Granular acid-activated neutralized red mud (AaN-RM) has been successfully prepared with good chemical stability and physical strength. However, its potential for industrial application remains unknown. Therefore, the performance of granular AaN-RM for phosphate recovery in a fixed-bed column was investigated. The results demonstrated that the phosphate adsorption performance of granular AaN-RM in a fixed-bed column was affected by various operational parameters, such as the bed depth, flow rate, initial solution pH and initial phosphate concentration. With the optimal empty-bed contact time (EBCT) of 24.27 min, the number of processed bed volumes and the phosphate adsorption capacity reached 496.95 and 84.80 mg/g, respectively. Then, the saturated fixed-bed column could be effectively regenerated with a 0.5 mol/L HCl solution. The desorption efficiency remained as high as 83.45% with a low weight loss of 3.57% in the fifth regeneration cycle. In addition, breakthrough curve modelling showed that a 5-9-1 feed-forward artificial neural network (ANN) could be effectively applied for the optimization of the fixed-bed adsorption system; the coefficient of determination (R2) and the root mean square error (RMSE) evaluated on the validation-testing data were 0.9987 and 0.0183, respectively. Therefore, granular AaN-RM fixed-bed adsorption exhibits promising potential for phosphate removal and recovery from polluted water. 相似文献
Both China’s national subsidy policies for plug-in electric vehicles (PEVs) purchasers and passenger cars corporate average fuel consumption and new vehicle credit regulation (dual-credit policy) favor long-range 300+ km battery electric vehicles (BEVs) and 80+ km plug-in hybrid electric vehicles (PHEVs). However, these electric vehicles tend to have lower energy efficiency and higher purchase and operation costs. Vehicle with larger batteries can also be less equitable because the subsidies are often provided to more expensive vehicles and wealthier owners. This study takes advantage of a novel dataset of daily driving data from 39,854 conventional gasoline vehicles in Beijing and 4999 PHEVs in Shanghai to determine the optimal range of BEVs and PHEVs within their respective cities. We simulate a model to explore ranges with which PEVs emit less GHGs than that of a baseline hybrid and conventional gasoline vehicle while ensuring that all daily travel demands are met. Our findings indicate that in both cities, the optimal ranges to balance cost and travel demand for BEVs are 350 km or less and for PHEVs are 60 km or less in Beijing and 80 km or less in Shanghai. We also find that to minimize carbon dioxide (CO2) emissions, the ranges are even lower 10 km in Beijing and 30 km in Shanghai. Our study suggests that instead of encouraging long-range PEVs, governments should subsidize PEV models with shorter ranges. Parallel efforts should also be made to both increase renewable energy over fossil fuels and expand charging facilities. Although individual mobility demand varies, the government could reduce occasional long-distance driving by subsidizing alternative transportation choices. Providing week-long driving trials to consumers before their purchases may help decrease the demand of very long range PEVs by alleviating the range anxiety through a learning process.
