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Machine Learning in Green Chemistry
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Machine Learning in Green Chemistry

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Green Chemistry".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4170

Special Issue Editors

Dr. Tu Le

E-Mail Website
Guest Editor
School of Engineering, RMIT University, Melbourne, VIC 3001, Australia
Interests: machine learning; advanced materials; renewable energy; advanced therapeutics; sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Haoxin Mai

E-Mail Website
Guest Editor Assistant
School of Science, RMIT University, Melbourne, VIC 3001, Australia
Interests: photocatalysis; water splitting; machine learning

Special Issue Information

Dear Colleagues,

Machine learning has emerged as a powerful tool in green chemistry. It is a data-driven approach harnessing data analysis and computational models to revolutionize the design, optimization, and assessment of sustainable chemical processes. Through predictive modeling, pattern recognition, and informed decision making, machine learning techniques contribute to the development of environmentally friendly and economically viable chemical solutions. In chemical synthesis, machine learning models not only facilitate the prediction of reaction outcomes, but also optimize reaction conditions, minimize waste, and reduce energy consumption. In toxicity prediction and assessment, by analyzing chemical structures and their associated toxicity profiles, machine learning helps in identifying safer compounds and ensuring the development of environmentally friendly products. In addition, machine learning contributes to solvent selection, recommending greener alternatives that have lower environmental impact and reduced health hazards. Therefore, the integration of machine learning and green chemistry holds promise for a more sustainable future. By accelerating the development of ecologically sound processes, optimizing resource utilization, and minimizing detrimental effects on the environment, machine learning is driving the paradigm shift towards greener and more sustainable chemical practices.

Dr. Tu Le
Guest Editor

Dr. Haoxin Mai
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • environment
  • green solvents
  • sustainability
  • renewable energy
  • catalysis
  • carbon reduction
  • machine learning
  • green synthesis

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Published Papers (3 papers)

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Research

20 pages, 3404 KiB  
Article
Prediction of Solvent Composition for Absorption-Based Acid Gas Removal Unit on Gas Sweetening Process
by Mochammad Faqih, Madiah Binti Omar, Rafi Jusar Wishnuwardana, Nurul Izni Binti Ismail, Muhammad Hasif Bin Mohd Zaid and Kishore Bingi
Molecules 2024, 29(19), 4591; https://doi.org/10.3390/molecules29194591 - 27 Sep 2024
Viewed by 968
Abstract
The gas sweetening process is essential for removing harmful acid gases, such as hydrogen sulfide (H2S) and carbon dioxide (CO2), from natural gas before delivery to end-users. Consequently, chemical absorption-based acid gas removal units (AGRUs) are widely implemented due [...] Read more.
The gas sweetening process is essential for removing harmful acid gases, such as hydrogen sulfide (H2S) and carbon dioxide (CO2), from natural gas before delivery to end-users. Consequently, chemical absorption-based acid gas removal units (AGRUs) are widely implemented due to their high efficiency and reliability. The most common solvent used in AGRU is monodiethanolamine (MDEA), often mixed with piperazine (PZ) as an additive to accelerate acid gas capture. The absorption performance, however, is significantly influenced by the solvent mixture composition. Despite this, solvent composition is often determined through trial and error in experiments or simulations, with limited studies focusing on predictive methods for optimizing solvent mixtures. Therefore, this paper aims to develop a predictive technique for determining optimal solvent compositions under varying sour gas conditions. An ensemble algorithm, Extreme Gradient Boosting (XGBoost), is selected to develop two predictive models. The first model predicts H2S and CO2 concentrations, while the second model predicts the MDEA and PZ compositions. The results demonstrate that XGBoost outperforms other algorithms in both models. It achieves R2 values above 0.99 in most scenarios, and the lowest RMSE and MAE values of less than 1, indicating robust and consistent predictions. The predicted acid gas concentrations and solvent compositions were further analyzed to study the effects of solvent composition on acid gas absorption across different scenarios. The proposed models offer valuable insights for optimizing solvent compositions to enhance AGRU performance in industrial applications. Full article
(This article belongs to the Special Issue Machine Learning in Green Chemistry)
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15 pages, 5076 KiB  
Article
Identification and Removal of Pollen Spectral Interference in the Classification of Hazardous Substances Based on Excitation Emission Matrix Fluorescence Spectroscopy
by Pengjie Zhang, Bin Du, Jiwei Xu, Jiang Wang, Zhiwei Liu, Bing Liu, Fanhua Meng and Zhaoyang Tong
Molecules 2024, 29(13), 3132; https://doi.org/10.3390/molecules29133132 - 1 Jul 2024
Viewed by 1037
Abstract
Sensitively detecting hazardous and suspected bioaerosols is crucial for safeguarding public health. The potential impact of pollen on identifying bacterial species through fluorescence spectra should not be overlooked. Before the analysis, the spectrum underwent preprocessing steps, including normalization, multivariate scattering correction, and Savitzky–Golay [...] Read more.
Sensitively detecting hazardous and suspected bioaerosols is crucial for safeguarding public health. The potential impact of pollen on identifying bacterial species through fluorescence spectra should not be overlooked. Before the analysis, the spectrum underwent preprocessing steps, including normalization, multivariate scattering correction, and Savitzky–Golay smoothing. Additionally, the spectrum was transformed using difference, standard normal variable, and fast Fourier transform techniques. A random forest algorithm was employed for the classification and identification of 31 different types of samples. The fast Fourier transform improved the classification accuracy of the sample excitation–emission matrix fluorescence spectrum data by 9.2%, resulting in an accuracy of 89.24%. The harmful substances, including Staphylococcus aureus, ricin, beta-bungarotoxin, and Staphylococcal enterotoxin B, were clearly distinguished. The spectral data transformation and classification algorithm effectively eliminated the interference of pollen on other components. Furthermore, a classification and recognition model based on spectral feature transformation was established, demonstrating excellent application potential in detecting hazardous substances and protecting public health. This study provided a solid foundation for the application of rapid detection methods for harmful bioaerosols. Full article
(This article belongs to the Special Issue Machine Learning in Green Chemistry)
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14 pages, 3304 KiB  
Article
Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms
by Pengjie Zhang, Bing Liu, Xihui Mu, Jiwei Xu, Bin Du, Jiang Wang, Zhiwei Liu and Zhaoyang Tong
Molecules 2024, 29(1), 197; https://doi.org/10.3390/molecules29010197 - 29 Dec 2023
Cited by 3 | Viewed by 1612
Abstract
Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky–Golay smoothing (SG), and wavelet transform [...] Read more.
Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky–Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices. Full article
(This article belongs to the Special Issue Machine Learning in Green Chemistry)
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