Prediction models for higher heating value based on the structural analysis of the biomass of plant remains from the greenhouses of Almerيa (Spain)

Within the realm of renewable energies, biomass will play a fundamental role in the coming years, especially due to the rise in the prices of fossil fuels, the doubtful safety of nuclear energy, and the need to reduce CO2 emissions. ería (SE Spain), a million tonnes of plant wastes are generated per year from greenhouse crops such as Cucurbita pepo L., Cucumis sativus L., Solanum melongena L., Solanum lycopersicum L., Phaseolus vulgaris L., Capsicum annuum L., Citrillus vulgaris Schrad, and Cucumis melo L., which have an energy potential of around a million MW h year−1. The aim of the present work is to conduct structural analyses (lignin, cellulose, hemicellulose, and extractives) together with new HHV prediction models based on these parameters. For the analyses, internationally recognised methods and norms were used. Also, in the 15 univariate and multivariate prediction equations formulated, R2 and adjusted R2 proved higher in all cases (0.748 and 0.717), respectively, with the mean absolute percentage error varying between 0.119 and 0.623. Finally, only 10 equations were validated.