The dimensional design of a laboratory-scale fluidized bed gasifier using machine learning based on a kinetic method - ÖZVEREN, UĞUR (My University, 2022)
Gasification provides various environmental and technological advantages, and the efficiency of the gasification system is affected by several factors, including the kind of fuel and gasification agent used, the gasifier\"s length and diameter, the operating pressure and temperature, etc. Experimental optimization approaches are more realistic, but they are time demanding; also, a reactor operating at high temperatures and pressures could be dangerous and expensive. Thus, researchers use a variety of modeling techniques, including the process simulators. Additionally, artificial neural network (ANN) as a machine learning approach, which is one of the process modeling methods, is a remarkable approach, and several papers have been published in which it has been utilized in combination with other modeling techniques. On the other hand, a combined process simulator/ANN model that considers gasifier design/operational parameters for the kinetic modeling of gasification process has not been reported. In this study, after kinetic modeling and validation of seven different circulating fluidized gasifiers using Aspen Plus, parametric studies were performed. Parametric analysis was used to examine the impacts of gasifier diameter, length, gasifier temperature, air/fuel ratio, and fuel type, and a dataset was created for ANN training. The syngas composition and thermal value were predicted using the ANN model. Therefore, a model was developed that takes into consideration both design and operating variables. The investigations revealed that heterogeneous reactions were the most critical factor in defining syngas characteristics. Although design factors have a considerable impact on syngas characteristics, the gasifier temperature is a key factor in the whole process. Furthermore, the ANN model estimates syngas specifications with great accuracy (R2 > 0.99 and MAPE < 3%) based on fuel attributes and gasifier design/operating parameters. Hence, ANN models can be used to analyze the effectiveness of systems including a complex combination of reactions and thermochemical processes.
Keywords: Tarımsal Bilimler; Ziraat; Tarım Makineleri; Tarımda Enerji; Biyoyakıt Teknolojisi; Fizik; Nükleer Fizik; Temel Bilimler; Mühendislik ve Teknoloji; Agricultural Sciences; Agriculture; Farm Machinery; Energy in Agriculture; Biofuels Technology; Physics; Nuclear physics; Natural Sciences; Engineering and Technology; Tarım ve Çevre Bilimleri (AGE); Mühendislik, Bilişim ve Teknoloji (ENG); Temel Bilimler (SCI); Tarım Bilimleri; Mühendislik; Doğa Bilimleri Genel; TARIM, MULTİDİSİPLİNLER; ENERJİ VE YAKITLAR; ÇOK DİSİPLİNLİ BİLİMLER; NÜKLEER BİLİMİ VE TEKNOLOJİSİ; Agriculture & Environment Sciences (AGE); Engineering, Computing & Technology (ENG); Natural Sciences (SCI); AGRICULTURAL SCIENCES; ENGINEERING; NATURAL SCIENCES, GENERAL; PHYSICS; AGRICULTURE, MULTIDISCIPLINARY; ENERGY & FUELS; MULTIDISCIPLINARY SCIENCES; NUCLEAR SCIENCE & TECHNOLOGY; Radyasyon; Yenilenebilir Enerji, Sürdürülebilirlik ve Çevre; Nükleer Enerji ve Mühendislik; Yakıt Teknolojisi; Enerji Mühendisliği ve Güç Teknolojisi; Enerji (çeşitli); Genel Enerji; Tarım ve Biyoloji Bilimleri (çeşitli); Multidisipliner; Fizik Bilimleri; Yaşam Bilimleri; Radiation; Renewable Energy, Sustainability and the Environment; Nuclear Energy and Engineering; Fuel Technology; Energy Engineering and Power Technology; Energy (miscellaneous); General Energy; Agricultural and Biological Sciences (miscellaneous); Multidisciplinary; Physical Sciences; Life Sciences