Integrating online learning technology with computational fluid dynamics to control combustion process

The work presented in this paper has been developed aiming at how to integrate an online learning controller with an online simulation module to control a complex combustion process, in which some critical process variables which are not easy to be measured using industry instruments. First, it is intended to design a neural network based adaptive controller which owns the ability of learning a real time process. This work consists of designing an online indirect adaptive controller based on radial basis function (RBF) and combining the controller with a numerical combustion process simulated using computational fluid dynamics (CFD). Secondly, the integrated system is simulated in Simulink. Finally, another proportional-integral-derivation (PID) controller is built which substitutes the proposed online learning controller combined with CFD based simulation module to test the proposed control system. The performance of the two different controllers is compared and the results show that the online learning controller is more efficient than PID controller. Moreover, all the work show encouraging results that integrating online learning controller with CFD based online simulation module can provide a new strategy to control a complex combustion process in which instrument reading data is difficult to obtain.