Design of a deep reinforcement learning based optimal PH controller for nitrification bioreactors in aquaponics systems

Abstract

Recent advances in deep reinforcement learning has produced state of the art algorithms. These algorithms have better training stability, convergence and computational performance. In this study a state of the art deep reinforcement learning algorithm is used to implement a self-learning, model free, non-linear controller to control pH of an aquaponic system. Aquaponics is a soil-less farming system where effluent water from a fish tank is used as nutrients for growing plants. Maintaining the pH of an aquaponic system provides the optimal condition for micro-organisms that convert the ammonia rich fish effluent to nitrates, which are easily absorbed by the plants. In order to optimize this conversion process known as nitrification, pH is maintained at optimal conditions within an intermediate setup known as the nitrification bioreactor. The implementation of a deep reinforcement learning based controller is studied in detail and the performance of the deep reinforcement learning based pH controller is evaluated by comparing the performance of a classic PID based controller in an aquaponic system. The results show that DRL based controllers are better suited for control of dynamic stochastic control pH process and is capable of learning complex plant models and tuning itself based on the learnt model. The outcomes of this research can be applied in the design of optimal controllers that learns purely from experience to optimize various industrial processes. This type of controllers is ideal in Industry 4.0 based applications.