 | Up a level |
Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
Use the mouse wheel or scroll gestures to zoom into the graph.
You can click on the nodes and links to highlight them and move the nodes by dragging them.
Hold down the "Ctrl" key or the "⌘" key while clicking on the nodes to open the list of this person's publications.
Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Al-Sakkari, E. G., Ragab, A., Amer, M., Ajao, O., Benali, M., Boffito, D. C., Dagdougui, H., & Amazouz, M. (2024). Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection. Digital Chemical Engineering, 14, 100207 (26 pages). External link
Al-Sakkari, E. G., Ragab, A., Ali, M., Dagdougui, H., Boffito, D. C., & Amazouz, M. (2024, July). Learn-To-Design: Reinforcement Learning-Assisted Chemical Process Optimization [Paper]. Foundations of Computer Aided Process Design (FOCAPD 2024), Breckenridge, Colorado, USA. Published in Systems and Control Transactions, 3. Available
Al-Sakkari, E. G., Ragab, A., Dagdougui, H., Boffito, D. C., & Amazouz, M. (2024). Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities. Science of the Total Environment, 917, 170085 (32 pages). External link
Al-Sakkari, E. G., Ragab, A., Ali, M., Dagdougui, H., Boffito, D. C., & Amazouz, M. (2024, July). Learn-to-design : reinforcement learning-assisted chemical process optimization [Paper]. 10th International Conference on Foundations of Computer Aid Process Design (FOCAPD 2024), Breckenridge, Colorado, USA. Published in Systems & Control Transactions, 3. External link
Al-Sakkari, E. G., Ragab, A., So, T. M. Y., Shokrollahi, M., Dagdougui, H., Navarri, P., Elkamel, A., & Amazouz, M. (2023). Machine learning-assisted selection of adsorption-based carbon dioxide capture materials. Journal of Environmental Chemical Engineering, 11(5), 110732 (25 pages). External link
Al-Sakkari, E. G., Elozeiri, A. A., Abdeldayem, O. M., Likozar, B., & Boffito, D. C. (2022). Fish and animal waste as catalysts for biodiesel synthesis. In Waste and Biodiesel (pp. 119-136). External link
Abdeldayem, O. M., Dabbish, A. M., Habashy, M. M., Mostafa, M. K., Elhefnawy, M., Amin, L., Al-Sakkari, E. G., Ragab, A., & Rene, E. R. (2022). Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook. Science of The Total Environment, 803, 24 pages. External link
Abdelmigeed, M. O., Al-Sakkari, E. G., Hefney, M. S., Ismail, F. M., Abdelghany, A., Ahmed, T. S., & Ismail, I. M. (2021). Magnetized ZIF-8 impregnated with sodium hydroxide as a heterogeneous catalyst for high-quality biodiesel production. Renewable Energy, 165, 405-419. External link
Al-Sakkari, E. G., Abdeldayem, O. M., El-Sheltawy, S. T., Abadir, M. F., Soliman, A., Rene, E. R., & Ismail, I. (2020). Esterification of high FFA content waste cooking oil through different techniques including the utilization of cement kiln dust as a heterogeneous catalyst: A comparative study. Fuel, 279, 11 pages. External link
Al-Sakkari, E. G., Abdeldayem, O. M., Genina, E. E., Amin, L., Bahgat, N. T., Rene, E. R., & El-Sherbiny, I. M. (2020). New alginate-based interpenetrating polymer networks for water treatment: a response surface methodology based optimization study. International Journal of Biological Macromolecules, 155, 772-785. External link
Dhawane, S. H., Al-Sakkari, E. G., & Yadav, D. (2021). Cost-effective viable solutions for existing technologies. In Unspecified (pp. 381-395). External link
Hosney, H., Al-Sakkari, E. G., & Mustafa, A. (2020). Kinetics and gibbs function studies on lipase-catalyzed production of non-phthalate plasticizer. Journal of Oleo Science, 69(7), 727-735. External link
Naeem, M. M., Al-Sakkari, E. G., Boffito, D. C., Rene, E. R., Gadalla, M. A., & Ashour, F. H. (2023). Single-stage waste oil conversion into biodiesel via sonication over bio-based bifunctional catalyst: Optimization, preliminary techno-economic and environmental analysis. Fuel, 341, 127587 (13 pages). External link
Naeem, M. M., Al-Sakkari, E. G., Boffito, D. C., Gadalla, M. A., & Ashour, F. H. (2021). One-pot conversion of highly acidic waste cooking oil into biodiesel over a novel bio-based bi-functional catalyst. Fuel, 283, 16 pages. External link
