Dataset from estimation of gasification system efficiency using artificial neural network technique

Abstract

Abstract The dataset contained in this article supplements the data information contained in the performance evaluation of gasification system efficiency using artificial neural network technique. The dataset was obtained from investigating efficiency of a gasification system using 315 fuel samples containing biomass, coal, and coal-biomass blends via the application of Levenberg–Marquardt (LM) back-propagation and Bayesian Regularization (BR) training algorithms in Artificial Neural Networks (ANN) domain. Input Variables Representation Technique-by-Visual Inspection method (IVRT-VIM) and Output Variables Representation Technique –by-Visual Inspection Method (OVRT-VIM) were developed as well to improve results from the ANN models. Sensitivity analysis on the prediction of the product gas compositions (CO, CO2, H2, and CH4) was carried out using partial derivatives (PaD) method. There was a considerable decrease in the MSE and an increase in the R2 of around 93 – 96% and 94 – 96% using 10 & 23 number of neurons in the hidden layer and IVRT-VIM technique, respectively. The L -M algorithm offered a better result than the BR algorithm, and with Sulphur as the most important input variable that affected the outputs.