Active learning-guided exploration of parameter space of air plasmas to enhance the energy efficiency of NO _x production

Low temperature, air plasmas have shown promise for production of NO x for nitrogen fixation. However, to make nitrogen fixation via air plasmas economically viable, a major challenge arises from reducing the energy cost of NO x generation, which is a complex function of a multitude of factors including the plasma discharge type, discharge operating parameters and presence of heterogeneous catalysts. This paper presents an active learning (AL) approach for exploring the multivariable and highly nonlinear parameter space of low temperature plasmas (LTPs) in a systematic and efficient manner. The proposed AL approach relies on Bayesian optimization, which is a data-driven optimization method that is particularly suited for optimizing black-box functions constructed from noisy observations. We demonstrate the AL approach for querying the parameter space of a DC pin-to-pin glow discharge in order to enhance the energy efficiency of NO x production. It is observed that, given a fixed experimental budget, AL consistently outperforms random search of the parameter space in terms of minimizing the energy cost or maximizing the rate of NO x generation in the presence of a constraint on discharge power. AL approaches can pave the way for automated and efficient exploration of the high-dimensional parameter space of LTPs, towards establishing insights into their complex behaviors.