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学者姓名：汤健
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Abstract ：
Precise control of furnace temperature (FT) is crucial for the stable, efficient operation and pollution control of the municipal solid waste incineration (MSWI) process. To address the inherent nonlinearity and uncertainty of the incineration process, a FT control strategy is proposed. Firstly, by analyzing the process characteristics of the MSWI process in terms of FT control, the secondary air flow is selected as the manipulated variable to control the FT. Secondly, an FT prediction model based on the Interval Type-2 Fuzzy Broad Learning System (IT2FBLS) is developed, incorporating online parameter learning and structural learning algorithms to enhance prediction accuracy. Next, particle swarm rolling optimization (PSRO) is used to solve the optimal control law sequence to ensure optimization efficiency. Finally, the stability of the proposed method is validated using Lyapunov theory, confirming the controller's reliability in practical applications. Experiments based on actual operational data confirm the method's effectiveness.
Keyword ：
municipal solid waste incineration (MSWI) municipal solid waste incineration (MSWI) furnace temperature (FT) furnace temperature (FT) model predictive control (MPC) model predictive control (MPC) particle swarm rolling optimization (PSRO) particle swarm rolling optimization (PSRO) interval type-2 fuzzy broad learning system (IT2FBLS) interval type-2 fuzzy broad learning system (IT2FBLS)
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| GB/T 7714 | Tian, Hao , Tang, Jian , Wang, Tianzheng . Furnace Temperature Model Predictive Control Based on Particle Swarm Rolling Optimization for Municipal Solid Waste Incineration [J]. | SUSTAINABILITY , 2024 , 16 (17) . |
| MLA | Tian, Hao 等. "Furnace Temperature Model Predictive Control Based on Particle Swarm Rolling Optimization for Municipal Solid Waste Incineration" . | SUSTAINABILITY 16 . 17 (2024) . |
| APA | Tian, Hao , Tang, Jian , Wang, Tianzheng . Furnace Temperature Model Predictive Control Based on Particle Swarm Rolling Optimization for Municipal Solid Waste Incineration . | SUSTAINABILITY , 2024 , 16 (17) . |
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Abstract ：
Municipal solid waste incineration (MSWI) is essential for tackling urban environmental challenges and facilitating renewable energy recycling. The MSWI process has characteristics of multiple variables, strong coupling, and complex nonlinearity, requiring advanced process control (APC) technology. Although there have been several reviews on the modeling and control of the MSWI process, there is a lack of focus on model predictive control (MPC), a widely used APC technology. This article aims to comprehensively review MPC strategies in the MSWI process. First, it describes MSWI process technology in detail, examining control issues and objectives to highlight the complexity and challenges in controller design while providing an overview of MPC methods and their benefits. Second, it reviews incinerator modeling for control, including traditional modeling techniques and machine learning technologies such as fuzzy neural networks. Third, it reviews the controllers used for MSWI process, emphasizing the advantages of MPC over existing control methods. Fourth, it discusses the current status of MPC design and online updates, covering the need for an accurate dynamic predictive model and objective function and the online updates components such as predictive modeling, rolling optimization, and feedback correction. Finally, the study concludes with a summary of the findings.
Keyword ：
machine learning machine learning advanced process control advanced process control municipal solid waste incineration municipal solid waste incineration model predictive control model predictive control intelligent optimization algorithms intelligent optimization algorithms
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| GB/T 7714 | Tang, Jian , Tian, Hao , Wang, Tianzheng . A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process [J]. | SUSTAINABILITY , 2024 , 16 (17) . |
| MLA | Tang, Jian 等. "A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process" . | SUSTAINABILITY 16 . 17 (2024) . |
| APA | Tang, Jian , Tian, Hao , Wang, Tianzheng . A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process . | SUSTAINABILITY , 2024 , 16 (17) . |
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Abstract ：
Theoretical research has explained the process of dioxin (DXN) formation in the municipal solid waste incineration (MSWI). This process includes the generation, adsorption, and emission of DXN. Actual DXN concentrations often significantly deviate from theoretical models. This discrepancy is influenced by several key factors: the type of integrated municipal solid waste (MSW) treatment process, the characteristics of the waste, and the operational controls. The progression of DXN generation, adsorption, and emission concentrations within the MSWI process remains unclear. This lack of clarity is especially pronounced when examining the accounting for the specific components of the MSW. To unravel the evolution of DXN, this article proposes a comprehensive numerical simulation model for the entire process of DXN concentration in an MSWI plant. The model is designed based on existing knowledge of MSW combustion and DXN mechanisms, leveraging FLIC and ASPEN simulation software. It incorporates six key stages to facilitate the DXN simulation: precipitation and formation, high- temperature pyrolysis, high-temperature gas-phase synthesis, low-temperature catalytic synthesis, adsorption on activated carbon, and emission to the atmosphere. Under both benchmark and multiple operating conditions, the simulated experiments confirm the effective representation of the evolution of DXN concentrationsthroughout the process. Consequently, this study presents a model designed to enhance the development of strategies aimed at reducing DXN emissions and to foster innovation in intelligent control technologies.
