Power Generation From Coal Mill Rejection Using Kalina Cycle

This paper proposes an ammonia–water Kalina cycle driven by low-grade waste energy released from the combustion reactions of mill's rejection which is coupled with 500 MW_e coal-fired thermal power plant to quantify the additional electrical power. Energy of combustion for mill rejection is computed by combustion modeling equations. A thermodynamic property calculator for the binary mixture and a computer simulation program have been developed by MS-Excel and Visual Basic for Application (VBA) to calculate and optimize the Kalina cycle operating parameters based on thermodynamic modeling equations. Variation of key operating parameters, namely, turbine inlet pressure, mass flow rate of binary mixture, and ammonia mass fraction in mixture is studied and filters the optimum value accordingly to maximize the cycle efficiency. Techno-commercial feasibility is also done through economic analysis. The results show that about 562.745 kW_e power generation can be added with total plant generation for organization profit. This will enhance the combined plant efficiency from 38.559% to 38.604%. Maximum net Kalina cycle efficiency of 24.74% can be achieved with ammonia mass fraction of 0.4 at condenser back pressure of 1.957 bar and turbine inlet pressure and temperature of 20 bar and 442.40 K, respectively. Ammonia mass fraction of 0.4 is the optimum choice for 20 bar turbine inlet pressure to get maximum output after maintaining minimum 50 K degree of superheat compared to ammonia mass fraction of 0.3. The cycle performance at ammonia mass fraction of 0.4 is better than 0.5 due to less condenser back pressure. Kalina cycle operating with less mass flow rate performs higher cycle efficiency when dryness fraction at turbine exhaust is less than 1 and performance deteriorates at above 1. This deterioration is due to higher condenser energy loss carried away by cooling water (CW) flow. The simple payback period of this system is around 5.5 years if the system is running with 80% plant availability factor and 100% plant load factor.