Literature DB >> 34959215

Co-combustion, life-cycle circularity, and artificial intelligence-based multi-objective optimization of two plastics and textile dyeing sludge.

Ziyi Ding1, Zihong Chen1, Jingyong Liu2, Fatih Evrendilek3, Yao He1, Wuming Xie1.   

Abstract

Given the globally abundant availability of waste plastics and the negative environmental impacts of textile dyeing sludge (TDS), their co-combustion can effectively enhance the circular economies, energy recovery, and environmental pollution control. The (co-)combustion performances, gas emissions, and ashes of TDS and two plastics of polypropylene (PP) and polyethylene (PE) were quantified and characterized. The increased blend ratio of PP and PE improved the ignition, burnout, and comprehensive combustion indices. The two plastics interacted with TDS significantly in the range of 200-600 ℃. TDS pre-ignited the combustion of the plastics which in turn promoted the combustion of TDS. The co-combustions released more CO2 but less CH4, C-H, and CO as CO2 was less persistent than the others in the atmosphere. The Ca-based minerals in the plastics enhanced S-fixation and reduced SO2 emission. The activation energy of the co-combustion fell from 126.78 to 111.85 kJ/mol and 133.71-79.91 kJ/mol when the PE and PP additions rose from 10% to 50%, respectively. The co-combustion reaction mechanism was best described by the model of f(α) = (1-α)n. The reaction order was reduced with the additions of the plastics. The co-combustion operation interactions were optimized via an artificial neural network so as to jointly meet the multiple objectives of maximum energy production and minimum emissions.
Copyright © 2021 Elsevier B.V. All rights reserved.

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Keywords:  Ash components; Energy recovery; Gas emissions; Pollution control; Polyolefin plastics

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Year:  2021        PMID: 34959215     DOI: 10.1016/j.jhazmat.2021.128069

Source DB:  PubMed          Journal:  J Hazard Mater        ISSN: 0304-3894            Impact factor:   10.588


  1 in total

1.  Thermal degradation of hazardous 3-layered COVID-19 face mask through pyrolysis: Kinetic, thermodynamic, prediction modelling using ANN and volatile product characterization.

Authors:  Ahmad Nawaz; Pradeep Kumar
Journal:  J Taiwan Inst Chem Eng       Date:  2022-09-28       Impact factor: 5.477

  1 in total

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