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Investigating the co-combustion characteristics of oily sludge and ginkgo leaves through thermogravimetric analysis coupled with an artificial neural network

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Abstract

The co-combustion characteristics of oily sludge and ginkgo leaves (GL) in an oxy-fuel atmosphere are investigated via thermogravimetric analysis coupled with an artificial neural network. The combustion characteristics of blends improve as the GL mass ratio increases. The interaction indices used to evaluate the interaction between the two solid combustibles present a complex nonlinear relationship in different stages. The Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose methods are used to calculate the activation energy of the blends, which increases with an increase in the oxygen concentration, in different atmospheres. Compared with the radial basis function, the backpropagation neural network performs better in predicting the combustion curve of the blends.

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References

  1. Li J, Lin F, Xiang L, et al. Hazardous elements flow during pyrolysis of oily sludge. J Hazard Mater, 2021, 409: 124986

    Article  Google Scholar 

  2. Bao D, Li Z, Liu X, et al. Biochar derived from pyrolysis of oily sludge waste: Structural characteristics and electrochemical properties. J Environ Manage, 2020, 268: 110734

    Article  Google Scholar 

  3. Cheng S, Wang Y, Gao N, et al. Pyrolysis of oil sludge with oil sludge ash additive employing a stirred tank reactor. J Anal Appl Pyrolysis, 2016, 120: 511–520

    Article  Google Scholar 

  4. Xie C, Liu J, Buyukada M, et al. Parametric assessment of stochastic variability in co-combustion of textile dyeing sludge and shaddock peel. Waste Manage, 2019, 96: 128–135

    Article  Google Scholar 

  5. Yang R, Ma C, Chen G, et al. Study on NOx emission during corn straw/sewage sludge co-combustion: Experiments and modelling. Fuel, 2021, 285: 119208

    Article  Google Scholar 

  6. Deng S, Wang X, Tan H, et al. Thermogravimetric study on the Co-combustion characteristics of oily sludge with plant biomass. ThermoChim Acta, 2016, 633: 69–76

    Article  Google Scholar 

  7. Lin Y, Ma X, Ning X, et al. TGA-FTIR analysis of co-combustion characteristics of paper sludge and oil-palm solid wastes. Energy Convers Manage, 2015, 89: 727–734

    Article  Google Scholar 

  8. Xie C, Liu J, Zhang X, et al. Co-combustion thermal conversion characteristics of textile dyeing sludge and pomelo peel using TGA and artificial neural networks. Appl Energy, 2018, 212: 786–795

    Article  Google Scholar 

  9. Lu K M, Lee W J, Chen W H, et al. Thermogravimetric analysis and kinetics of co-pyrolysis of raw/torrefied wood and coal blends. Appl Energy, 2013, 105: 57–65

    Article  Google Scholar 

  10. Xiao R, Yang W, Cong X, et al. Thermogravimetric analysis and reaction kinetics of lignocellulosic biomass pyrolysis. Energy, 2020, 201: 117537

    Article  Google Scholar 

  11. Zhu X, Yu S, Xu K, et al. Sustainable activated carbons from dead ginkgo leaves for supercapacitor electrode active materials. Chem Eng Sci, 2018, 181: 36–45

    Article  Google Scholar 

  12. Panahi A, Sirumalla S K, West R H, et al. Temperature and oxygen partial pressure dependencies of the coal-bound nitrogen to NOx conversion in O2/CO2 environments. Combust Flame, 2019, 206: 98–111

    Article  Google Scholar 

  13. Tu Y, Xu M, Zhou D, et al. CFD and kinetic modelling study of methane MILD combustion in O2/N2, O2/CO2 and O2/H2O atmospheres. Appl Energy, 2019, 240: 1003–1013

    Article  Google Scholar 

  14. Luo J, Zou C, He Y, et al. The characteristics and mechanism of NO formation during pyridine oxidation in O2/N2 and O2/CO2 atmospheres. Energy, 2019, 187: 115954

    Article  Google Scholar 

  15. Yi B, Zhang L, Huang F, et al. Effect of H2O on the combustion characteristics of pulverized coal in O2/CO2 atmosphere. Appl Energy, 2014, 132: 349–357

