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Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network for Furnace Temperature Control
The furnace temperature (FT) control is the key for ensuring the stable operation and effective pollution reduction in municipal solid waste incineration (MSWI) processes. However, conventional control strategies encounter challenges in effectively managing FT due to uncertainties associated with material composition, feeding modes, and equipment maintenance. In response to these challenges, this ...Show More
Conservative Q-Learning Based Furnace Temperature Control in Municipal Solid Waste Incineration Process
Hao Tian;Jian Tang;Heng Xia;Jian Zhang;Tianzheng Wang;Wen Yu
2024 6th International Conference on Industrial Artificial Intelligence (IAI)
Year: 2024 | Conference Paper |
The nonlinear, time-varying, and lagging nature of the municipal solid waste incineration (MSWI) process presents challenges for ensuring controller safety. While offline reinforcement learning (RL) can ensure safety in furnace temperature (FT) control, its performance is hindered by extrapolation errors, making it unsuitable for direct application in the incineration environment. To address this,...Show More
Interval Type-2 Fuzzy Neural Network Control for Furnace Temperature in Municipal Solid Waste Incineration Processes
2024 36th Chinese Control and Decision Conference (CCDC)
Year: 2024 | Conference Paper |
Furnace temperature plays a pivotal role in ensuring the stability of municipal solid waste incineration (MSWI), influenced by variable factors such as material composition, feeding patterns, and equipment operation. We introduce an interval type-2 fuzzy neural network (IT2FNN) control method. Initially, we analyze furnace temperature control characteristics to identity crucial operating variables...Show More
An Improved Hierarchical Fused Fuzzy Deep Neural Network Method for Controlled Object Model of Furnace Temperature for MSWI Process
Hao Tian;Jian Tang;Heng Xia;Tianzheng Yang;Aijun Yan;Zhiwei Wu
2024 36th Chinese Control and Decision Conference (CCDC)
Year: 2024 | Conference Paper |
Addressing the challenge of precisely controlling furnace temperature, this study introduces a modeling strategy based on an enhanced hierarchical fused fuzzy deep neural network (HF-FDNN) model. Initially, an analysis of the control characteristics of furnace temperature identifies key manipulated variables (MVs). Subsequently, we refine the HF-FDNN algorithm within the task-driven layer to devel...Show More
Adaptive PID Control of Furnace Temperature in MSWI Process Based on Reinforcement Learning
2024 36th Chinese Control and Decision Conference (CCDC)
Year: 2024 | Conference Paper |
This paper tackles the challenge of effectively regulating furnace temperature in the municipal solid waste incineration (MSWI) process. It proposes an approach that integrates reinforcement learning with traditional PID control. Firstly, an analysis of the characteristics of furnace temperature control is conducted to identify the key manipulated variables affecting it. Subsequently, a control st...Show More
Furnace Temperature Control for MSWI Process Based on Neural Network Predictive Controller
Bokang Wang;Jian Tang;Heng Xia;Hao Tian;Tianzheng Wang;Zhiwei Wu
2024 36th Chinese Control and Decision Conference (CCDC)
Year: 2024 | Conference Paper |
To tackle the challenge of controlling furnace temperature in municipal solid waste incineration (MSWI), which is critical for incineration efficiency and pollutant reduction, this paper introduces a model predictive control (MPC) strategy utilizing a back propagation neural network (BPNN). Traditional PID struggles with the nonlinearity and disturbances of process. Our approach involves construct...Show More
Construction of flame image classification criteria and reference database for municipal solid waste incineration process
Xiaotong Pan;Jian Tang;Heng Xia;Hao Tian;Tianzheng Wang;Wen Xu
2023 35th Chinese Control and Decision Conference (CCDC)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
The flame combustion state inside the furnace is important feedback information for intelligent optimization control of the municipal solid waste incineration (MSWI) process. However, none of the existing studies on the identification of MSWI combustion state have proposed a combustion state classification criteria, which with actual physical significance and interpretability for the incineration ...Show More
Interpretable controlled object model of furnace temperature for MSWI process based on a novle linear regression decision tree
Heng Xia;Jian Tang;Tianzheng Wang;Hao Tian;Canlin Cui;Wen Xu
2023 35th Chinese Control and Decision Conference (CCDC)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
The furnace temperature is one of the most critical controlled variables in the municipal solid waste incineration (MSWI) process. The primary challenge of intelligent optimal control is to construct a high-precision and interpretable controlled object model in terms of furnace temperature. To address this problem, this article proposes a novel modeling method, i.e., linear regression decision tre...Show More
Furnace Temperature Control based on Adaptive TS-FNN for Municipal Solid Waste Incineration Process
Hao Tian;Jian Tang;Heng Xia;Tianzheng Wang;Canlin Cui;Xiaotong Pan
2023 35th Chinese Control and Decision Conference (CCDC)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
In the process of municipal solid waste incineration (MSWI), the stability of furnace temperature determines the running safety and pollution emission level. The furnace temperature is influenced by different uncertainty fluctuations and strong interference. This makes it difficult to effectively control using the PID algorithm. Aiming at the above problems, an adaptive TS fuzzy neural network (FN...Show More
Combustion State Identification of Municipal Solid Waste Incineration Process Based on VGG19 Depth Feature Migration
Hao Tian;Jian Tang;Xiaotong Pan;Heng Xia;Tianzheng Wang;Zixuan Wang
2023 35th Chinese Control and Decision Conference (CCDC)
Year: 2023 | Conference Paper |
The combustion stability of solid waste in the incinerator determines the operating efficiency and pollutant emission concentration of the municipal solid waste incineration (MSWI) process. At present, domain experts identity the combustion state and manually control the MSWI process has the problem of unstable identification results and low intelligence. A combustion state recognition method for ...Show More
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