Multi-Objective Optimization of Supercritical Water Oxidation for Radioactive Organic Anion Exchange Resin Wastewater Using GPR–NSGA-II
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| Publicado en: | Processes vol. 13, no. 12 (2025), p. 3759-3780 |
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/pr13123759 |2 doi | |
| 035 | |a 3286345737 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231553 |2 nlm | ||
| 100 | 1 | |a Jin Yabin |u The Institute of Energy and Architecture, Xihang University, Xi’an 710077, China | |
| 245 | 1 | |a Multi-Objective Optimization of Supercritical Water Oxidation for Radioactive Organic Anion Exchange Resin Wastewater Using GPR–NSGA-II | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Radioactive organic anion exchange resins present a significant challenge in nuclear power plant waste disposal due to their volatility, instability, and biotoxicity. Based on experimental degradation data from the supercritical water oxidation (SCWO) of organic anion exchange resin waste liquids from the nuclear industry, this study conducted correlation analysis, cluster analysis, and Sobol sensitivity analysis of key process parameters. The results indicate that temperature is the primary factor influencing chemical oxygen demand (COD) and total nitrogen (TN) removal, while oxidant dosage exhibits a notable synergistic effect on nitrogen transformation. A Gaussian Process Regression–Non-Dominated Sorting Genetic Algorithm II (GPR–NSGA-II) multi-objective optimization model was developed to balance COD/TN removal rate and treatment cost. The optimal operating conditions were identified as a temperature of 472.2 °C, an oxidant stoichiometric ratio (OR) of 136%, an initial COD concentration of 73,124 mg·L−1, and a residence time of 3.8 min. Under these conditions, COD and TN removal efficiencies reached 99.63% and 32.92%, respectively, with a treatment cost of 128.16 USD·t−1. The proposed GPR–NSGA-II optimization strategy provides a methodological foundation for process design and economic assessment of SCWO in treating radioactive organic resin waste liquids and can be extended to other studies involving high-concentration, refractory organic wastewater treatment. | |
| 653 | |a Supercritical water oxidation | ||
| 653 | |a Organic wastes | ||
| 653 | |a Sensitivity analysis | ||
| 653 | |a Chemical oxygen demand | ||
| 653 | |a Nitrogen | ||
| 653 | |a Radioactive wastes | ||
| 653 | |a Optimization | ||
| 653 | |a Anion exchanging | ||
| 653 | |a Genetic transformation | ||
| 653 | |a Waste disposal | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Liquids | ||
| 653 | |a Oxidants | ||
| 653 | |a Energy consumption | ||
| 653 | |a Industrial plant emissions | ||
| 653 | |a Efficiency | ||
| 653 | |a Corrosion | ||
| 653 | |a Wastewater treatment | ||
| 653 | |a Nuclear reactors | ||
| 653 | |a Nuclear power plants | ||
| 653 | |a Oxidizing agents | ||
| 653 | |a Experiments | ||
| 653 | |a Algorithms | ||
| 653 | |a Correlation analysis | ||
| 653 | |a Resins | ||
| 653 | |a Process parameters | ||
| 653 | |a Emissions | ||
| 653 | |a Parameter sensitivity | ||
| 653 | |a Effluents | ||
| 653 | |a Sorting algorithms | ||
| 653 | |a Optimization models | ||
| 653 | |a Machine learning | ||
| 653 | |a Nuclear energy | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Oxidation | ||
| 653 | |a Temperature | ||
| 653 | |a Synergistic effect | ||
| 653 | |a Gaussian process | ||
| 653 | |a Fluidized bed reactors | ||
| 700 | 1 | |a Xu, Tiantian |u The Institute of Energy and Architecture, Xihang University, Xi’an 710077, China | |
| 700 | 1 | |a Zhang, Le |u The Institute of Energy and Architecture, Xihang University, Xi’an 710077, China | |
| 700 | 1 | |a Zhang, Qian |u The Institute of Energy and Architecture, Xihang University, Xi’an 710077, China | |
| 700 | 1 | |a Zhou, Liang |u The Institute of Energy and Architecture, Xihang University, Xi’an 710077, China | |
| 700 | 1 | |a Shen, Zhe |u The Institute of Energy and Architecture, Xihang University, Xi’an 710077, China | |
| 700 | 1 | |a Wan Zhenjie |u The College of Building Environmental Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China | |
| 773 | 0 | |t Processes |g vol. 13, no. 12 (2025), p. 3759-3780 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286345737/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286345737/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286345737/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |