ANN-Based modeling and performance analysis of pyrolytic oil production system

Authors : Emirhan Yelekin, İbrahim Mutlu, Murat Alçın, Murat Tuna, İsmail Koyuncu

Pages : 750-757

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Publication Date : 2025-10-19

Article Type : Research Paper

Abstract :In this study, the modeling of the Pyrolytic oil production system using Artificial Neural Networks (ANNs) has been conducted with oak acorn, which can be considered as non-wood forest product. The parameters used in the pyrolytic oil production system have been determined as reactor temperature, nitrogen gas flow rate, biomass particle size, and heating rate. In experimental studies, the highest pyrolytic oil production has been achieved at 500 °C temperature, 1.5 L/min nitrogen gas flow rate, 5 °C/min heating rate, and 0-2 mm biomass particle size, with a product yield of 17.83%. 164 different Multi-Layer Feed Forward (MLFF) ANN-based network architectures have been trained for 20,000 iterations using the data obtained from the pyrolytic oil production system. In the training process, various network architectures including activation functions such as TanSig, LogSig, and RadBas with one or two hidden layers have been utilized. According to the results obtained from the studies, the Multi-Layer Feed Forward ANN-based Pyrolytic Oil Production System structure, which has a single hidden layer and contains 16 LogSig activation function neurons, has been the network structure with the best performance with the value of 1.08E-15.
Keywords : Meşe palamudu, Aktivasyon fonksiyonları, Yapay sinir ağları, Pirolitik yağ üretimi

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