Prediction and optimization of struvite recovery from wastewater by machine learning
The recovery of nitrogen and phosphorus from simulated wastewater in the form of struvite was investigated through a Machine Learning (ML)-based approach. The Extreme Gradient Boosting Algorithm (XGBoost) and Random Forest (RF) models were used for single-objective and multi-objective prediction of...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Energy Environmental Protection
2023-12-01
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| Series: | 能源环境保护 |
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| Online Access: | https://eep1987.com/en/article/4655 |
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| _version_ | 1850109201555128320 |
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| author | TONG Ying JIANG Shaojian KANG Bingyan LENG Lijian* LI Hailong |
| author_facet | TONG Ying JIANG Shaojian KANG Bingyan LENG Lijian* LI Hailong |
| author_sort | TONG Ying |
| collection | DOAJ |
| description | The recovery of nitrogen and phosphorus from simulated wastewater in the form of struvite was investigated through a Machine Learning (ML)-based approach. The Extreme Gradient Boosting Algorithm (XGBoost) and Random Forest (RF) models were used for single-objective and multi-objective prediction of the recovery rates of N and P, respectively. The effects of seven process conditions on struvite crystallization were identified. The results showed that XGBoost outperformed RF in both single-objective (R^2=0.91~0.93) and multi-objective (R^2=0.89) predictions. Furthermore, experimental validation was conducted with initial phosphorus concentrations of 10 mg/L and 1000 mg/L to determine the optimized process conditions for struvite recovery using the multi-objective model. The optimal conditions were found to be: N∶P ratio of 1.2∶1, Mg∶P ratio of 1∶1, pH of 9.5, reaction time of 80 min, reaction temperature of 25 ℃, and stirring rate of 240 r/min. |
| format | Article |
| id | doaj-art-0b9afbad69144bc98ffe23da4153ccb0 |
| institution | OA Journals |
| issn | 2097-4183 |
| language | zho |
| publishDate | 2023-12-01 |
| publisher | Editorial Office of Energy Environmental Protection |
| record_format | Article |
| series | 能源环境保护 |
| spelling | doaj-art-0b9afbad69144bc98ffe23da4153ccb02025-08-20T02:38:09ZzhoEditorial Office of Energy Environmental Protection能源环境保护2097-41832023-12-01376798810.20078/j.eep.20231102Prediction and optimization of struvite recovery from wastewater by machine learningTONG Ying 0JIANG Shaojian1KANG Bingyan2LENG Lijian* 3LI Hailong4School of Energy Science and Engineering, Central South UniversitySchool of Energy Science and Engineering, Central South UniversitySchool of Energy Science and Engineering, Central South UniversitySchool of Energy Science and Engineering, Central South UniversitySchool of Energy Science and Engineering, Central South UniversityThe recovery of nitrogen and phosphorus from simulated wastewater in the form of struvite was investigated through a Machine Learning (ML)-based approach. The Extreme Gradient Boosting Algorithm (XGBoost) and Random Forest (RF) models were used for single-objective and multi-objective prediction of the recovery rates of N and P, respectively. The effects of seven process conditions on struvite crystallization were identified. The results showed that XGBoost outperformed RF in both single-objective (R^2=0.91~0.93) and multi-objective (R^2=0.89) predictions. Furthermore, experimental validation was conducted with initial phosphorus concentrations of 10 mg/L and 1000 mg/L to determine the optimized process conditions for struvite recovery using the multi-objective model. The optimal conditions were found to be: N∶P ratio of 1.2∶1, Mg∶P ratio of 1∶1, pH of 9.5, reaction time of 80 min, reaction temperature of 25 ℃, and stirring rate of 240 r/min.https://eep1987.com/en/article/4655wastewater resource utilizationmachine learningstruvitephosphorus recoverynitrogen recovery |
| spellingShingle | TONG Ying JIANG Shaojian KANG Bingyan LENG Lijian* LI Hailong Prediction and optimization of struvite recovery from wastewater by machine learning 能源环境保护 wastewater resource utilization machine learning struvite phosphorus recovery nitrogen recovery |
| title | Prediction and optimization of struvite recovery from wastewater by machine learning |
| title_full | Prediction and optimization of struvite recovery from wastewater by machine learning |
| title_fullStr | Prediction and optimization of struvite recovery from wastewater by machine learning |
| title_full_unstemmed | Prediction and optimization of struvite recovery from wastewater by machine learning |
| title_short | Prediction and optimization of struvite recovery from wastewater by machine learning |
| title_sort | prediction and optimization of struvite recovery from wastewater by machine learning |
| topic | wastewater resource utilization machine learning struvite phosphorus recovery nitrogen recovery |
| url | https://eep1987.com/en/article/4655 |
| work_keys_str_mv | AT tongying predictionandoptimizationofstruviterecoveryfromwastewaterbymachinelearning AT jiangshaojian predictionandoptimizationofstruviterecoveryfromwastewaterbymachinelearning AT kangbingyan predictionandoptimizationofstruviterecoveryfromwastewaterbymachinelearning AT lenglijian predictionandoptimizationofstruviterecoveryfromwastewaterbymachinelearning AT lihailong predictionandoptimizationofstruviterecoveryfromwastewaterbymachinelearning |