Activities per year
Abstract
Conventional methods for water and wastewater treatment are energy-intensive, notably at the stage of coagulation–flocculation, calling for new strategies to predict pollutant reduction because the amount of energy consumed is related to how much of the pollutant is treated. Here we developed a model, named Bio-logic, inspired by ecosystems, where pollutants represent organisms, coagulants are food, and the wider environmental conditions are the living environment. Artificial intelligence was used to learn the biological behavior, which enabled an accurate prediction of the amount of pollutant reduction. Results show that pseudo-biological objects that have a strong affinity for biological food, such as turbidity, total phosphorus, ammonia nitrogen and the potassium permanganate index, induced a strong correlation, between measured pollutant consumption capacity and predicted values. For instance, R 2 correlation coefficients are 0.97 for turbidity and 0.92 for the potassium permanganate index in the laboratory; and 0.99 for turbidity, 0.90 for total phosphorus, 0.75 for ammonia nitrogen and 0.63 for the potassium permanganate index in water treatment plants. Overall, our findings demonstrate that artificial intelligence can use the water Bio-logic model to predict the pollutant consumption capacity.
Original language | English |
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Number of pages | 10 |
Journal | Environmental Chemistry Letters |
DOIs | |
Publication status | Published - 28 Apr 2023 |
Keywords
- drinking water
- coagulation
- artificial intelligence
- pollutant removal
- bio-logic
Fingerprint
Dive into the research topics of 'Ecosystem-inspired model and artificial intelligence predicts pollutant consumption capacity by coagulation in drinking water treatment'. Together they form a unique fingerprint.Activities
- 1 Types of Award - Fellowship awarded competitively
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High End Expert Scholarship – Hunan Regional Government
Hursthouse, A. (Recipient)
2016 → 2019Activity: Other › Types of Award - Fellowship awarded competitively
Prizes
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Liancheng Friendship Award of Xiangtan City, Hunan.
Hursthouse, A. (Recipient), 12 Dec 2017
Prize: Prize (including medals and awards)