Deep reinforcement learning for automated search of model parameters: photo-fenton wastewater disinfection case study

Abstract

Numerical optimization solves problems that are analytically intractable at the cost of arriving at a sufficiently good but rarely optimal solution. To maximize the result, optimization algorithms are run with the guidance and supervision of a human, usually an expert in the problem. Recent advances in deep reinforcement learning motivate interest in an artificial agent capable of learning to do the expert’s task. Specifically, we present a proximal policy optimization agent that learns to optimize in a real case study such as the modeling of the photo-fenton disinfection process, which involves a number of parameters that have to be adjusted to minimize the error of the model with respect to the experimental data collected in several trials. The expert spends an average of 4 h to find a suitable set of parameters. On the other hand, the agent we present does not require a human expert to guide or validate the optimization procedure and achieves similar results in $$2.5\times$$ 2.5 × less time.