Adaptive neuro-fuzzy prediction of carbon monoxide emission from a clinical waste incineration plant

Abstract

The current practice of monitoring air emission from an incineration plant is through a hardware system known as Continuous Emission Monitoring Systems (CEMSs). Considering that CEMS suffers from high installation and maintenance cost, thus, the present work focuses on a modelling technique through an Adaptive Neuro-Fuzzy Inference System (ANFIS) to develop a predictive model of carbon monoxide (CO) emission utilizing actual data taken from a clinical waste incineration plant having capacity to process 250 kg waste/h. An hourly averaged of 1000 data points consisted of nine input–output data pairs was utilized to develop a Sugeno-type fuzzy structure by applying subtractive clustering method. As the data were divided into three sets, i.e. 70% for training, 15% for checking and the rest for testing, the values of the coefficient of determination (R2), root-mean-square error (RMSE), mean bias error (MBE) and accuracy (Ac) were calculated for each set to demonstrate its applicability and validity, emphasizing on the testing set since unseen data were exposed to the model. Result showed that ANFIS was able to learn from these data and excellently predicted the CO emission with R2, RMSE, MBE and Ac of 0.98, 4.45, 0.66 ppm and 87.98% in the testing set, respectively.