05 خرداد 1403
عباس رضايي

عباس رضایی

مرتبه علمی: استادیار
نشانی: دانشگاه صنعتی کرمانشاه، دانشکده مهندسی برق، مهندسی برق (گرایش های الکترونیک و مخابرات)
تحصیلات: دکترای تخصصی / مهندسی برق
تلفن: 083-38305001
دانشکده: دانشکده مهندسی برق

مشخصات پژوهش

عنوان
A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
biomass sample; higher heating value; Elman neural network; topology tuning; training algorithm
پژوهشگران بابک عاقل (نفر اول)، صلاح اسماعیل یحیی (نفر دوم)، عباس رضایی (نفر سوم)، فلاح الوبید (نفر چهارم)

چکیده

The higher heating value (HHV) is the main property showing the energy amount of biomass samples. Several linear correlations based on either the proximate or the ultimate analysis have already been proposed for predicting biomass HHV. Since the HHV relationship with the proximate and ultimate analyses is not linear, nonlinear models might be a better alternative. Accordingly, this study employed the Elman recurrent neural network (ENN) to anticipate the HHV of different biomass samples from both the ultimate and proximate compositional analyses as the model inputs. The number of hidden neurons and the training algorithm were determined in such a way that the ENN model showed the highest prediction and generalization accuracy. The single hidden layer ENN with only four nodes, trained by the Levenberg–Marquardt algorithm, was identified as the most accurate model. The proposed ENN exhibited reliable prediction and generalization performance for estimating 532 experimental HHVs with a low mean absolute error of 0.67 and a mean square error of 0.96. In addition, the proposed ENN model provides a ground to clearly understand the dependency of the HHV on the fixed carbon, volatile matter, ash, carbon, hydrogen, nitrogen, oxygen, and sulfur content of biomass feedstocks.