Ding, ZiyiChen, ZihongLiu, JingyongEvrendilek, FatihHe, YaoXie, Wuming2023-07-052023-07-052022Ding, Z., Chen, Z., Liu, J., Evrendilek, F., He, Y., & Xie, W. (2022). Co-combustion, life-cycle circularity, and artificial intelligence-based multi-objective optimization of two plastics and textile dyeing sludge. Journal of Hazardous Materials, 426, 128069.0304-38941873-3336http://dx.doi.org/10.1016/j.jhazmat.2021.128069https://hdl.handle.net/20.500.12491/11231his research was financially supported by the National Natural Science Foundation of China (No. 51978175), the Scientific and Technological Planning Project of Guangzhou, China (No. 202103000004), and the Science and Technology Planning Project of Yunfu, Guangdong Province, China (No. 2020040401).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 degrees C. 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 C--O 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(alpha) = (1-alpha)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.eninfo:eu-repo/semantics/closedAccessPolyolefin PlasticsPollution ControlGas EmissionsSewage-SludgeReaction-MechanismWater HyacinthArticle10.1016/j.jhazmat.2021.128069426114349592152-s2.0-85121760144Q1WOS:000752473200004Q1