Modeling and dynamic characteristics of the dense phase region of a biomass-fired circulating fluidized bed combustion system using Modelica

Abstract

Using Modelica language, a mathematical model combining static and dynamic contributions of the dense phase region of a 130 t/h biomass circulating fluidized bed boiler combustion system was established on MWorks simulation platform. The mathematical model adopted modular packaging to increase the universality of the model, and it used an implicit, high-order, and multi-step Dassl integration algorithm to conduct the simulation. Under the design condition parameters, the relative error between the bed temperature of the dense phase region obtained by the simulation model and the actual temperature was less than 3.8%, which indicated that the static characteristics of the established simulation model were accurate. The effects of biomass feed and primary air volume step changes on the bed temperature, oxygen content in the flue gas, height of the dense phase region, and the bed pressure difference in the dense phase region were investigated. Both the biomass feeding and the primary wind step of 10% reduced the temperature, and it was obvious that the primary wind had a greater impact on the bed temperature. Meanwhile, the primary wind had a greater impact on the bed pressure difference than the biomass feeding.

Influence of Operating Parameters on Chlorine Release and Pollutant Emission Characteristics of a 130 t/h BCFB Combustion System

“…The changes of these operating parameters have a complex interleaving effect on the BCFB. At the same time, the safety requirement of boiler systems is high and some system parameters are difficult to be measured directly . It is difficult to obtain the coupling relationship between the above-mentioned factors and the bed temperature, bed pressure, and pollutant concentration in flue gas through simple tests.…”

“…At the same time, the safety requirement of boiler systems is high and some system parameters are difficult to be measured directly. 41 It is difficult to obtain the coupling relationship between the abovementioned factors and the bed temperature, bed pressure, and pollutant concentration in flue gas through simple tests. Therefore, it was important means to obtain the qualitative and quantitative effects of parameter disturbance on system output through numerical simulation.…”