QI Pengyuan, YAO Xiwen, LIU Qinghua, et al. Prediction of the characteristic fusion temperature of co-combustion ash of biomass and bituminous coal using multiple linear regression model[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2024, 40(15): 174-182. DOI: 10.11975/j.issn.1002-6819.202404179
    Citation: QI Pengyuan, YAO Xiwen, LIU Qinghua, et al. Prediction of the characteristic fusion temperature of co-combustion ash of biomass and bituminous coal using multiple linear regression model[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2024, 40(15): 174-182. DOI: 10.11975/j.issn.1002-6819.202404179

    Prediction of the characteristic fusion temperature of co-combustion ash of biomass and bituminous coal using multiple linear regression model

    • A large amount of biomass wastes have been generated during agricultural production nowadays. The combustion and gasification can be expected to convert the biomass into clean energy, such as gas, electricity, and heat. Among them, the characteristic temperature of ash fusion is one of the important indexes for the tendency of slagging. Since the biomass ash is a mixture of a variety of minerals, there is no fixed melting point within a certain temperature range. In general, ash fusion temperatures are commonly characterized by four characteristic temperatures, namely the deformation, softening, hemisphere, and flow temperature. Few studies have been conducted to predict the ash fusion temperature of biomass and coal co-combustion ash. Most of them only considered the role of alkaline components in the ash, particularly without the influence of acidic components (SO3 and P2O5). This investigation aims to fully consider the effects of acidic/alkaline components in ash on the ash fusion temperature. Seven oxides were taken as the variables, including Al2O3, SiO2, P2O5, SO3, K2O, CaO, and Fe2O3 in biomass and coal co-combustion ash. A prediction model was established for the ash fusion temperature of biomass and coal co-combustion ash using a multiple linear regression model in the Matlab software. Besides, taking the corn straw and Shenmu bituminous coal in rural areas of China as examples, the ash composition and ash fusion temperatures were measured under different mixing ratios, temperatures, and residence times. The results showed that the content of MgO, K2O, CaO, Na2O, Fe2O3, and MnO oxides increased with the increase of the content of corn straw from 25% to 75%. Especially for K2O, the content increased from 5.89% to 14.41%. The content of acidic oxides decreased gradually, such as Al2O3, P2O5, and SO3, among which the content of Al2O3 decreased from 12.05% to 7.78%, the content of P2O5 decreased from 3.66% to 1.07%, and the content of SO3 decreased from 7.70% to 1.48%. The Cl content decreased outstandingly with the increase of ashing temperature and the extension of residence time. A comparison was made between the prediction and the experimental values, according to the existing empirical formula. It was found that the influence coefficients of P2O5 and SO3 on the characteristic ash fusion temperature were large in the prediction model, which was consistent with the test. It inferred that there was a significant effect of acidic components, such as P2O5 and SO3, on the ash fusion temperatures. Actually, the ash fusion temperature was positively correlated with the content of oxides, such as Al2O3, SiO2, P2O5, and CaO, indicating a great contribution to the inhibition of ash melting and slagging. There was an error of less than 5% in the characteristic ash fusion temperatures between the prediction of the model and the experiment, indicating the better fitting of the multiple linear regression. The accuracy and reliability of the model were verified as well.
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