    Article  Google Scholar 

  16. Liu Y, Cheng J, Zou C, et al. Ignition delay times of ethane under O2/CO2 atmosphere at different pressures by shock tube and simulation methods. Combust Flame, 2019, 204: 380–390

    Article  Google Scholar 

  17. Lai Z Y, Ma X Q, Tang Y T, et al. A study on municipal solid waste (MSW) combustion in N2/O2 and CO2/O2 atmosphere from the perspective of TGA. Energy, 2011, 36: 819–824

    Article  Google Scholar 

  18. Chen J, Xie C, Liu J, et al. Co-combustion of sewage sludge and coffee grounds under increased O2/CO2 atmospheres: Thermodynamic characteristics, kinetics and artificial neural network modeling. Bioresource Tech, 2018, 250: 230–238

    Article  Google Scholar 

  19. Qiu C, Yi Y K, Wang M, et al. Coupling an artificial neuron network daylighting model and building energy simulation for vacuum photovoltaic glazing. Appl Energy, 2020, 263: 114624

    Article  Google Scholar 

  20. Yang R, Xiong R, Ma S, et al. Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks. Appl Energy, 2020, 260: 114253

    Article  Google Scholar 

  21. Yang S, Wan M P, Chen W, et al. Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control. Appl Energy, 2021, 288: 116648

    Article  Google Scholar 

  22. Li C. Designing a short-term load forecasting model in the urban smart grid system. Appl Energy, 2020, 266: 114850

    Article  Google Scholar 

  23. Han Y, Fan C, Geng Z, et al. Energy efficient building envelope using novel RBF neural network integrated affinity propagation. Energy, 2020, 209: 118414

    Article  Google Scholar 

  24. Zhang T, Zhang D, Yan H, et al. A new method of data missing estimation with FNN-based tensor heterogeneous ensemble learning for internet of vehicle. Neurocomputing, 2021, 420: 98–110

    Article  Google Scholar 

  25. Chen J, Liu J, He Y, et al. Investigation of co-combustion characteristics of sewage sludge and coffee grounds mixtures using thermogravimetric analysis coupled to artificial neural networks modeling. Bioresource Tech, 2017, 225: 234–245

    Article  Google Scholar 

  26. Hai A, Bharath G, Daud M, et al. Valorization of groundnut shell via pyrolysis: Product distribution, thermodynamic analysis, kinetic estimation, and artificial neural network modeling. Chemosphere, 2021, 283: 131162

    Article  Google Scholar 

  27. Buyukada M, Aydogmus E. Utilization of apricot seed in (co-)combustion of lignite coal blends: Numeric optimization, empirical modeling and uncertainty estimation. Fuel, 2018, 216: 190–198

    Article  Google Scholar 

  28. Xie W, Wen S, Liu J, et al. Comparative thermogravimetric analyses of co-combustion of textile dyeing sludge and sugarcane bagasse in carbon dioxide/oxygen and nitrogen/oxygen atmospheres: Thermal conversion characteristics, kinetics, and thermodynamics. Bioresource Tech, 2018, 255: 88–95

    Article  Google Scholar 

  29. Mundike J, Collard F X, Görgens J F. Co-combustion characteristics of coal with invasive alien plant chars prepared by torrefaction or slow pyrolysis. Fuel, 2018, 225: 62–70

    Article  Google Scholar 

  30. Li X, Miao W, Lv Y, et al. TGA-FTIR investigation on the co-combustion characteristics of heavy oil fly ash and municipal sewage sludge. ThermoChim Acta, 2018, 666: 1–9

    Article  Google Scholar 

  31. Deng S, Tan H, Wang X, et al. Investigation on the fast co-pyrolysis of sewage sludge with biomass and the combustion reactivity of residual char. Bioresource Tech, 2017, 239: 302–310

    Article  Google Scholar 

  32. Wang Q, Zhao W, Liu H, et al. Interactions and kinetic analysis of oil shale semi-coke with cornstalk during co-combustion. Appl Energy, 2011, 88: 2080–2087

    Article  Google Scholar 

  33. Das P, Mondal D, Maiti S. Thermochemical conversion pathways of Kappaphycus alvarezii granules through study of kinetic models. Bioresource Tech, 2017, 234: 233–242

