Assessment of novel power generation systems for the biomass industry
The objective of this programme of research is to produce a method for assessing and optimising the performance of advanced gas turbine power plants for electricity generation within the Brazilian electric sector. With the privatisation of the Brazilian electric sector, interest has been given to the thermal plants and studies have been carried out along with the use of other alternative fuels rather than fossil fuels. Biomass is a fuel of increasing interest for power generation systems since it is clean and renewable. Essentially all biomass power plants in the Brazilian market today operate on a steam Rankine cycle, which has a poor efficiency. The Brazilian electricity market has paid attention on Biomass integrated gasification gas turbine (BIG/GT) combined cycle plants where solid biomass is gasified. A simple chemical model for representing the gasifier in the power plant is presented and optimisation of the gasification process has been applied. The method for assessing the performance of power plants takes into account not only energy, but it applies the exergy method, which uses the second law of thermodynamics and works out the destruction of energy inside plant components and energy losses rejected to atmosphere. A thermoeconomic model for assessing the power plant has also been described. The optimisation of the assessment method of power plants using exergy and thermoeconomics has been proposed based on genetic algorithms. This new technique has been fairly successful at solving optimisation problems and is easy to implement. The decision of applying genetic algorithms is due to the complexity of the mathematical model applied in the performance assessment of power plants. The assessment of combined cycles like gas / steam cycle, gas / air cycle, gas / steam / freon cycle, gas / air / freon cycle and chemically recuperated gas turbine have been investigated. The application of the overall assessment method helps to understand different and very expensive choices of power plants before making final decisions.