A simple approach for prediction of Henry’s law constant of pesticides, solvents, aromatic hydrocarbons, and persistent pollutants without using complex computer codes and descriptors

Keshavarz, Mohammad Hossein; Rezaei, Mehdi; Hosseini, Seyyed Hesamodin

doi:10.1016/j.psep.2022.04.045

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…In cloud processing, where secondary organic aerosols are incorporated into cloud droplets, the partitioning of secondary organic aerosols and organic compounds between atmospheric particles, gas, and aqueous phases is characterized by HLC [4]. Experimentally determining HLC values is limited by factors such as solute adsorption in experimental systems, detection limits of analytical systems, potential interferences from impurities, and the challenges associated with measuring low concentrations [5][6][7]. Consequently, reliable estimation and prediction methods for determining HLC are critical.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for the Estimation of Henry’s Law Constant Based on Molecular Descriptors

Ullah,

Shaheryar,

Lim

2024

Atmosphere

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In atmospheric chemistry, the Henry’s law constant (HLC) is crucial for understanding the distribution of organic compounds across gas, particle, and aqueous phases. Quantitative structure–property relationship (QSPR) models described in scientific research are generally tailored to specific groups or categories of substances and are often developed using a limited set of experimental data. This study developed a machine learning model using an extensive dataset of experimental HLCs for approximately 1100 organic compounds. Molecular descriptors calculated using alvaDesc software (v 2.0) were used to train the models. A hybrid approach was adopted for feature selection, ensuring alignment with the domain knowledge. Based on the root mean squared error (RMSE) of the training and test data after cross-validation, Gradient Boosting (GB) was selected as a model for predicting HLC. The hyperparameters of the selected model were optimized using the automated hyperparameter optimization framework Optuna. The impact of features on the target variable was assessed using the SHapley Additive exPlanations (SHAP). The optimized model demonstrated strong performance across the training, evaluation, and test datasets, achieving coefficients of determination (R2) of 0.96, 0.78, and 0.74, respectively. The developed model was used to estimate the HLC of compounds associated with carbon capture and storage (CCS) emissions and secondary organic aerosols.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for the Estimation of Henry’s Law Constant Based on Molecular Descriptors

Ullah,

Shaheryar,

Lim

2024

Atmosphere

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“…However, in the current research on negative pressure deamination, this technology requires a high vacuum degree of 70–100 kPa to maintain good ammonia removal efficiency, and there is a significant high energy consumption input . Generally speaking, negative pressure deamination technology utilizes Henry’s law equilibrium and the movement of ammonia nitrogen dissociation equilibrium to promote the release and transfer of ammonia . From the perspective of enhancing mass transfer through the impact of porous jets, the jet impingement-negative pressure reactor was proposed to get a favorable deamination efficiency with a vacuum degree maintained at only 10–30 kPa .…”

Section: Introductionmentioning

confidence: 99%

CFD Simulation Investigation on the Flow Field Distribution Pattern under Different Operating Environment Pressures in a Jet Impingement-Negative Pressure Reactor

Dong,

Qiu,

Chai

2023

ACS Omega

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A jet impingement-negative pressure deamination reactor (JI-NPDR) has excellent ammonia removal efficiency. The CFD numerical simulation method based on the Euler−Euler model and the realizable k−ε turbulence model was used to investigate the effect of different negative pressures at the reactor top outlet on the distribution pattern of pressure, velocity, turbulent kinetic energy, and vortex. The results indicate that the overall water flow distribution in the reactor increases in axial cohesion with the increase in negative pressure. The scattered small eddies gradually connect to a large eddy current as a whole, and the small eddies generated in the jet area also become regular and flat with the increase of negative pressures. These findings can provide detailed information for the study of flow patterns in a jet impingement-negative pressure reactor.

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The use of simple structural parameters of organic compounds to assess their PUF-air partition coefficients

Keshavarz,

Shirazi,

Jafari

et al. 2024

Chemosphere

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A simple approach for prediction of Henry’s law constant of pesticides, solvents, aromatic hydrocarbons, and persistent pollutants without using complex computer codes and descriptors

Cited by 8 publications

References 29 publications

Machine Learning Approach for the Estimation of Henry’s Law Constant Based on Molecular Descriptors

Machine Learning Approach for the Estimation of Henry’s Law Constant Based on Molecular Descriptors

CFD Simulation Investigation on the Flow Field Distribution Pattern under Different Operating Environment Pressures in a Jet Impingement-Negative Pressure Reactor

The use of simple structural parameters of organic compounds to assess their PUF-air partition coefficients

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