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, R2 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.
An inversion method is applied to identify ingredients of zeotropic refrigerants in a circular duct. In the case of low Reynolds number and constant fluid pressure, the temperature distribution of direct heat transfer problem can be solved numerically. The thermophysical parameters of zeotropic refrigerants are determined by using inversion problem technique, the ingredients of refrigerants can be identified eventually. An in-situ experimental apparatus was proposed and three test samples with different composition refrigerants were conducted in this study. The experiment results show that the relative error of ingredients identification can be limited within 8.33%. 相似文献