Keyword ：
Municipal solid waste incineration (MSWI) Municipal solid waste incineration (MSWI) Mechanism model Mechanism model Numerical simulation model Numerical simulation model adsorption and emission adsorption and emission Generation Generation Dioxins (DXN) Dioxins (DXN)
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| GB/T 7714 | Xia, Heng , Tang, Jian , Aljerf, Loai et al. Unveiling dioxin dynamics: A whole-process simulation study of municipal solid waste incineration [J]. | SCIENCE OF THE TOTAL ENVIRONMENT , 2024 , 954 . |
| MLA | Xia, Heng et al. "Unveiling dioxin dynamics: A whole-process simulation study of municipal solid waste incineration" . | SCIENCE OF THE TOTAL ENVIRONMENT 954 (2024) . |
| APA | Xia, Heng , Tang, Jian , Aljerf, Loai , Chen, Jiakun . Unveiling dioxin dynamics: A whole-process simulation study of municipal solid waste incineration . | SCIENCE OF THE TOTAL ENVIRONMENT , 2024 , 954 . |
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Abstract ：
燃烧线是表征城市固废焚烧(municipal solid waste incineration,MSWI)过程燃烧稳定性的关键参数之一.完备的火焰图像模板库是实现燃烧线量化以从检测视角代替依靠运行专家"人工看火",进而通过实时反馈提升MSWI过程控制的智能化水平的基础.针对燃烧线极端异常火焰图像缺失问题,该文提出基于机理知识和对抗网络的燃烧线极端异常火焰图像生成方法.首先,基于焚烧炉内三维空间位置到图像像素点的机理映射关系分析燃烧线极端异常火焰图像,通过对正常火焰图像像素点的平移、拼接和组合等方式获取伪标记的燃烧线极端异常火焰图像;然后,采用循环生成 对 抗 网 络(cycle generative adversarial networks,CycleGAN)获得符合真实火焰图像分布的候选图像;最后,提出组合基于弗雷歇距离(Fréchet inception distance,FID)评估最优模型参数和根据伪标记筛选最终燃烧线极端异常火焰图像的 2 级评估与筛选策略.针对北京某MSWI厂的实验结果表明:依据燃烧线可划分图像为51%～73.6%正常、47%～51%和 73.6%～100%异常、0%～47%极端异常;当第 2级评估阈值设定为0.4时,所提方法生成合格极端异常火焰图像的比例为85.7%,优于传统评估方法.
