Co-firing of coal and biomass under air and oxy-fuel environments in fluidized bed rig
Coal combustion is one of the main technologies utilized for production of energy throughout the world. Despite the active development of alternative energy sources and climate change conventions, the coal demand has shown to increase around the world, caused by growth of economy and industrial activity. Taking strong positions in countries’ energy generation shares (in Central Europe countries like Poland coal share in energy sector reached 80% in 2017) coal demand has a potential to increase in the following years (till 2022) according to the forecast of International Energy Agency. However, despite the strong positions in energy field, maturity of the technology and wide application around the world, coal combustion as an energy source brings significant environmental drawback that put this technology under a debate. Environmental drawbacks include emissions of trace pollutants (SOx, NOx, CO, CO2) in large amounts contributing to greenhouse gases and overall air quality deterioration. Kazakhstan, being among the countries whose energy sector is primarily based on coal combustion, also experiences disadvantages of coal combustion. The situation of Kazakhstani coal combustion is aggravated by the fact that coal mined in Kazakhstan has high ash content (low-grade Ekibastuz coal contains 30-40% of ash) and the process does not exhibit sufficient efficiency. In general, countries exposed to combustion of low-grade coal experience severe losses due to low efficiency. Here a need for convenient optimization tool emerges in order to minimize the emissions and costs along with increasing the energy generated. One of such tools might be simulation of the process, which can be performed in Aspen Plus modelling software. A dynamic model designed in Aspen Plus will allow to observe cause and effect correlation of variable changes and see how changes brought on the plant will result in the output by implementing sensitivity analysis on wide range of variables.