，能够依据建模数据特点自适应确定特征选择阈值范围，其次将采用超一维卷积进行非线性特征提取后的深度特征输入至LSTM以构建预测模型，基于损失函数对卷积层和LSTM超参数进行更新，以模型的泛化性能作为优化算法的适应度函数，最后采用粒子群优化(PSO)算法依据数据特性进行自适应的特征和超参数选择。本发明提供的MSWI过程CO排放预测方法，能够基于约简深度特征和LSTM优化实现MSWI过程中的CO排放预测。
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| GB/T 7714 | 汤健 , 张润雨 , 夏恒 et al. 一种MSWI过程CO排放预测方法 : CN202310620063.6[P]. | 2023-05-29 . |
| MLA | 汤健 et al. "一种MSWI过程CO排放预测方法" : CN202310620063.6. | 2023-05-29 . |
| APA | 汤健 , 张润雨 , 夏恒 , 杜胜利 , 乔俊飞 . 一种MSWI过程CO排放预测方法 : CN202310620063.6. | 2023-05-29 . |
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