Simulation and analysis of sea-level change from tide gauge station by using artificial neural network models

Biomass solid waste (BSW) generation in Malaysia is rapidly increasing as a result of nation’s industrialization, urbanization, and population growth. Thermochemical conversion of BSW to produce energy is not straightforward due to fuel’s high moisture content, low heating value, and poor grindability. Accessing different combinatorial scheme of BSW may help to mitigate above-mentioned issues while maintaining attractively high energy outputs. In this work, calorific values and ultimate analyses of a wide variety of BSW reported in literature were compiled. Based on the collected data, two empirical correlations to predict high heating value (HHV) of BSW were developed using a multiple regression method. The developed correlations were (i) HHV = 908.37C + 2942.94H + 4439.73S + 518.92O − 63558.52(municipal solid waste) and (ii) HHV = 382.62C − 368.16H + 2788.24S − 37.83O + 926.26(biomass/biochar) where, C, H, O, N, and S represent biomass content in a form of elemental carbon, hydrogen, oxygen, nitrogen, and sulfur, respectively. The accuracies of the correlations were verified by comparing the predicted values with those experimentally determined. Thermogravimetric analysis was used to analyze BSW combustion behavior and retrieve important combustion parameters. The best-fit correlations obtained in this work had R2 values of 0.98 (MAPE of 3.2%) and 0.92 (MAPE of 7.1%) for municipal solid waste and biomass/biochar samples, respectively. Moreover, the correlations were fairly accurate in predicting HHV of different BSW combination with prediction error of less than 15%. The correlations developed in this work could be instrumental for a precise determination of different combination of solid biomass.

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