Document Type : ORIGINAL RESEARCH ARTICLE
Authors
Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India
Abstract
BACKGROUND AND OBJECTIVES: The prediction models, response surface methodology and adaptive neuro-fuzzy inference system are utilized in this study. This study delves into the removal efficiency of reactive orange 16 using hydrochar derived from the Prosopis juliflora roots. Hydrochar dose, pH, temperature, and initial reactive orange 16 concentration were studied in batch processes. The correlation coefficients for the batch processes were found to be 0.978 and 0.9999. The results denote that the adaptive neuro-fuzzy inference system predicted the reactive orange 16 removal efficiency more accurately than the response surface methodology model.
METHODS: Prosopis juliflora roots roots are converted into hydrochar to remove azo dye from textile waste water. Prosopis juliflora roots roots were collected from Ramanad District, Southern Tamil Nadu, India. The moisture content was lowered by drying for 24 hours at 103 degree celcius in an oven with hot air. This biomass was thermally destroyed at 300 degree celcius for 15 minutes without oxygen in an autoclave in a muffle furnace (heating rate: 5 degree celcius per minute). As soon as it reaches room temperature, the hydrochar residue of this biomass was used for adsorption investigations. The batch adsorption process was conducted for 6 hours in a 250 milliliter Erlenmeyer conical flask with a 100 milliliter working volume using an orbital shaker. The pH, dosage, concentration, and temperature are the four parameters chosen for this study to find the maximum removal efficiency of the dye from aqueous solutions. This study validated adaptive neuro-fuzzy inference system, an artificial neural network with a fuzzy inference system, using response surface methodology projected experimental run with Box–Behnken method.
FINDINGS: The adaptive neuro-fuzzy inference system model is created alongside the response surface methodology model to compare experimental outcomes. Experimental data was evaluated using a hybrid least square and gradient technique. Statistical and residual errors assessed experimental and mathematical model correctness. Experimental data matched the adaptive neuro-fuzzy inference system results. Statistical error analysis verified the model’s accuracy and precision against experimental data.
CONCLUSION: Response surface methodology and adaptive neuro-fuzzy inference system optimized process conditions. At pH 2, 2 gram per litre hydrochar dosage, 35 degree celcius , and a reactive orange 16 starting concentration of 250 milligram per liter, removal effectiveness reached 86.1 percent. Adaptive neuro-fuzzy inference system predicted higher values than response surface methodology, with batch correlation coefficients of 0.9999 and 0.9997, respectively. Mathematical techniques can accurately estimate dye removal efficiency from aqueous solutions.
Graphical Abstract
)
Highlights
- The ANFIS produced all FIS-required fuzzy data;
- Capacity were determined using parametric analysis;
- At pH 2, 2 g/L hydrochar dosage, 35 °C temperature, and 250 mg/L starting Reactive Orange 16 concentration, removal effectiveness reached 86.1%;
- ANFIS predicted higher values than RSM, with batch correlation coefficients of 0.9999 and 0.9997.
Keywords
- Adaptive neuro-fuzzy inference system
- Hydrochar
- Reactive orange-16 (RO 16)
- Response surface methodology
- Statistical error analysis
Main Subjects
OPEN ACCESS
©2023 The author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third-party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit:
http://creativecommons.org/licenses/by/4.0/
PUBLISHER NOTE
GJESM Publisher remains neutral concerning jurisdictional claims in published maps and institutional affiliations.
CITATION METRICS & CAPTURES
Google Scholar | Scopus | Web of Science | PlumX Metrics | Altmetrics | Mendeley |
CURRENT PUBLISHER
GJESM Publisher
)
Letters to Editor
[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.
[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.
[3] Letters can be no more than 300 words in length.
[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.
[5] Anonymous letters will not be considered.
[6] Letter writers must include their city and state of residence or work.
[7] Letters will be edited for clarity and length.
Send comment about this article