A convolutional neural networks approach using X-Ray absorption images for studying granular activated carbon

Torres, Jeamichel Puente; Codorniú, Rafael Trujillo; Baracaldo, Rene López; Sariol, Harold Crespo; Peacok, Thayset Mariño; Yperman, Jan; Adriaensens, Peter; Carleer, Robert; Sauvanell, Ángel Brito

doi:10.1007/s42452-020-03835-3

Cited by 4 publications

(11 citation statements)

References 14 publications

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“…XRA method [3,[15][16][17] uses digital image processing on digital X-Ray radiography. X-ray radiography experiments were conducted using a TOSHIBA Mamorez-mgu 100d X-ray equipment used for mammography studies in manual mode, according to [16].…”

Section: X-ray Absorption Experiments (Xra)mentioning

confidence: 99%

“…X-ray radiography experiments were conducted using a TOSHIBA Mamorez-mgu 100d X-ray equipment used for mammography studies in manual mode, according to [16]. The mathematical analysis of the digital X-Ray radiographic images and obtaining the image's histograms and frequency spectra were performed using dedicated software developed in MATLAB® specifically for this application [3,[15][16][17].…”

Section: X-ray Absorption Experiments (Xra)mentioning

confidence: 99%

“…The performance of both acids for regeneration has been compared using different analytical techniques such as TGA and ICP-AES, SEM and RSSCT (for obtaining breakthrough curves), as well as a new X-ray absorption method (XRA) [3,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Chemical Regeneration of Activated Carbon Used in A Water Treatment System for Medical Services

Torres¹,

Sariol²,

Yperman³

et al. 2023

Adv Environ Eng Res

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The chemical regeneration of exhausted granular activated carbon (GAC) from a water treatment plant to produce dialysis water used in hemodialysis treatments for chronic renal disease patients from a general Hospital of Ciego de Avila province, Cuba, was investigated. Activated carbon (AC) exhausted mainly by inorganics (Ca (Ca-chelates), Mg and Na) was regenerated using hydrochloric acid and acetic acid (one regeneration cycle). Solutions of 5%, 10%, 15% and 20%(v/v) as well as four contact times (2 h, 4 h, 6 h and 48 h) for hydrochloric acid and three contact times (2 h, 4 h and 6 h) for acetic acid at 25°C and 1 atm in a dosage of 1 g GAC/10 mL were used. Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES), thermogravimetric analysis (TGA), X-ray absorption technique (XRA) and Scanning Electronic Microscopy (SEM) were used to evaluate the effect of the regeneration on exhausted GAC. Batch and scaled column experiments were developed. Breakthrough curves were obtained to assess the AC’s performances after chemical regeneration. The removal/adsorption capacity towards free chlorine (as disinfection agent) and hardness ions (expressed in mg CaCO<sub>3</sub>/L) was estimated using the area under the breakthrough curves from scaled columns experiments. The hydrochloric acid arises as the best acidic regenerator; using a concentration of 20%(v/v), regenerated GACs using hydrochloric acid showed the highest desorption rate of inorganics as well as a free chlorine removal performance of around 30% concerning the virgin GAC sample. A new GAC management scheme is proposed for GACs used in the medical industry to improve the sustainability and economics of the water treatment process.

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Section: X-ray Absorption Experiments (Xra)mentioning

confidence: 99%

Section: X-ray Absorption Experiments (Xra)mentioning

confidence: 99%

See 1 more Smart Citation

Chemical Regeneration of Activated Carbon Used in A Water Treatment System for Medical Services

Torres¹,

Sariol²,

Yperman³

et al. 2023

Adv Environ Eng Res

Self Cite

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“…For adsorbent materials investigations CNNs have been trained to extract the chemical and physical characteristics from their topology images or diverse spectra. [107][108][109][110][111] Finally, the accuracy of the model predictions must be evaluated. For regression models, the error metrics are the coefficient of determination (r 2 ), root mean square error (RMSE), mean ab-solute error (MAE), and mean absolute percentage error (MAPE).…”

Section: Developing Machine Learning Modelsmentioning

confidence: 99%

“…For adsorbent materials investigations CNNs have been trained to extract the chemical and physical characteristics from their topology images or diverse spectra. [ 107 , 108 , 109 , 110 , 111 ]…”

Section: Developing Machine Learning Modelsmentioning

confidence: 99%

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Mai

Chen

et al. 2022

Advanced Science

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Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.

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Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal–Organic Framework Space

Xin,

Wang,

Zhang

et al. 2024

ACS Nano

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Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal− organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs.

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A convolutional neural networks approach using X-Ray absorption images for studying granular activated carbon

Cited by 4 publications

References 14 publications

Chemical Regeneration of Activated Carbon Used in A Water Treatment System for Medical Services

Chemical Regeneration of Activated Carbon Used in A Water Treatment System for Medical Services

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal–Organic Framework Space

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