|
Simulation of optimization of process parameters of nitrogen and phosphorus recovery in biogas slurry derived from swine manure by struvite precipitation method |
Received:January 28, 2018 |
View Full Text View/Add Comment Download reader |
KeyWord:nitrogen and phosphorus recovery;swine manure;response surface methodology;optimal parameters |
Author Name | Affiliation | E-mail | LI Ai-xiu | Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China | | ZHAI Zhong-wei | Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China | | DING Fei-fei | College of Resources and Environment, Jilin Agricultural University, Changchun 130117, China | | DU Lian-zhu | Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China | | ZHANG Ke-qiang | Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China | kqzhang68@126.com |
|
Hits: 2672 |
Download times: 2904 |
Abstract: |
The present study focuses on exploring the process conditions of nitrogen and phosphorus recovery from biogas slurry and optimizing the process parameters using a chemical method to alleviate the difficulties faced by large-scale farms in the digestion of biogas slurry. The biogas slurry of swine manure was assessed. By using the struvite precipitation method, the influence of factors such as pH, magnesium:nitrogen ratio, and phosphorus:nitrogen ratio on the nitrogen and phosphorus recovery rate was investigated and optimized using the Box-Behnken response surface methodology design. The optimum conditions for the recovery of nitrogen and phosphorus in biogas slurry from swine manure were a pH of 10, a magnesium:nitrogen ratio of 1.1, and a phosphorus:nitrogen ratio of 0.6. Under the above optimized conditions, the recovery rates for ammonia and phosphate were 65.21% and 89.47%, respectively. In the actual experiments, the recovery rates for ammonia and phosphate were 65.01% and 90.81%, respectively, with difference values of 0.20% and 1.34%, respectively, indicating good fit with the regression model. |
|
|
|