Thermodynamic equilibrium-based models of gasification process are relatively simple and widely used to predict producer gas characteristics in performance studies of energy conversion plants. However, if an unconstrained calculation of equilibrium is performed, the estimations of product gas yield and heating value are too optimistic. Therefore, reasonable assumptions have to be made in order to correct the results. This paper proposes a model of the process that can be used in case of deficiency of information and unavailability of experimental data. The model is based on free energy minimization, material and energy balances of a single zone reactor. The constraint quasi-equilibrium calculations are made using approximated amounts of non-equilibrium products, i.e. solid char, tar, CH4 and C2H4. The yields of these products are attributed to fuel characteristics and estimated using experimental results published in the literature. A genetic algorithm optimization technique is applied to find unknown parameters of the model that lead to the best match between modelled and experimental characteristics of the product gas. Finally, generic correlations are proposed and quality of modelling results is assessed in the aspect of its usefulness for performance studies of power generation plants.
The paper presents modeling and simulation results of the operation of a three-phase fluidized bed bioreactorwith partial recirculation of biomass. The proposed quantitative description of the bioreactor takes into account biomass growth on inert carriers, microorganisms decay and interphase biomass transfer. Stationary characteristics of the bioreactor and local stability of steady-stateswere determined. The influence of microbiological growth kinetics on the multiplicity of steady-states was discussed. The relationship between biofilm growth and boundaries of fluidized bed existence was shown.
The industrial grinding devices, which work in the high-energetic fluidized bed conditions make it possible to obtain guaranteed particle size distribution of product and decrease of consumption energy. The matrix model for transformation of particle size distribution in the fluidized bed opposed jet mill is presented in the part IV of article. The proposed model contains the mass population balance of particle equation, in which are block matrices: the matrix of circuit M, the matrix of inputs F and the matrix of feed F0. The matrix M contains blocks with the transition matrix P, the classification matrix C, the identity matrix I and the zero matrix 0. The matrix was marked using with discrete forms of the selection and breakage functions, mean while the matrices of classification - using the equation, describing classification of grains in the grinding chamber of mill. In paper was discussed this model in details (part 2.1). The correctness of received form of the selection and breakage functions was confirmed. The method determination of the transition matrix for fluidized-jet grinding of grains (part 2.2) and the classification matrix for gravitational and centrifugal zones of grains (part 2.3) are presented. The verification of model obtained on basis results with experimental investigations, which were performed on a laboratory fluidized bed opposed jet mill. The experiment contained grinding of selected narrow size fractions of limestone in turbulent fluidized layer conditions, what in part I and part II of article (Zbroński, Górecka-Zbrońska 2007a, b) are presented. The parameters of parametric identification were: factor of proportionality - contained in the equation on the discrete form of selection function and sizes of limiting grains - contained in equation on the diagonal elements of classification matrix for stage of gravitational and centrifugal (part 3). The classic Fisher-Snedecor test was applied for estimation of prediction particle size distribution of grinding product (part 4). The significant divergences between numerical and experimental results of particle size distribution weren't affirmed. The experimental verification, parametric identification and statistical estimation of the proposed model showed that this model make it possible to forecasting particle size distribution of grinding product.