ArticleScientific reports2025
Exploring the adsorption desulfurization efficiency using RSM and ANN methodologies.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Optimization of fermentation conditions and enzymatic properties of extracellular lipase produced by Serratia marcescens derived from food waste.World journal of microbiology & biotechnology · 2026Article
- Comprehensive characterization and RSM-based optimization of crystal violet adsorption using SnClRSC advances · 2026Article
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Authors and funding
3 authors.
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Abstract
Zeolites, known for their extensive surface area and customizable adsorption characteristics, demonstrate significant efficiency in adsorptive desulfurization. This research investigates the application of Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) for modeling and optimizing the sulfur adsorption performance of modified zeolites. Key structural and operational parameters were investigated, including surface area, micropore volume, temperature, time, and sulfur compound molecular weight. Using the central composite design (CCD), RSM modeled the adsorption process by fitting experimental data through least-squares regression, providing valuable insights into parameter effects. The quadratic model achieved an adjusted correlation coefficient (R
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