Journal
ENERGIES
Volume 9, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/en9090755
Keywords
benchmark; energy saving; environmental impact; intelligent control; reinforcement learning; wastewater system
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Funding
- Spanish Ministry of Economy and Competitiveness [DPI2011-27818-C02-02, DPI2014-55932-C2-2-R]
- FEDER funds
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Currently, energy and environmental efficiency are critical aspects in wastewater treatment plants (WWTPs). In fact, WWTPs are significant energy consumers, especially in the active sludge process (ASP) for the N-ammonia removal. In this paper, we face the challenge of simultaneously improving the economic and environmental performance by using a reinforcement learning approach. This approach improves the costs of the N-ammonia removal process in the extended WWTP Benchmark Simulation Model 1 (BSM1). It also performs better than a manual plant operator when disturbances affect the plant. Satisfactory experimental results show significant savings in a year of a working BSM1 plant.
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