Autonomous Greywater Treatment
Protecting Our Scarce Resources

Autonomous Greywater Treatment

Autonomous Greywater Treatment

Autonomous Greywater Treatment

The unit resembles the conventional wastewater treatment plant, which is miniaturized and consists of primary, secondary, tertiary treatments to effectively remove the pollutants and disinfect the reclaimed effluent in the greywater. In addition, the built greywater system is remote-controllable by the developed mobile application via Arduino controller board, with a machine learning algorithm implemented to study the habit of freshwater consumption and optimize the purification process.

SDG Goal 06 - Clean Water and Sanitation
SDG Goal 08 - Decent Work and Economic Growth
SDG Goal 09 - Industry, Innovation and Infrastructure

What is the problem this project is trying to address?

Water crisis is one of the most important environmental issues in this generation. Only 0.007% of the planet’s natural water resources is freshwater that is readily accessible for human consumption, but as the rapid increase in population and activities in both commercial and industrial sectors, the freshwater consumption and pollution have skyrocketed over the past few decades.

How does this project support our sustainable smart campus as a living lab vision?

Reclaiming greywater from domestic sewages of the HKUST facilities can significantly reduce water consumption by recycling the water for watering plants, fish ponds, flushing water for toilets or even drinking water etc. The autonomous remote-control system minimises the power consumption by smart control. A mobile application will be available to show the wastewater level, UV operation, ozone consumption, pH change, as well as the organics level. The usage of BOD/COD optical sensor probe helps to achieve real-time BOD/COD monitoring is a new element in the designed prototype, estimating the required power for chemical oxidation based on the pollutant concentration. Collected data from the app can be used for big data analytics to create a platform for the machine to learn the habits of water consumption.

What's next?

The optimized unit can be further applied for other kinds of greywater treatment such as the kitchen sink effluent and the washing basin effluent. The built prototype will be installed and sampled at different locations of the HKUST campus, including the school halls’ restrooms, sports hall’s showing facilities, and the labs’ flushing devices. In addition, practical training will be provided in developing the machine learning algorithm for the greywater treatment unit.