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

• Published in: Bioresource technology Vol. 287; p. 121461
• Main Author: Buyukada, Musa
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.09.2019
• Subjects: algorithms; Artificial intelligence; Co-combustion; combustion; lignite; Monte Carlo method; neural networks; Numeric optimization; Pine sawdust; prediction; sawdust; standard deviation; temperature; uncertainty; Uncertainty estimation; Pine sawdust; Co-combustion; Numeric optimization; Uncertainty estimation; Artificial intelligence
• ISSN: 0960-8524; 1873-2976; 1873-2976
• DOI: 10.1016/j.biortech.2019.121461

•Thermal behaviors of lignite coal and pine sawdust were investigated.•TG values of co-combustion process were predicted by ANN.•PSO was utilized in optimization of input parameters of co-combustion process.•Monte Carlo was used in identification of stochastic variability and uncertainty. (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 (R2 of 99.99%) was obtained by ANN28 model. Operating conditions of 90LC10PS, 425 °C, and 19 °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.