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Title Thermogravimetric study of textile lime sludge and cement raw meal for co-processing as alternative raw material for cement production using response surface methodology and neural networks
ID_Doc 25846
Authors Prabhakaran, SPS; Swaminathan, G
Title Thermogravimetric study of textile lime sludge and cement raw meal for co-processing as alternative raw material for cement production using response surface methodology and neural networks
Year 2022
Published
Abstract In the textile industry-water treatment process, lime sludge is generated and usually piled up in nearby barren lands to avoid transportation costs. However, the textile sludge could have disposal value as an alternative raw material in cement kilns. Advanced analytical techniques were employed to investigate the raw material property of textile sludge for cement production. Fourier Transform Infrared(FTIR) Spectroscopy Analysis and Thermo-gravimetric hyphenated FTIR analysis revealed that textile sludge was predominantly inorganic and gas emissions mainly were CO2. Sludge was fused at calcination temperatures, and the phase transition was compared to the Hot Meal (HM) formed from the raw meal. Inductively coupled plasma optical emission spectrometry(ICP-OES) revealed that textile sludge is rich in metal oxides such as 45% CaO, 11% SiO2, 0.44% Al2O3, 8.57% Fe2O3, 7.85% MgO, respectively, which are essential raw materials for the cement manufacturing process. The thermal degradation kinetics were computed under non-isothermal conditions (30-900 degrees C) at 10, 15, 20 degrees Cimin. Response surface regression techniques identified the effect of heating rate and temperature on mass loss. Regression Coefficient of the predictive models were above 0.90. The prediction accuracy increased by Artificial neural network using MSE as performance function and TRANSIG as transfer function. (C) 2021 The Authors. Published by Elsevier B.V.
PDF https://doi.org/10.1016/j.eti.2021.102100

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9588 Wen, ST; Zou, HH; Liu, JY; Evrendilek, DE; Yan, YP; Liang, GJ Multi-response optimization toward efficient and clean (co-)combustions of textile dyeing sludge and second-generation feedstock(2021)
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