ArticleScientific reports2025
An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach.
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 10 papers.
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Who cites it
10 citing papers in PubMed.
- Biomass-Derived Activated Carbon for Congo Red Dye Adsorption: Machine-Learning-Based Prediction and Comparative Evaluation.ACS omega · 2026Article
- The role of green chemistry in the transformation of agro-industrial wastes for health and environment: a review.Biodegradation · 2026Review
- Sustainable methylene blue dye removal via bio-derived micro/micron-sized porous particles Zygophyllum coccineum and Calotropis procera: A machine learning-assisted study.Scientific reports · 2026Article
- Effective and sustainable methylene blue removal from wastewater using palm kernel cake.Scientific reports · 2026Article
- Comparative analysis of ANFIS, ANN, and BBD for enhanced prediction of methyl orange adsorption in water treatment.Scientific reports · 2026Article
- Isotherm, kinetic, and thermodynamic insights into textile effluent remediation using acid-treated sugarcane peel.Scientific reports · 2026Article
- A drift-aware RSScientific reports · 2026Article
- Modeling studies of the adsorption of Methyl Red and Acid Yellow 36 dyes by sulphonated Ulva lactuca carbon.Scientific reports · 2025Article
- Valorization ofACS omega · 2025Article
- Mechanistic insights into methylene blue biodegradation by Tetradesmus obliquus: a multimodal approach using absorption, fluorescence, and square wave voltammetry.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and response surface methodology (RSM). To evaluate the adsorbent's potential, assessments were conducted using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The evaluation of RSM, ANN, and ANFIS included the quantification of R
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