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Biomass is a renewable and sustainable source of ''green'' energy. The elemental composition comprising carbon (C), hydrogen (H) and oxygen (O) as major components, is an important measure of the biomass fuel's energy content. Its knowledge is also valuable in: (a) computing material balance in a biomass-based process, (b) designing and operating biomass utilizing efficient and clean combustors, gasifiers and boilers, (c) fixing the quantity of oxidants required for biomass combustion/ gasification, and (d) determining the volume and composition of the combustion/gasification gases. Obtaining the elemental composition of a biomass fuel via ultimate analysis is an expensive and time-consuming task. In comparison, proximate analysis that determines fixed carbon, ash, volatile matter and moisture content is a cruder characterization of the fuel and easier to perform. Thus, there exists a need for models possessing high accuracies for predicting the elemental composition of a solid biomass fuel from its proximate analysis constituents. Accordingly, this study utilizes three computational intelligence (CI) formalisms, namely, genetic programming, artificial neural networks and support vector regression, for developing nonlinear models for the prediction of C, H and O fractions of solid biomass fuels. A large database of 830 biomasses has been used in the stated model development. A comparison of the prediction accuracy and generalization performance of the nine CI-based models (three each for C, H and O) with that of the currently available linear models indicates that the CI-based models have consistently and significantly outperformed their linear counterparts. The models developed in this study have proved to be the best models for the prediction of elemental composition of solid biomass fuels from their proximate analyses.

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