Online performance prediction of CCPP byproduct coal-gas system based on online sequential extreme learning machine

Aiming at the problem of mismatch between the model and the process for a byproduct coal-gas system in a combined cycle power plant(CCPP) due to frequent changes in working conditions,this article introduces a method for online performance prediction of the CCPP byproduct coal-gas system based on an online sequential extreme learning machine(OS-ELM).Firstly,by analyzing the working principle of each main component in the byproduct coal-gas system and using the fluid mechanics,energy conservation and mass conservation principles,a mechanistic model is established for performance prediction of the byproduct coal-gas system,which essentially consists of scrubbers,centrifugal compressors,and coolers.Further,the OS-ELM and the sliding window technique are also used to correct the error of the mechanistic model,thus we realize the accurate prediction of export parameters and the update of the model in time.Simulation results show that this method can accurately predict the pressure ratio and temperature ratio of the byproduct coal-gas system and track the change in coal-gas system working conditions and the characteristics drift,which meet the needs of actual industrial production.