Research objective

The cognitive treatment plant project aims to increase the efficiency of the water treatment process through the use of an artificial intelligence (AI) technique known as deep reinforcement learning. We teach the AI to minimise energy usage and disposal cost, while maintaining high quality effluent and minimise the number of regulatory violations. By exposing the AI to a wide variety of conditions, through a simulator we aim to ensure the robustness and stability of the control system. Common problems for operating a wastewater plant efficiently are that sensors do not always accurately measure the process; there are uncertainty in loads and volume of influents (which are dependent on weather and domestic/industrial usages).

As a result, wastewater plants are often run in a risk-averse way. For example the plant is operated as if a storm were coming, so more oxygen is pumped into the aerated tanks to ensure that the wastewater is treated properly even under adverse conditions. This means that there might be opportunities for smart pumping scheduling or reduced pumping to lower energy/­electricity/­operational costs based on using weather forecasts and a thorough analysis of which amount of pumping should be sufficient.

We have implemented AI on a controller that controls the pumping rate into an aerated tank. While in operation, the controller learns how different setpoints can lead to different influent qualities and energy costs. It then continuously learns and determines how to optimise the setpoints under different states of the tank so that it can minimise the operational costs while still ensuring that the effluents satisfy the treatment standard.

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Lan Hoang

Lan Hoang

Edward Pyzer-Knapp

Edward Pyzer-Knapp

Chris Thompson

Chris Thompson