Abstract
The present study intends to develop multi-layered feed-forward back-propagation algorithm based artificial neural network (FFBPNN) models to predict the synthesis gas (SG) compositions (H2, CH4, CO & CO2) and yields (mol/kg) for supercritical water gasification (SCWG) of food wastes. Such models are trained with Levenberg-Marquardt (L-M) algorithm, minimized using gradient descent approach and tested with real-time experimental datasets obtained from literature. Moreover, to determine an optimal form of the neural network for a typical non-catalytic SCWG process, a trial and error approach involving multiple combinations of transfer functions and neurons in the network layers is performed. The predicted values of SG compositions yield delivered by the FFBPNN models are in line with the experimental datasets converging to a mean squared error (MSE) value below 0.300 range and coefficient of determination (R2) above 98%. Best prediction accuracy is achieved for CO yield prediction characterized by a least MSE of 0.022 and highest train-test R2 of 0.9942–0.9939. The performance of the developed FFBPNN models can be arranged on the basis of MSE as (ann7)CO < (ann6)CH₄ < (ann5)H₂ < (ann8)CO₂ and on the basis of testing R2 as (ann7)CO > (ann6)CH₄ > (ann5)H₂ > (ann8)CO₂.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.