2 research outputs found
Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning
Wastewater treatment plants are designed to eliminate pollutants and
alleviate environmental pollution. However, the construction and operation of
WWTPs consume resources, emit greenhouse gases (GHGs) and produce residual
sludge, thus require further optimization. WWTPs are complex to control and
optimize because of high nonlinearity and variation. This study used a novel
technique, multi-agent deep reinforcement learning, to simultaneously optimize
dissolved oxygen and chemical dosage in a WWTP. The reward function was
specially designed from life cycle perspective to achieve sustainable
optimization. Five scenarios were considered: baseline, three different
effluent quality and cost-oriented scenarios. The result shows that
optimization based on LCA has lower environmental impacts compared to baseline
scenario, as cost, energy consumption and greenhouse gas emissions reduce to
0.890 CNY/m3-ww, 0.530 kWh/m3-ww, 2.491 kg CO2-eq/m3-ww respectively. The
cost-oriented control strategy exhibits comparable overall performance to the
LCA driven strategy since it sacrifices environmental bene ts but has lower
cost as 0.873 CNY/m3-ww. It is worth mentioning that the retrofitting of WWTPs
based on resources should be implemented with the consideration of impact
transfer. Specifically, LCA SW scenario decreases 10 kg PO4-eq in
eutrophication potential compared to the baseline within 10 days, while
significantly increases other indicators. The major contributors of each
indicator are identified for future study and improvement. Last, the author
discussed that novel dynamic control strategies required advanced sensors or a
large amount of data, so the selection of control strategies should also
consider economic and ecological conditions
Recommended from our members
Indicator based multi-criteria decision support systems for wastewater treatment plants
Data availability:
Data will be made available on request.Wastewater treatment plant decision makers face stricter regulations regarding human health protection, environmental preservation, and emissions reduction, meaning they must improve process sustainability and circularity, whilst maintaining economic performance. This creates complex multi-objective problems when operating and selecting technologies to meet these demands, resulting in the development of many decision support systems for the water sector. European Commission publications highlight their ambition for greater levels of sustainability, circularity, and environmental and human health protection, which decision support system implementation should align with to be successful in this region. Following the review of 57 wastewater treatment plant decision support systems, the main function of multi-criteria decision-making tools are technology selection and the optimisation of process operation. A large contrast regarding their aims is found, as process optimisation tools clearly define their goals and indicators used, whilst technology selection procedures often use vague language making it difficult for decision makers to connect selected indicators and resultant outcomes. Several recommendations are made to improve decision support system usage, such as more rigorous indicator selection protocols including participatory selection approaches and expansion of indicators sets, as well as more structured investigation of results including the use of sensitivity or uncertainty analysis, and error quantification.Horizon 2020 research and innovation programme DEEP PURPLE. The H2020 DEEP PURPLE project has received funding from the Bio-based Industries Joint Undertaking (JU) under the European Union's Horizon 2020 research and innovation programme under grant agreement No 837998. The JU receives support from the European Union's Horizon 2020 research and innovation programme and the Bio-based Industries Consortium