Artificial Intelligence-Based Prediction of Biomass Higher Heating Value Using Proximate Analysis

Fatima Balarabe Ilyasu

Abstract


The higher heating value (HHV) is a critical factor to assess when analysing and selecting biomass substrates for combustion and power generation. Traditionally, the determination of HHV is conducted in a laboratory setting utilising an adiabatic oxygen bomb calorimeter. In the meantime, this method requires significant effort and financial resources. Therefore, it is crucial to investigate alternative possibilities. This study utilised two distinct techniques based on artificial intelligence: a support vector machine (SVM) and an artificial neural network (ANN) to develop models for predicting biomass HHV based on proximate analysis. The input variables, which include ash, volatile matter, and fixed carbon, were combined to develop four distinct inputs for the prediction models. The comprehensive results indicated that both the ANN and SVM methodologies can ensure precise predictions across all input combinations. The best prediction performances were noted when fixed carbon and volatile matter were combined as the input variables. The results indicated that the ANN surpassed the SVM, achieving the lowest root mean squared error of 0.0008 and the highest correlation coefficient of 0.9274. This study concluded that the ANN is favoured over SVM for predicting biomass HHV based on the proximate analysis. 


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