ArticleEnvironmental science and pollution research international2020
Response surface methodology and artificial neural network for remediation of acid orange 7 using TiO
Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 40 citations in OpenAlex.
- Machine learning modeling to predict the photocatalytic degradation rate constant in wastewater using TiOScientific reports · 2026Article
- Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging.PloS one · 2024Article
- Article
- Use and treatment of chicken feathers as a natural adsorbent for the removal of copper in aqueous solution.Journal of environmental health science & engineering · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The primary responsibility for continuously discharging toxic organic pollutants into water bodies and open environments is the increase in industrial and agricultural activities. Developing economical and suitable methods to continuously remove organic pollutants from wastewater is highly essential. The aim of the present research was to apply response surface methodology (RSM) and artificial neural networks (ANNs) for optimization and modeling of photocatalytic degradation of acid orange 7 (AO7) by commercial TiO
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Registered trials
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