Performance evaluation of gasification system efficiency using artificial neural network

Gasification is one of the thermo-chemical energy conversion processes with energy self-sufficiency, recoverability and controllable efficiency when compared to combustion and pyrolysis. Gasification process is carried out using different systems (gasifiers), different types of fuels and process conditions, and these factors determine the efficiency of the overall process. The performance of a gasifier is evaluated by some parameters such as Cold Gas Efficiency (CGE), Carbon Conversion Efficiency (CCE), gas yield, gas composition, and lower heating value (LHV) of gas. To understand how efficient a gasifier is, several experiments are needed, but conducting these experiments is time consuming and capital intensive, and the information is vital in energy production plants. Meanwhile, a model that could accurately predict the aforementioned parameters irrespective of the type of gasifier, fuel, and operating conditions is imperative for some time conditions. In this study, Levenberg-Marquardt (LM) back-propagation and Bayesian Regularisation (BR) training algorithms for an Artificial Neural Networks (ANN) were used to study a dataset containing 315 experimental data of biomass, coal, and blends of biomass and coal from various gasifiers and process conditions. Eleven input variables were used in the study, and the result shows that the Mean Square Error (MSE) of the BR algorithm was higher than that of the L-M algorithm. To reduce the MSE, techniques called Input Variables Representation Technique with Visual Inspection method (IVRT-VIM) and Output Variables Representation Technique with Visual Inspection Method (OVRT-VIM) were developed, and their applications produced smaller MSE and an R-2 of between 79 - 98% and 95-96%, respectively. Further, the results of the sensitivity analysis revealed that carbon (% amount) is the most important input parameter affecting the outputs, and with sum of the Squares of the Partial Derivatives (SSD) value of 1.18 for the CGE prediction. (c) 2019 Elsevier Ltd. All rights reserved.