Keyword ：
燃烧线量化 燃烧线量化 燃烧线极端异常火焰图像 燃烧线极端异常火焰图像 城市固废焚烧 城市固废焚烧 循环生成对抗网络 循环生成对抗网络 图像评估与选择 图像评估与选择
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| GB/T 7714 | 郭海涛 , 汤健 , 夏恒 et al. 城市固废焚烧过程燃烧线极端异常火焰图像对抗生成 [J]. | 中国电机工程学报 , 2024 , 44 (11) : 4376-4386,中插16 . |
| MLA | 郭海涛 et al. "城市固废焚烧过程燃烧线极端异常火焰图像对抗生成" . | 中国电机工程学报 44 . 11 (2024) : 4376-4386,中插16 . |
| APA | 郭海涛 , 汤健 , 夏恒 , 乔俊飞 . 城市固废焚烧过程燃烧线极端异常火焰图像对抗生成 . | 中国电机工程学报 , 2024 , 44 (11) , 4376-4386,中插16 . |
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Abstract ：
Numerous investigations have shown that the municipal solid waste incineration (MSWI) has become one of the major sources of dioxin (DXN) emissions. Currently, the primary issue that needs to be addressed for DXN emission reduction control is the online measurement of DXN. Data-driven AI algorithms enable real-time DXN concentration measurement, facilitating its control. However, researchers mainly focus on building models for DXN emissions at the stack. This approach does not allow for the construction of models that online measurement of DXN generation and absorption throughout the whole process. To achieve optimal pollution control, models that encompass the whole process are necessary, not just models focused on the stack. Therefore, this article focuses on modeling the whole process of DXN concentrations, including generation, adsorption, and emission. It uses machine learning techniques based on advanced tree-based data-driven deep and broad learning algorithms. The determination of data characteristics at different phases is grounded in the understanding of the DXN mechanism, offering a novel framework for DXN modeling. State-of-the-art tree-based models, including adaptive deep forest regression algorithm based on cross layer full connection, tree broad learning system, fuzzy forest regression, and aid modeling technologies, are applied to handle diverse data characteristics. These characteristics encompass high-dimensional small samples, low-dimensional ultra-small size samples, and medium- dimensional small samples across different phases related to DXN. The most interesting is the robust validation where the proposed a whole process tree-based model for DXN is validated using nearly one year of authentic data on DXN generation, adsorption, and emission phases in an MSWI plant of Beijing. The proposed modeling framework can be used to explore the mechanism characterization and support the pollution reduction optimal control.
Keyword ：
Generation, adsorption and emission phases Generation, adsorption and emission phases AI-based model AI-based model Municipal solid waste incineration (MSWI) Municipal solid waste incineration (MSWI) Pollution emission modeling Pollution emission modeling Whole process dioxins (DXN) concentration Whole process dioxins (DXN) concentration Tree-based deep/broad learning Tree-based deep/broad learning
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| GB/T 7714 | Xia, Heng , Tang, Jian , Aljerf, Loai et al. AI-based tree modeling for multi-point dioxin concentrations in municipal solid waste incineration [J]. | JOURNAL OF HAZARDOUS MATERIALS , 2024 , 480 . |
| MLA | Xia, Heng et al. "AI-based tree modeling for multi-point dioxin concentrations in municipal solid waste incineration" . | JOURNAL OF HAZARDOUS MATERIALS 480 (2024) . |
| APA | Xia, Heng , Tang, Jian , Aljerf, Loai , Wang, Tianzheng , Gao, Bingyin , Alajlani, Muaaz . AI-based tree modeling for multi-point dioxin concentrations in municipal solid waste incineration . | JOURNAL OF HAZARDOUS MATERIALS , 2024 , 480 . |
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Abstract ：
受限于检测技术难度、高时间与经济成本等原因,难测参数的软测量模型建模样本存在数量少、分布稀疏与不平衡等问题,严重制约了数据驱动模型的泛化性能.针对以上问题,提出一种基于多目标粒子群优化(Multi-objective particle swarm optimization,MOPSO)混合优化的虚拟样本生成(Virtual sample generation,VSG)方法.首先,设计综合学习粒子群优化算法的种群表征机制,使其能够同时编码用于连续变量和离散变量;然后,定义具有多阶段多目标特性的综合学习粒子群优化算法适应度函数,使其能够在确保模型泛化性能的同时最小化虚拟样本数量;最后,提出面向虚拟样本生成的多目标混合优化任务以改进综合学习粒子群优化算法,使其能够适应虚拟样本优选过程的变维特性并提高收敛速度.同时,首次借鉴度量学习提出用于评价虚拟样本质量的综合评价指标和分布相似指标.利用基准数据集和真实工业数据集验证了所提方法的有效性和优越性.
