Investigation of relative influence of process variables in a 10-kW downdraft fixed-bed gasifier with ANN models

Abstract

Biomass gasification is considered among promising solutions for renewable energy generation. The process converts the biomass, such as rice husk, to synthetic gas (syngas). It produces CO, CO2, CH4, and H2 gas that are useful for internal combustion engines. The process is complicated to control. Hence, a thorough knowledge of this process is needed. One of the approaches to reveal the control parameters of the gasifier is using an artificial neural network (ANN). In this research, an ANN model is deployed from experiments that measure combustion temperature, intake, and discharge airflow rate as input variables. The output of this model is to predict the increase of combustion temperature in the reactor as this parameter is crucial for the design of an automated control system. From the two experiments, the models produce satisfying accuracy (R2 = 0.832 and 0.911) and relatively low errors (RMSE values of 0.250 and 0.098). The neural network itself is used to analyze the significant control parameters by the permutation importance method.