FAST PREDICTIVE MODEL FOR DESIGN AND OPTIMIZATION OF A LOW EMISSIONS BIOMASS FUELED CHP SYSTEM

摘要

Biomass can play a key role in the development of distributed micro-generation appliances, allowingrnan easy and safe storage of energy in view of satisfying energy needs for the next future. As result of a recent project,rncalled Megaris [1], a new micro CHP system has been proposed. Essentially, the system consists of a fluidized bedrncombustor with the heat transfer head of a Stirling engine in direct contact with the bed, where very low emissionsrncan be achieved with a large variety of biomass sources, together with very high heat transfer coefficients towards thernengine, raising its efficiency to competitive levels. To develop and optimize a configuration ready for introductionrninto the market, a reliable and fast model is required. In this work, a hybrid dynamic model is presented, consisting ofrna set of ordinary differential equations and of a Neural Network submodel introduced for those aspects that cannot bernrepresented by lumped parameter models. The resulting model must correctly reproduce effects due to thernfluidization regime that effectively establishes as working conditions are changed, both on heat transfer coefficientsrnand on combustion efficiency. Results of the model are compared with experimental data collected on a prototype ofrnthe combustor equipped with a Stirling engine. The model is able to correctly reproduce the dynamic behavior of thernsystem under different working conditions, with the accuracy required to perform design and optimization analysis.