A simplified model of nitric oxide emission from a circulating fluidized bed combustor

Abstract

A simplified mathematical model leading to a closed form of solution is developed for estimation of nitric oxide emission from a coal fired circulating fluidized bed (CFB) furnace. The furnace is divided into two sections: a lower section below and an upper section above the secondary air injection level. Reactions in the cyclone and the return leg are neglected. Furnace dimensions, coal feed rate, coal composition and furnace temperature are inputs to the model which was validated against several pilot scale and commercial units. Experimental results from two pilot plants and two

Simulation of NOxEmission in Circulating Fluidized Beds Burning Low-grade Fuels†

“…Higher carbon content and CO concentration lead to lower NO x emissions as clearly seen in Figure 4. Another explanation of decreasing NO x emission is the gas dilution caused by increasing excess air, which is also observed in the studies of Liu and Gibbs 34 and Xie et al 35 A smaller mean size of char in the combustor will result in a lower emission of NO x if other parameters are kept unchanged, 36 as clearly seen from Figures 4 and 5.…”

“…For CFB only 106 reactions with 28 species were used to model the NO x emission. However, a detailed review shows that all N-related reactions have not the same importance . So instead of considering all N-related reactions, one could use only the important reactions for the development of a predictive procedure for the overall NO x emission from a CFBC.…”

Optimization of Fluidization State of a Circulating Fluidized Bed Boiler for Economical Operation

“…In this work, reaction used to model the NO x formation and reduction is shown in Table 1. As all N-related reaction does not have the same importance, this work uses only the critical reaction, instead of considering all reaction [28][29][30].…”

Selection of simplified models: I. Analysis of model‐selection criteria using mean‐squared error

“…Inevitably, modelling leads to the situation where some parameters and terms that would appear in the “true” model are missing, either by mistake or by design. There are many reasons to choose a simplified model (SM) with fewer parameters and terms than the truly structured complex model (Neto and Cotta, 1992; Talukdar and Basu, 1995; Gray, 1997; Zhang, 1997; Ismail, 2004; Jaree et al, 2004; Forney et al, 2005; Marquardt, 2005). The practical advantages of a parsimonious model often overshadow concerns over the correctness of the model structure.…”