Volatilization of benzene on soil surface under different factors: evaluation and modeling DOI Creative Commons
Qian Wang,

Jianmin Bian,

Dongmei Ruan

et al.

Sustainable Environment Research, Journal Year: 2024, Volume and Issue: 34(1)

Published: Aug. 6, 2024

Abstract The volatilization of volatile organic compounds following a leakage event is crucial mechanism that influences their migration and transformation in the soil. It noteworthy this process intricately shaped by soil properties environmental factors, exhibiting highly complex nonlinear relationships. However, there currently no reliable mathematical model to predict relationship. To address gap, study conducted dynamic experiments considering various including particle size, matter content, temperature, wind speed moisture content. rate ( $$k$$ k ), an important parameter kinetics reflecting volatilization, was calculated first-order kinetic principle. Finally, innovative approach introduced using Back Propagation Neural Network (BPNN) for prediction. findings indicate exerts most significant impact on benzene among examined factors. application BPNN demonstrates model's accuracy simulating rates under diverse conditions. results K-fold cross-validation alleviate concerns potential over-prediction, affirming reliability constructed model. This research introduces novel methodology predicting parameters real-world scenarios.

Language: Английский

Artificial intelligence for life sciences: A comprehensive guide and future trends DOI

Ming Luo,

Wenyu Yang, Long Bai

et al.

The Innovation Life, Journal Year: 2024, Volume and Issue: unknown, P. 100105 - 100105

Published: Jan. 1, 2024

<p>Artificial intelligence has had a profound impact on life sciences. This review discusses the application, challenges, and future development directions of artificial in various branches sciences, including zoology, plant science, microbiology, biochemistry, molecular biology, cell developmental genetics, neuroscience, psychology, pharmacology, clinical medicine, biomaterials, ecology, environmental science. It elaborates important roles aspects such as behavior monitoring, population dynamic prediction, microorganism identification, disease detection. At same time, it points out challenges faced by application data quality, black-box problems, ethical concerns. The are prospected from technological innovation interdisciplinary cooperation. integration Bio-Technologies (BT) Information-Technologies (IT) will transform biomedical research into AI for Science paradigm.</p>

Language: Английский

Citations

9
Multitask Deep Learning Model Reveals Oils and Phenols Co-Adsorption Effect in Coal Chemical Wastewater: Breaking the Bottleneck of Selective Adsorption Separation DOI

Zhuangzhuang Yang,

Yongjun Liu, Zhe Liu

et al.

Published: Jan. 1, 2025

Adsorption technology is a green, low-carbon approach, but its main challenge in industrial applications selectively separating target pollutants. This paper investigates the adsorption of 66 oils and phenols (OPs) coal chemical wastewater effluent. A multitask deep learning (MTDL) model was developed to analyze time distribution properties, revealing co-adsorption mechanisms OPs complex systems. The results showed that BTEX, phenols, NHCs, PAHs, alkanes adsorb this order composite pollution system. comprehensive evaluation rate capacity using MTDL demonstrated high robustness (R2>0.96, RMSE<0.16). associated shapley additive explanations values partial dependence plot analyses indicated molecular concentration, weight, complexity, ratio, carbon percentage, carbon/hydrogen ratio were most vital variables affecting adsorption. Additionally, competitive on adsorbent surface as well synergistic mechanism involving numerous interacting forces, has been clarified. Based these findings, selective strategy proposed experimentally validated, showing separation efficiencies for OPs: BTEX (67.79%), Phenols (78.31%), NHCs (43.39%), PAHs (52.78%), Alkanes (61.41%). These findings offer theoretical insights into guide engineering design recovery OPs.

Language: Английский

Citations

0
Machine learning-driven predictive frameworks for optimizing chemical strategies in Microcystis aeruginosa mitigation DOI

Zobia Khatoon,

Suiliang Huang,

Adeel Ahmed Abbasi

et al.

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 71, P. 107235 - 107235

Published: Feb. 12, 2025

Language: Английский

Citations

0
Multi-field coupled migration characteristics of heat and mass in the process of in-situ thermal remediation of organic contaminated soil DOI Creative Commons
Guangchang Yang, Mengying Zhou, Lu Yu

et al.

Case Studies in Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 106100 - 106100

Published: April 1, 2025

Language: Английский

Citations

0
Prediction of BTEX volatilization in polluted soil based on the sorption potential energy theory DOI
Yongxiang Han, Yaqi Sheng, Jiating Zhao

et al.

Environmental Pollution, Journal Year: 2024, Volume and Issue: 360, P. 124624 - 124624

Published: July 27, 2024

Citations

2
Volatilization of benzene on soil surface under different factors: evaluation and modeling DOI Creative Commons
Qian Wang,

Jianmin Bian,

Dongmei Ruan

et al.

Sustainable Environment Research, Journal Year: 2024, Volume and Issue: 34(1)

Published: Aug. 6, 2024

Abstract The volatilization of volatile organic compounds following a leakage event is crucial mechanism that influences their migration and transformation in the soil. It noteworthy this process intricately shaped by soil properties environmental factors, exhibiting highly complex nonlinear relationships. However, there currently no reliable mathematical model to predict relationship. To address gap, study conducted dynamic experiments considering various including particle size, matter content, temperature, wind speed moisture content. rate ( $$k$$ k ), an important parameter kinetics reflecting volatilization, was calculated first-order kinetic principle. Finally, innovative approach introduced using Back Propagation Neural Network (BPNN) for prediction. findings indicate exerts most significant impact on benzene among examined factors. application BPNN demonstrates model's accuracy simulating rates under diverse conditions. results K-fold cross-validation alleviate concerns potential over-prediction, affirming reliability constructed model. This research introduces novel methodology predicting parameters real-world scenarios.

Language: Английский

Citations

0