Keyword ：
小样本建模 小样本建模 分布相似度 分布相似度 虚拟样本生成 虚拟样本生成 混合优化 混合优化 多目标粒子群优化 多目标粒子群优化
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| GB/T 7714 | 王丹丹 , 汤健 , 夏恒 et al. 基于多目标PSO混合优化的虚拟样本生成 [J]. | 自动化学报 , 2024 , 50 (4) : 790-811 . |
| MLA | 王丹丹 et al. "基于多目标PSO混合优化的虚拟样本生成" . | 自动化学报 50 . 4 (2024) : 790-811 . |
| APA | 王丹丹 , 汤健 , 夏恒 , 乔俊飞 . 基于多目标PSO混合优化的虚拟样本生成 . | 自动化学报 , 2024 , 50 (4) , 790-811 . |
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Abstract ：
用于复杂工业过程难测运行指标和异常故障建模的样本具有量少稀缺、分布不平衡以及内涵机理知识匮乏等特性.虚拟样本生成(Virtual sample generation,VSG)作为扩充建模样本数量及其涵盖空间的技术,已成为解决上述问题的主要手段之一,但已有研究还存在缺乏理论支撑、分类准则与应用边界模糊等问题.本文在描述复杂工业过程难测运行指标和异常故障建模所存在问题的基础上,梳理虚拟样本定义及其内涵,给出面向工业过程回归与分类问题的VSG实现流程;接着,从样本覆盖区域、实现流程与推广应用等方向进行综述;然后,分析讨论VSG的下一步研究方向;最后,对全文进行总结并给出未来挑战.
Keyword ：
复杂工业过程 复杂工业过程 虚拟样本生成 虚拟样本生成 数据驱动建模 数据驱动建模 样本覆盖区域 样本覆盖区域
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| GB/T 7714 | 汤健 , 崔璨麟 , 夏恒 et al. 面向复杂工业过程的虚拟样本生成综述 [J]. | 自动化学报 , 2024 , 50 (4) : 688-718 . |
| MLA | 汤健 et al. "面向复杂工业过程的虚拟样本生成综述" . | 自动化学报 50 . 4 (2024) : 688-718 . |
| APA | 汤健 , 崔璨麟 , 夏恒 , 乔俊飞 . 面向复杂工业过程的虚拟样本生成综述 . | 自动化学报 , 2024 , 50 (4) , 688-718 . |
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Abstract ：
The municipal solid waste incineration (MSWI) process has become the primary technology for municipal solid waste (MSW) treatment worldwide due to its advantages of harmlessness, reduction, and resource recovery. However, the automatic combustion control (ACC) system developed in developed countries are often not effectively applicable to other countries, particularly China, due to regional differences in MSW composition. To achieve intelligent development of MSWI plants, it is crucial to develop intelligent optimal control technology tailored to national conditions to reduce comprehensive pollutant emission concentration (CPEC) and improve combustion efficiency (CE). This article proposes a data-driven multi-objective intelligent optimal control strategy. Firstly, based on the analysis of the whole process, the Tikhonov regularization-least regression decision tree (TR-LRDT) algorithm is used to establish a MSWI whole process model, incorporating serial controlled objects and parallel pollutant indicators, to support a multi-objective optimization model. Then, leveraging the ACC system and expert experience, a multi-input multi-output loop controller is established using the single neuron adaptive PID (SNA-PID) algorithm to achieve stable control of key controlled variables. Next, a mutation crossover strategy and termination condition are integrated with the multi-objective particle swarm optimization (MOPSO) algorithm to solve the multi-objective optimization model, and domain expert rules are applied to determine the optimal setpoints for key controlled variables in the Pareto front, aiming to reduce CPEC and improve CE. Finally, the effectiveness of the proposed intelligent optimal control strategy is validated using actual data. The experimental results demonstrate that this strategy not only maintains the stability of key controlled variables but also decreases CPEC by 1.82% and increases CE by 2.38%, laying a foundation for further research into intelligent optimization control of the MSWI process. Further, the software system based on the proposed strategy is developed and validated on a hardware-in-loop simulation platform.
