Thermal decomposition of rice husk: a comprehensive artificial intelligence predictive model

Comparing artificial neural network algorithms for prediction of higher heating value for different types of biomass

“…Complete set of new data, used for this estimation, consisted of 237 examples gathered from the literature on experimentally determined values for FC, VM, ASH and HHV. The literature that is used comprised five papers: (Cordero et al 2001), (Alaba et al 2020), (Conag et al 2019), (Lakovic et al 2021) and (Pattanayak et al 2020), denoted with I, II, III, IV and V, respectively, in the Table 8 where the excerpt of the full data set is given. The complete dataset is available from (Jakšić 2021).…”

LeafsnapNet: An Experimentally Evolved Deep Learning Model for Recognition of Plant Species based on Leafsnap Image Dataset

“…The advancement in image processing, pattern recognition and computer vision has brought about the upsurge in the use of deep neural networks in the field of image classification. Relatively, a lot of shallow neural network classifiers have been reported to pose problems of data overfitting, which greatly affects how objects or images are classified (Janani and Gopal, 2013;Sweetwilliams et al, 2019;Alaba et al, 2020;Akanle et al, 2020), hence, the advent of CNN to mitigate the vanishing gradient problem. A CNN is a supervised deep learning method that employs the use of convolution mathematics, which is the process of implementing a 2-D convolution with a filter on an input image, in at least one of the network layers.…”

Ex situ catalytic fast pyrolysis of soy sauce residue with HZSM-5 for co-production of aromatic hydrocarbons and supercapacitor materials

“…Considering that the thermal decomposition behaviors of SSR are not well known at present, thermogravimetric (TG) and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) analyses were rst executed to investigate the thermal degradation of SSR and reveal the pyrolysis kinetics and product distribution. 25 Aerwards, ex situ CFP tests of SSR with HZSM-5 were executed via both Py-GC/MS and a scale-up device. The effects of the pyrolytic reaction temperature as well as the HZSM-5-to-SSR (HZ-to-SSR) ratio on the product formation were studied comprehensively to maximize the yield of aromatic hydrocarbons.…”