    Article  Google Scholar 

  34. Li J, Qiao Y, Zong P, et al. Fast pyrolysis characteristics of two typical coastal zone biomass fuels by thermal gravimetric analyzer and down tube reactor. Bioresource Tech, 2019, 283: 96–105

    Article  Google Scholar 

  35. Ahmad R, Mohd Ishak M A, Kasim N N, et al. Properties and thermal analysis of upgraded palm kernel shell and Mukah Balingian coal. Energy, 2019, 167: 538–547

    Article  Google Scholar 

  36. Niu S, Yu H, Zhao S, et al. Apparent kinetic and thermodynamic calculation for thermal degradation of stearic acid and its esterification derivants through thermogravimetric analysis. Renew Energy, 2019, 133: 373–381

    Article  Google Scholar 

  37. Yıldız Z, Uzun H, Ceylan S, et al. Application of artificial neural networks to co-combustion of hazelnut husk-lignite coal blends. Bioresource Tech, 2016, 200: 42–47

    Article  Google Scholar 

  38. Huang J, Xiao Q, Liu J, et al. Modeling heat transfer properties in an ORC direct contact evaporator using RBF neural network combined with EMD. Energy, 2019, 173: 306–316

    Article  Google Scholar 

  39. Xie Z, Ma X. The thermal behaviour of the co-combustion between paper sludge and rice straw. Bioresource Tech, 2013, 146: 611–618

    Article  Google Scholar 

  40. Tahmasebi A, Kassim M A, Yu J, et al. Thermogravimetric study of the combustion of Tetraselmis suecica microalgae and its blend with a Victorian brown coal in O2/N2 and O2/CO2 atmospheres. Bioresource Tech, 2013, 150: 15–27

    Article  Google Scholar 

  41. Liu C, Liu J, Sun G, et al. Thermogravimetric analysis of (co-)combustion of oily sludge and litchi peels: Combustion characterization, interactions and kinetics. ThermoChim Acta, 2018, 667: 207–218

    Article  Google Scholar 

  42. Chen W H, Wang C W, Ong H C, et al. Torrefaction, pyrolysis and two-stage thermodegradation of hemicellulose, cellulose and lignin. Fuel, 2019, 258: 116168

    Article  Google Scholar 

  43. Senneca O, Cerciello F, Heuer S, et al. Slow pyrolysis of walnut shells in nitrogen and carbon dioxide. Fuel, 2018, 225: 419–425

    Article  Google Scholar 

  44. Gao Y, Tahmasebi A, Dou J, et al. Combustion characteristics and air pollutant formation during oxy-fuel co-combustion of microalgae and lignite. Bioresource Tech, 2016, 207: 276–284

    Article  Google Scholar 

  45. Zhao R, Qin J, Chen T, et al. Experimental study on co-combustion of low rank coal semicoke and oil sludge by TG-FTIR. Waste Manage, 2020, 116: 91–99

    Article  Google Scholar 

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Authors and Affiliations

  1. Shandong Engineering Laboratory for High-efficiency Energy Conservation and Energy Storage Technology & Equipment, School of Energy and Powering, Shandong University, Jinan, 250061, China

    ShuChen Li, ShengLi Niu, KuiHua Han, YingJie Li, YongZheng Wang & ChunMei Lu

Authors
  1. ShuChen Li
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  2. ShengLi Niu
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  3. KuiHua Han
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  4. YingJie Li
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  5. YongZheng Wang
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  6. ChunMei Lu
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Corresponding author

Correspondence to ShengLi Niu.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant No. 51876106), the Primary Research & Development Plan of Shandong Province, China (Grant No. 2018GGX104027), and the Young Scholars Program of Shandong University (Grant No. 2015WLJH33).

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Li, S., Niu, S., Han, K. et al. Investigating the co-combustion characteristics of oily sludge and ginkgo leaves through thermogravimetric analysis coupled with an artificial neural network. Sci. China Technol. Sci. 65, 261–271 (2022). https://doi.org/10.1007/s11431-021-1959-0

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  • DOI: https://doi.org/10.1007/s11431-021-1959-0

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