Keyword ：
Municipal solid waste incineration (MSWI) Municipal solid waste incineration (MSWI) Multi-objective particle swarm optimization Multi-objective particle swarm optimization Tikhonov regularization-least regression deci- Tikhonov regularization-least regression deci- Single neuron adaptive PID Single neuron adaptive PID sion tree (TR-LRDT) sion tree (TR-LRDT) Intelligent optimal control Intelligent optimal control (MOPSO) (MOPSO)
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| GB/T 7714 | Wang, Tianzheng , Tang, Jian , Xia, Heng et al. Data-driven multi-objective intelligent optimal control of municipal solid waste incineration process [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 137 . |
| MLA | Wang, Tianzheng et al. "Data-driven multi-objective intelligent optimal control of municipal solid waste incineration process" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 137 (2024) . |
| APA | Wang, Tianzheng , Tang, Jian , Xia, Heng , Yang, Cuili , Yu, Wen , Qiao, Junfei . Data-driven multi-objective intelligent optimal control of municipal solid waste incineration process . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 137 . |
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Abstract ：
城市固废焚烧（MSWI）过程产生的二英（DXN）是至今机理仍复杂不清的剧毒污染物，获悉DXN在炉排炉内的生成、燃烧和再生成等过程的边界条件对降低污染排放极为重要。对此，本文提出了城市固废炉排炉焚烧过程DXN排放浓度数值仿真方法。首先，依据面向DXN的典型炉排炉MSWI工艺流程，描述焚烧炉内固相燃烧、气相燃烧、高温换热和低温换热等与DXN相关反应的机理。接着，依据上述所划分区域，结合实际MSWI过程相关参数构建DXN数值仿真模型。最后，基于烟气分流分率所表征的反应物浓度和不同区域的反应温度进行单因素分析，以获取G1处DXN浓度的边界条件，并基于正交实验分析分流分率和反应温度对G1处DXN浓度的影响，进而获得最优参数组合。基于北京某MSWI电厂实际数据的数值仿真分析与验证，表明了该数值仿真模型的有效性，为后续优化控制G1处的DXN排放浓度提供了支撑。
Keyword ：
城市固废焚烧 城市固废焚烧 正交实验 正交实验 最优参数 最优参数 二英 二英 单因素分析 单因素分析 数值仿真 数值仿真
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| GB/T 7714 | 陈佳昆 , 汤健 , 夏恒 et al. 城市固废炉排炉焚烧过程二英排放浓度数值仿真 [J]. | 化工进展 , 2023 , 42 (02) : 1061-1072 . |
| MLA | 陈佳昆 et al. "城市固废炉排炉焚烧过程二英排放浓度数值仿真" . | 化工进展 42 . 02 (2023) : 1061-1072 . |
| APA | 陈佳昆 , 汤健 , 夏恒 , 乔俊飞 . 城市固废炉排炉焚烧过程二英排放浓度数值仿真 . | 化工进展 , 2023 , 42 (02) , 1061-1072 . |
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Abstract ：
The prevailing method for handling municipal solid waste (MSW) is incineration, a critical process that demands safe, stable, and eco-conscious operation. While grate-typed furnaces offer operational flexibility, they often generate pollution during unstable operating conditions. Moreover, fluctuations in the physical and chemical characteristics of MSW contribute to variable combustion statuses, accelerating internal furnace wear and ash accumulation. Tackling the challenges of pollution, wear, and efficiency in the MSW incineration (MSWI) process necessitates the automatic online recognition of combustion status. This article introduces a novel online recognition method using deep forest classification (DFC) based on convolutional multi-layer feature fusion. The method entails several key steps: initial collection and analysis of flame image modeling data and construction of an offline model utilizing LeNet-5 and DFC. Here, LeNet-5 trains to extract deep features from flame images, while an adaptive selection fusion method on multi-layer features selects the most effective fused deep features. Subsequently, these fused deep features feed into DFC, constructing an offline recognition model for identifying combustion status. Finally, embedding this recognition system into an existing MSWI process data monitoring system enables online flame video recognition. Experimental results show remarkable accuracies: 93.80% and 95.08% for left and right grate furnace offline samples, respectively. When implemented in an online flame video recognition platform, it aptly meets recognition demands.
Keyword ：
LeNet-5 network LeNet-5 network deep forest classification deep forest classification municipal solid waste incineration municipal solid waste incineration online flame video identification online flame video identification combustion status combustion status
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| GB/T 7714 | Pan, Xiaotong , Tang, Jian , Xia, Heng et al. Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion [J]. | SUSTAINABILITY , 2023 , 15 (23) . |
| MLA | Pan, Xiaotong et al. "Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion" . | SUSTAINABILITY 15 . 23 (2023) . |
| APA | Pan, Xiaotong , Tang, Jian , Xia, Heng , Wang, Tianzheng . Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion . | SUSTAINABILITY , 2023 , 15 (23) . |
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