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Optimization strategy design and simulation of power plant boiler combustion system based on Internet of Things and fuzzy neural network in the context of sustainable development

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Abstract

The annual consumption of coal resources by the combustion process is significant, and the associated environmental degradation has gotten worse. Enhancing the combustion system of power plant boilers to increase energy conversion efficiency and support sustainable development has become a significant research goal in light of this crucial problem. If the combustion process is not properly optimized, it dangerously contaminates the atmospheric environment in addition to wasting coal resources and undermining coal usage. In light of this, this study creates an enhanced fuzzy neural network-based boiler combustion model and a boiler combustion optimization control system that is supported by Internet of Things technology. In order to facilitate fuel mixing in the furnace, the system modifies the amount of coal in each coal feeder depending on the quality of the coal entering the feeder. Lastly, the model's effectiveness is assessed. According to the results, the improved model performs better than the conventional model in terms of computational complexity and generalizability. Boiler combustion rate and NOx emissions are accurately predicted by the method, with errors under 0.84% and 3.6%, respectively.

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References

  • Ahn CK (2010) Delay-dependent state estimation for TS fuzzy delayed Hopfield neural networks. Nonlinear Dyn 61:483–489

    Article  MathSciNet  MATH  Google Scholar 

  • Bhavani NPG, Sujatha K, Ponmagal RS et al (2017) Monitoring of SO2 emissions in power plants using Internet of Things. In: 2017 International conference on energy, communication, data analytics and soft computing (ICECDS), IEEE, pp. 1064–1067

  • Chunlai B, Jingfei Z (2013) Combustion optimization of power plant boiler based on RBF neural network model. Power Gener Equip 27(02):97–100

    Google Scholar 

  • Effendy N, Kurniawan ED, Dwiantoro K et al (2022) The prediction of the oxygen content of the flue gas in a gas-fired boiler system using neural networks and random forest. IAES Int J Artif Intell 11(3):923

    Google Scholar 

  • Fang L (2011) Boiler combustion optimization based on neural network and genetic algorithm, North China Electric Power University, Baoding

  • Jiangtao F (2015) The influence of boiler heating surface transformation on boiler desuperheating water volume. Shandong Ind Technol 04:31–31

    Google Scholar 

  • Karthick SA, Sakthivel R, Ma YK et al (2019) Disturbance rejection of fractional-order TS fuzzy neural networks based on quantized dynamic output feedback controller. Appl Math Comput 361:846–857

    MathSciNet  MATH  Google Scholar 

  • Li N, Hu Y (2020) The deep convolutional neural network for NOx emission prediction of a coal-fired boiler. IEEE Access 8:85912–85922

    Article  Google Scholar 

  • Liu L, Guo X, Lee C (2021) Promoting smart cities into the 5G era with multi-field Internet of Things (IoT) applications powered with advanced mechanical energy harvesters. Nano Energy 88:106304

    Article  Google Scholar 

  • Meiru Z (2021a) Application of distributed regional protection technology in coal mine power supply system. Shaanxi Coal 40:142–146

    Google Scholar 

  • Meiru Z (2021b) Application of distributed regional protection technology in coal mine power supply system. Shaanxi Coal 40(01):142–146

    Google Scholar 

  • Shi K, Wang J, Zhong S et al (2020) Non-fragile memory filtering of TS fuzzy delayed neural networks based on switched fuzzy sampled-data control. Fuzzy Sets Syst 394:40–64

    Article  MathSciNet  MATH  Google Scholar 

  • Song J, Romero CE, Yao Z et al (2016) Improved artificial bee colony-based optimization of boiler combustion considering NOX emissions, heat rate and fly ash recycling for on-line applications. Fuel 172:20–28

    Article  Google Scholar 

  • Sujatha K, Bhavani NPG, Reddy TK et al (2017) Internet of Things for flame monitoring power station boilers. In: 2017 Trends in industrial measurement and automation (TIMA), IEEE, pp. 1–7

  • Sun (2012) Modeling of boiler combustion process based on Bayesian network and fuzzy neural network. North China Electric Power University, Beijing

  • Wei R (2020) Self-powered wireless sensors with dual-modality sensing for Internet of Things (IoT) applications, Case Western Reserve University, Cleveland

  • Xiang He, Dafu Ma, Jing J (2021) Combustion optimization of swirl opposed firing boiler for reducing CO emission. J Power Eng 1(07):33–41

    Google Scholar 

  • Xiaolan W, Shasha C (2021) Research on the application of Internet of Things technology in abnormal fault monitoring of coal mine equipment. Energy Environ Prot 43(07):182–188

    Google Scholar 

  • Xiaoqin Z (2021) Attack detection model of SCADA system based on data preprocessing and improved ELM. J Nanjing Univ Aeronaut Astronaut 53(05):708–717

    Google Scholar 

  • Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control Eng 8(01):22–34

    Article  Google Scholar 

  • Yan F (2021) Combustion optimization modeling of power plant boiler based on constrained support vector regression. Therm Power Eng 36(11):26–132

    Google Scholar 

  • Yang G, Wang Y, Li X (2020) Prediction of the NOx emissions from thermal power plant using long-short term memory neural network. Energy 192:116597

    Article  Google Scholar 

  • Ze D, Ning Ma, Lei M (2019) Model improvement for boiler NOx emission based on DEQPSO algorithm. J Chin Soc Power Eng 39(03):191–197

    Google Scholar 

  • Zeng G, Xu M, Tu Y et al (2020) Influences of initial coal concentration on ignition behaviors of low-NOx bias combustion technology. Appl Energy 278:115745

    Article  Google Scholar 

  • Zhang H, Qiu M, Yu X et al (2022) Application of boiler optimization monitoring system based on embedded Internet of Things. Math Probl Eng 2022:1–12

    Google Scholar 

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Authors and Affiliations

  1. School of Energy and Environment, Southeast University, Nanjing, 210096, China

    Zhiqiang Xu

  2. School of Applied Technolgy and Mangagment, Liaoning Technical University, Fuxin, 123000, China

    Cong Wu

  3. Institute for Optimization and Decision Analytics, Liaoning Technical University, FuXin, 123000, China

    Cong Wu

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  1. Zhiqiang Xu
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  2. Cong Wu
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Correspondence to Cong Wu.

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Xu, Z., Wu, C. Optimization strategy design and simulation of power plant boiler combustion system based on Internet of Things and fuzzy neural network in the context of sustainable development. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08478-1

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