Integrating GIS Mapping and Artificial Neural Networks for Assessing Biomass Energy Potential From Agricultural Residues in Iran

Abstract

Agricultural residues (such as straw and other nonmarketable plant waste) in Iran exceed 200 million tons annually, which can supply 10%-15% of the country's energy needs. The objective of this study was to investigate and estimate the biomass energy potential derived from crop residues in Iran using GIS mapping and artificial neural networks. The energy potential of the residues was determined by considering their heating value and the quantity of available residues. The available agricultural residues for the 10 crops studied were estimated to be 9,688,450 tons. Sugarcane and sugar beet contributed the largest shares, representing 32.33% and 25.72%, respectively. The largest quantities of sugarcane and wheat residues were found in Khuzestan province, amounting to 3,131,620 and 124,660 tons, respectively. For sugar beet, the maximum amount of residues was recorded in West Azerbaijan, with 719,140 tons. The total heating values for the residues were calculated to be 56,376 TJ for sugarcane, 18,212.36 TJ for wheat, and 42,887.32 TJ for sugar beet. The artificial neural network was able to predict the energy potential of biomass from the main products with a correlation coefficient of over 0.99 and the lowest error rate. GIS maps proved highly effective for rapidly analyzing the status of plant residues and their energy potential in each province. The findings suggest that agricultural residues in Iran have significant potential as a sustainable biomass energy source.