Skip to main content
SpringerLink
Log in

Event-triggered fuzzy neural multivariable control for a municipal solid waste incineration process

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

Because of coupling, nonlinearity, and uncertainty in a municipal solid waste incineration (MSWI) process, a suitable multivariable controller is difficult to establish under strong disturbance. Additionally, the problems of reducing mechanical wear and energy consumption in the control process should also be considered. To solve these problems, an event-triggered fuzzy neural multivariable controller is proposed in this paper. First, the MSWI object model based on the multiinput multioutput Takagi-Sugeno fuzzy neural network is established using a data-driven method. Second, a fuzzy neural multivariable controller is designed to control the furnace temperature and flue gas oxygen content synchronously under external disturbance. Third, an event-triggered mechanism is constructed to update the control rate online while ensuring control effects. Then, the stability of the proposed control strategy is proven through the Lyapunov II theorem to guide its practical application. Finally, the effectiveness of the controller is verified using the real industrial data of an MSWI factory in Beijing, China. The experimental results show that the proposed control strategy greatly improves the control efficiency, reduces energy consumption by 66.23%, and improves the multivariable tracking control accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Wang P J, Yan F, Xie F, et al. The co-removal of PCDD/Fs by SCR system in a full-scale municipal solid waste incinerator: Migration-transformation and decomposition pathways. Sci China Tech Sci, 2022, 65: 2429–2441

    Article  Google Scholar 

  2. Sun J, Meng X, Qiao J. Prediction of oxygen content using weighted PCA and improved LSTM network in MSWI process. IEEE Trans Instrum Meas, 2021, 70: 1–12

    Google Scholar 

  3. Lu G, Wang Y B, Xu H X, et al. Deep multimodal learning for municipal solid waste sorting. Sci China Tech Sci, 2022, 65: 324–335

    Article  Google Scholar 

  4. Kaza S, Yao L, Bhada-Tata P, et al. What a waste 2.0: A global snapshot of solid waste management to 2050. World Bank Group, 2018

  5. Korai M S, Mahar R B, Uqaili M A. The feasibility of municipal solid waste for energy generation and its existing management practices in Pakistan. Renew Sustain Energy Rev, 2017, 72: 338–353

    Article  Google Scholar 

  6. Wu H F, Zhang B W, Qu W J, et al. Integration of a thermochemical energy system driven by solar energy and biomass for natural gas and power production. Sci China Tech Sci, 2022, 65: 1383–1395

    Article  Google Scholar 

  7. Kalyani K A, Pandey K K. Waste to energy status in India: A short review. Renew Sustain Energy Rev, 2014, 31: 113–120

    Article  Google Scholar 

  8. Chen D Z, Christensen T H. Life-cycle assessment (EASEWASTE) of two municipal solid waste incineration technologies in China. Waste Manag Res, 2010, 28: 508–519

    Article  Google Scholar 

  9. Li J, Xu R, Wang G, et al. Study on the feasibility and co-combustion mechanism of mixed injection of biomass hydrochar and anthracite in blast furnace. Fuel, 2021, 304: 121465

    Article  Google Scholar 

  10. Zhuang J, Tang J, Aljerf L. Comprehensive review on mechanism analysis and numerical simulation of municipal solid waste incineration process based on mechanical grate. Fuel, 2022, 320: 123826

    Article  Google Scholar 

  11. Suntivarakorn R, Treedet W. Improvement of Boiler’s efficiency using heat recovery and automatic combustion control system. Energy Procedia, 2016, 100: 193–197

    Article  Google Scholar 

  12. Calbry-Muzyka A, Madi H, Rüsch-Pfund F, et al. Biogas composition from agricultural sources and organic fraction of municipal solid waste. Renew Energy, 2022, 181: 1000–1007

    Article  Google Scholar 

  13. Sun R, Ismail T M, Ren X, et al. Numerical and experimental studies on effects of moisture content on combustion characteristics of simulated municipal solid wastes in a fixed bed. Waste Manage, 2015, 39: 166–178

    Article  Google Scholar 

  14. Wang H, Gui H, Ren C, et al. Factors influencing urban residents’ intention of garbage sorting in china: An extended TPB by Integrating expectancy theory and norm activation model. Sustainability, 2021, 13: 12985

    Article  Google Scholar 

  15. Ding H X, Tang J, Qiao J F. Control methods of municipal solid wastes incineration process: A survey. In: Proceedings of the 2021 40th Chinese Contr Confer (CCC). Shanghai, IEEE, 2021. 662–667

    Google Scholar 

  16. Meng X, Tang J, Qiao J. NOx emissions prediction with a brain-inspired modular neural network in municipal solid waste incineration processes. IEEE Trans Ind Inf, 2022, 18: 4622–4631

    Article  Google Scholar 

  17. Han Z Z, Huang Y Z, Li J, et al. A hybrid deep neural network based prediction of 300 MW coal-fired boiler combustion operation condition. Sci China Tech Sci, 2021, 64: 2300–2311

    Article  Google Scholar 

  18. Shi Y, Li L, Yang J, et al. Center-based transfer feature learning with classifier adaptation for surface defect recognition. Mech Syst Signal Process, 2023, 188: 110001

    Article  Google Scholar 

  19. Yue C F, Lin T, Zhang X, et al. Hierarchical path planning for multiarm spacecraft with general translational and rotational locomotion mode. Sci China Tech Sci, 2023, 66: 1180–1191

    Article  Google Scholar 

  20. Xie Y B, Wang D, Qiao J F. Dynamic multi-objective intelligent optimal control toward wastewater treatment processes. Sci China Tech Sci, 2022, 65: 569–580

