{"title":"活性羊骨生物炭可持续去除废水中刚果红染料的实验优化和机器学习建模","authors":"Ghazala Muteeb , Adil Alshoaibi , Khalid Ansari","doi":"10.1016/j.jsamd.2025.100947","DOIUrl":null,"url":null,"abstract":"<div><div>This study explores the use of goat bone-based activated biochar (GB<sub>PAC</sub>) synthesized from animal waste as an efficient and sustainable adsorbent for removing Congo Red (CR) dye from aqueous solutions. GB<sub>PAC</sub>, prepared through chemical activation with phosphoric acid, was tested in batch adsorption experiments. FTIR analysis revealed key functional groups such as hydroxyl (O–H), carboxyl (C<img>O), and phosphate groups, which play a crucial role in the adsorption of CR dye through interactions like hydrogen bonding and electrostatic attraction. BET surface area analysis showed that GB<sub>PAC</sub> exhibited a surface area of 91.27 m<sup>2</sup>/g, with a mesoporous structure that enhances its adsorption capacity. The study systematically analyzed factors such as dye concentration (10–50 mg/L), adsorbent dosage (0.15–0.75 g/100 mL), pH (7.5), and contact time (30–180 min). The maximum adsorption capacity of GB<sub>PAC</sub> for CR dye was 83.33 mg/g, and the adsorption process followed the Langmuir isotherm model (R<sup>2</sup> = 0.9907) and pseudo-second-order kinetics. Process Optimization was performed using Response Surface Methodology (RSM), which enabled statistically guided experimental design and optimization of influential variables. Optimal conditions were identified as 48.596 mg/L dye concentration, 0.398 g adsorbent dose, and 88.23 min contact time, achieving a predicted removal efficiency of 94.34 %. To enhance prediction capabilities, machine learning (ML) models, specifically Decision Tree and Random Forest, were trained using experimental data. These models demonstrated strong predictive accuracy, with R<sup>2</sup> values of 0.91 and 0.87, respectively. This dual-framework approach, combining RSM for optimization and ML for predictive modeling, underscores the novelty of using waste-derived GB<sub>PAC</sub> for wastewater treatment applications. The findings support GB<sub>PAC</sub> as a cost-effective, sustainable, and data-driven solution for CR dye removal from contaminated water.</div></div>","PeriodicalId":17219,"journal":{"name":"Journal of Science: Advanced Materials and Devices","volume":"10 3","pages":"Article 100947"},"PeriodicalIF":6.8000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Experimental optimization and machine learning modeling for sustainable Congo red dye removal from wastewater using activated goat bone biochar\",\"authors\":\"Ghazala Muteeb , Adil Alshoaibi , Khalid Ansari\",\"doi\":\"10.1016/j.jsamd.2025.100947\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study explores the use of goat bone-based activated biochar (GB<sub>PAC</sub>) synthesized from animal waste as an efficient and sustainable adsorbent for removing Congo Red (CR) dye from aqueous solutions. GB<sub>PAC</sub>, prepared through chemical activation with phosphoric acid, was tested in batch adsorption experiments. FTIR analysis revealed key functional groups such as hydroxyl (O–H), carboxyl (C<img>O), and phosphate groups, which play a crucial role in the adsorption of CR dye through interactions like hydrogen bonding and electrostatic attraction. BET surface area analysis showed that GB<sub>PAC</sub> exhibited a surface area of 91.27 m<sup>2</sup>/g, with a mesoporous structure that enhances its adsorption capacity. The study systematically analyzed factors such as dye concentration (10–50 mg/L), adsorbent dosage (0.15–0.75 g/100 mL), pH (7.5), and contact time (30–180 min). The maximum adsorption capacity of GB<sub>PAC</sub> for CR dye was 83.33 mg/g, and the adsorption process followed the Langmuir isotherm model (R<sup>2</sup> = 0.9907) and pseudo-second-order kinetics. Process Optimization was performed using Response Surface Methodology (RSM), which enabled statistically guided experimental design and optimization of influential variables. Optimal conditions were identified as 48.596 mg/L dye concentration, 0.398 g adsorbent dose, and 88.23 min contact time, achieving a predicted removal efficiency of 94.34 %. To enhance prediction capabilities, machine learning (ML) models, specifically Decision Tree and Random Forest, were trained using experimental data. These models demonstrated strong predictive accuracy, with R<sup>2</sup> values of 0.91 and 0.87, respectively. This dual-framework approach, combining RSM for optimization and ML for predictive modeling, underscores the novelty of using waste-derived GB<sub>PAC</sub> for wastewater treatment applications. The findings support GB<sub>PAC</sub> as a cost-effective, sustainable, and data-driven solution for CR dye removal from contaminated water.</div></div>\",\"PeriodicalId\":17219,\"journal\":{\"name\":\"Journal of Science: Advanced Materials and Devices\",\"volume\":\"10 3\",\"pages\":\"Article 100947\"},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Science: Advanced Materials and Devices\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468217925001005\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Science: Advanced Materials and Devices","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468217925001005","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Experimental optimization and machine learning modeling for sustainable Congo red dye removal from wastewater using activated goat bone biochar
This study explores the use of goat bone-based activated biochar (GBPAC) synthesized from animal waste as an efficient and sustainable adsorbent for removing Congo Red (CR) dye from aqueous solutions. GBPAC, prepared through chemical activation with phosphoric acid, was tested in batch adsorption experiments. FTIR analysis revealed key functional groups such as hydroxyl (O–H), carboxyl (CO), and phosphate groups, which play a crucial role in the adsorption of CR dye through interactions like hydrogen bonding and electrostatic attraction. BET surface area analysis showed that GBPAC exhibited a surface area of 91.27 m2/g, with a mesoporous structure that enhances its adsorption capacity. The study systematically analyzed factors such as dye concentration (10–50 mg/L), adsorbent dosage (0.15–0.75 g/100 mL), pH (7.5), and contact time (30–180 min). The maximum adsorption capacity of GBPAC for CR dye was 83.33 mg/g, and the adsorption process followed the Langmuir isotherm model (R2 = 0.9907) and pseudo-second-order kinetics. Process Optimization was performed using Response Surface Methodology (RSM), which enabled statistically guided experimental design and optimization of influential variables. Optimal conditions were identified as 48.596 mg/L dye concentration, 0.398 g adsorbent dose, and 88.23 min contact time, achieving a predicted removal efficiency of 94.34 %. To enhance prediction capabilities, machine learning (ML) models, specifically Decision Tree and Random Forest, were trained using experimental data. These models demonstrated strong predictive accuracy, with R2 values of 0.91 and 0.87, respectively. This dual-framework approach, combining RSM for optimization and ML for predictive modeling, underscores the novelty of using waste-derived GBPAC for wastewater treatment applications. The findings support GBPAC as a cost-effective, sustainable, and data-driven solution for CR dye removal from contaminated water.
期刊介绍:
In 1985, the Journal of Science was founded as a platform for publishing national and international research papers across various disciplines, including natural sciences, technology, social sciences, and humanities. Over the years, the journal has experienced remarkable growth in terms of quality, size, and scope. Today, it encompasses a diverse range of publications dedicated to academic research.
Considering the rapid expansion of materials science, we are pleased to introduce the Journal of Science: Advanced Materials and Devices. This new addition to our journal series offers researchers an exciting opportunity to publish their work on all aspects of materials science and technology within the esteemed Journal of Science.
With this development, we aim to revolutionize the way research in materials science is expressed and organized, further strengthening our commitment to promoting outstanding research across various scientific and technological fields.