Investigation of thermal conversion characteristics and performance evaluation of co-combustion of pine sawdust and lignite coal using TGA, artificial neural network modeling and likelihood method
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(Co-)combustion of pine sawdust (PS) and lignite coal (LC) were investigated using artificial neural networks (ANN), particle swarm optimization (PSO), and Monte Carlo simulation (MC) as a function of blend ratio, heating rate, and temperature via thermal conversion characteristics. The order of degraded compounds in terms of hemi-cellulosic and lignin-based compounds demonstrated the main oxidation and degradation mechanism of co-combustion of PS and LC. The best prediction (R-2 of 99.99%) was obtained by ANN28 model. Operating conditions of 90LC10PS, 425 degrees C, and 19 degrees C min(-1) were determined by PSO as optimum levels with TG value of 67.5%. Once three-replicated validation experiments were performed under PSO-optimized conditions, mean TG values ware observed as 67.5% with a standard deviation of +/- 0.4%. Consequently, MC was used to identify the stochastic variability and uncertainty associated with ANN models that were derived to predict TG values.