    Article  Google Scholar 

  21. Said Saab S, Hauser M, Ray A, et al. Multivariable nonadaptive controller design. IEEE Trans Ind Electron, 2021, 68: 6181–6191

    Article  Google Scholar 

  22. Zhao D, Li F, Ma R, et al. An unknown input nonlinear observer based fractional order PID control of fuel cell air supply system. IEEE Trans Ind Applicat, 2020, 56: 5523–5532

    Article  Google Scholar 

  23. Wang Y, Wang Z, Zou L, et al. Multiloop decentralized H∞ fuzzy PID-Like control for discrete time-delayed fuzzy systems under dynamical Event-Triggered schemes. IEEE Trans Cybern, 2022, 52: 7931–7943

    Article  Google Scholar 

  24. Çetin G, Özkaraca O, Keçebaş A. Development of PID based control strategy in maximum exergy efficiency of a geothermal power plant. Renew Sustain Energy Rev, 2021, 137: 110623

    Article  Google Scholar 

  25. Ashida Y, Wakitani S, Yamamoto T. Design of an augmented output-based multiloop self-tuning PID control system. Ind Eng Chem Res, 2019, 58: 11474–11484

    Article  Google Scholar 

  26. Arruda L V R, Swiech M C S, Neves-Jr F, et al. Um método evolucionário para sintonia de controladores PI/PID em processos multivariáveis. Sba Controle Automação, 2008, 19: 1–17

    Article  Google Scholar 

  27. Han H G, Zhang J C, Du S L, et al. Robust optimal control for anaerobic-anoxic-oxic reactors. Sci China Tech Sci, 2021, 64: 1485–1499

    Article  Google Scholar 

  28. Han H G, Wang T, Sun H Y, et al. Fuzzy super-twisting sliding mode control for municipal wastewater nitrification process. Sci China Tech Sci, 2022, 65: 2420–2428

    Article  Google Scholar 

  29. Qiao J F, Hou Y, Zhang L, et al. Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation. Neurocomputing, 2018, 275: 383–393

    Article  Google Scholar 

  30. Han H G, Zhang L, Liu H X, et al. Multiobjective design of fuzzy neural network controller for wastewater treatment process. Appl Soft Comput, 2018, 67: 467–478

    Article  Google Scholar 

  31. Dai W, Chai T, Yang S X. Data-driven optimization control for safety operation of hematite grinding process. IEEE Trans Ind Electron, 2015, 62: 2930–2941

    Article  Google Scholar 

  32. Wang B, Huang J, Wen C, et al. Event-triggered model predictive control for power converters. IEEE Trans Ind Electron, 2021, 68: 715–720

    Article  Google Scholar 

  33. Sun Z, Dai L, Xia Y, et al. Event-based model predictive tracking control of nonholonomic systems with coupled input constraint and bounded disturbances. IEEE Trans Automat Contr, 2018, 63: 608–615

    Article  MathSciNet  MATH  Google Scholar 

  34. Du S L, Liu T, Ho D W C. Dynamic event-triggered control for leader-following consensus of multiagent systems. IEEE Trans Syst Man Cybern Syst, 2020, 50: 3243–3251

    Article  Google Scholar 

  35. Gao H, Song Y, Wen C. Event-triggered adaptive neural network controller for uncertain nonlinear system. Inf Sci, 2020, 506: 148–160

    Article  MathSciNet  MATH  Google Scholar 

  36. Wu L B, Park J H, Xie X P, et al. Neural network adaptive tracking control of uncertain MIMO nonlinear systems with output constraints and event-triggered inputs. IEEE Trans Neural Netw Learn Syst, 2021, 32: 695–707

    Article  MathSciNet  Google Scholar 

  37. Li S, Ahn C K, Guo J, et al. Neural-network approximation-based adaptive periodic event-triggered output-feedback control of switched nonlinear systems. IEEE Trans Cybern, 2021, 51: 4011–4020

    Article  Google Scholar 

  38. Malinauskaite J, Jouhara H, Czajczyńska D, et al. Municipal solid waste management and waste-to-energy in the context of a circular economy and energy recycling in Europe. Energy, 2017, 141: 2013–2044

    Article  Google Scholar 

  39. Yan M, Antoni M, Wang J, et al. Numerical investigation of MSW combustion influenced by air preheating in a full-scale moving grate incinerator. Fuel, 2021, 285: 119193

    Article  Google Scholar 

  40. Ding H, Tang J, Qiao J. MIMO modeling and multi-loop control based on neural network for municipal solid waste incineration. Control Eng Pract, 2022, 127: 105280

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    HaiXu Ding, JunFei Qiao, WeiMin Huang & Tao Yu

  2. Beijing Laboratory of Smart Environmental Protection, Beijing, 100124, China

    HaiXu Ding, JunFei Qiao, WeiMin Huang & Tao Yu

  3. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China

    HaiXu Ding, JunFei Qiao, WeiMin Huang & Tao Yu

Authors
  1. HaiXu Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. JunFei Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. WeiMin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tao Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to JunFei Qiao.

Additional information

This work was supported by the Science and Technology Innovation 2030-“New Generation Artificial Intelligence” Major Project of China (Grant No. 2021ZD0112300), the Innovative Research Group Project of the National Natural Science Foundation of China (Grant No. 62021003), the National Natural Science Foundation of China (Grant No. 62073006), and the Natural Science Foundation of Beijing (Grant Nos. 4212032 and 4192009).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, H., Qiao, J., Huang, W. et al. Event-triggered fuzzy neural multivariable control for a municipal solid waste incineration process. Sci. China Technol. Sci. 66, 3115–3128 (2023). https://doi.org/10.1007/s11431-022-2294-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-022-2294-3

Search

Navigation