Utilizing machine learning in predicting yields of products in biomass thermochemical conversion processes
Résumé
emissions goal. In the era of biorefineries, and especially for new types of waste and new waste blends, a feasibility study is required to identify the optimum waste conversion process which is usually preceded by experimental investigation. Especially for developing countries, securing the corresponding funds is usually challenging. In this research work, machine learning has been applied to develop a statistical model based on 144 samples of the published experimental data for different types of wastes undergoing three thermochemical conversion processes; namely, slow pyrolysis, fast pyrolysis and gasification. Statistical analysis has been performed and models were built with (95% confidence level) which correlate the various products' yields with the waste composition and operating conditions. These models provide a guide to researchers regarding the expected yield of products for each of the three studied processes so as to discard the non-promising processes for the used type of waste. This will accordingly minimize the number of required experimental runs; hence saving time and money. A decision matrix was also developed based on the statistical models of each of the studied thermochemical processes. It was concluded that when carbon content is moderate (40-46%, by mass), gasification process is favored if the hydrogen content is high; otherwise, slow pyrolysis is favored. On the other hand, if the waste has high carbon content (above 47%) then fast pyrolysis is favored.
Citer ce document
Accès au document
Texte intégral en lecture en ligne, réservé aux abonnés SPHAERO et aux membres de l'institution. Se connecter
Voir l'article sur le site de la revueAuteur(s)
Statistiques
Consultations : 1
Téléchargements : 0