In Tejaswini et al. (2021), genetic algorithm was used to determine the optimal parameters of PI controllers to improve the effluent quality of WWTPs at a minimum operational cost. Husin et al. (2019) used a neural network to improve the effluent quality of a WWTP in an ammonia-based aeration control scheme.
See how water and wastewater treatment facilities are using AI software like Jacobs' Intelligent O&M to leverage predictive operations. Water and wastewater treatment are energy-intensive, requiring tens or sometimes hundreds of thousands of kWh of electricity every day, leading to high operational costs.
This enables intelligent controllers to improve both effluent quality and reduce the overall cost of energy needed to remove pollution from raw wastewater. The training and testing times of both the ANFIS-GA and the ANFIS-PSO intelligent controllers are 1 h 40 min and 3 h 18 min, respectively.
Water Sci Technol (2024) 90 (3): 731–757. Artificial intelligence (AI) is increasingly being applied to wastewater treatment to enhance efficiency, improve processes, and optimize resource utilization.
Many utilities across the country are tackling these challenges by investing in water and wastewater infrastructure technology to create what are known as intelligent water systems.
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AI can help bridge the gap between an employee''s current skillset and these highly sophisticated water and wastewater management systems. With predictive recommendations delivered directly to a
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nhanced bio-gas recovery, and superior water quality. In this white paper, we''ll examine that long-term cost analysis in depth so you can make the best design and purch.
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Notably, the review highlights successful outcomes from these initiatives across diverse geographical contexts, showcasing the adaptability and positive impact of AI in revolutionizing wastewater
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Pump and aeration systems account for more than 80% of WWTPs'' energy consumption. Therefore, designing effective operational strategies and monitoring the wastewater treatment
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The Plant module of Hubgrade Performance creates an online digital twin of the wastewater treatment plant and/or sewer network; applies predictive AI models, real-time analysis of key parameters of the
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To address these challenges, this study introduces an innovative feature extraction method designed to enhance the cost-effectiveness of dynamic control in wastewater treatment plants.
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These plants are required to operate continuously to meet stringent effluent requirements at the lowest operational cost. This paper presents the design of intelligent computational hybrid control schemes
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In this study, intelligent (fuzzy logic) control strategy is designed for plant-level wastewater treatment plants (WWTP) with an aim to reduce the operational cost and improve the...
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