Advancing wastewater treatment: removal of methylene blue using sustainable low-cost activated carbon—comparative and efficient prediction by ANN and ANFIS
廃水処理の高度化：持続可能な低コスト活性炭を用いたメチレンブルー除去—ANNとANFISによる比較・効率的予測 (AI 翻訳)
Sivaprakasam Anbazhagan, T. Venugopal, J. Aarthi, M. Dhineshkumar, M. Prabakaran, A. Selvaganapathi, P. Govindhan, D. Dhivya Priya, K. Vidhya, P. Suppuraj, C. Balakrishnan
🤖 gxceed AI 要約
日本語
ANNとANFISを用いて、Calotropis gigantea葉由来活性炭によるメチレンブルー除去効率を予測。接触時間が最重要因子であり、ANNがANFISより安定した汎化性能を示した。
English
ANN and ANFIS models were developed to predict methylene blue removal using activated carbon from Calotropis gigantea leaves. ANN showed superior generalization over ANFIS, with contact time as the most influential parameter.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の化学・繊維産業の廃水処理において、低コスト吸着材とAI予測の組み合わせはコスト削減に寄与し得る。ただし本論文は直接的にGX政策や開示基準と結びつくものではない。
In the global GX context
This study demonstrates the application of AI models (ANN and ANFIS) for optimizing sustainable wastewater treatment, which is relevant to global circular economy goals and water resource management.
👥 読者別の含意
🔬研究者:Provides a comparative evaluation of ANN and ANFIS for adsorption prediction, valuable for those developing AI models in environmental engineering.
🏢実務担当者:Offers insights into low-cost adsorbent and predictive modeling for dye removal, applicable to industrial wastewater treatment operations.
📄 Abstract（原文）
Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the removal efficiency of the cationic dye methylene blue from wastewater using activated carbon derived from Calotropis gigantea leaves (CGAC). The adsorbent was characterized by SEM-EDAX, FT-IR, XRD, BET, and XPS analyses. Batch adsorption studies showed that the equilibrium data were best described by the Freundlich isotherm, followed by Langmuir and Temkin models over the investigated temperature range, while the adsorption kinetics followed a pseudo-second-order model. A dataset comprising 128 experimental runs (100 for training and 28 for testing) was employed for model development using initial dye concentration, contact time, temperature, adsorbent dosage, and pH as input variables. Principal component analysis (PCA) was applied, but did not significantly alter model performance compared to the raw data. Among 14 ANN training functions and three transfer functions, the trainbr–tansig combination provided the highest predictive accuracy. In the ANFIS framework, the Gaussmf membership function with four memberships yielded optimal results. Although ANFIS achieved excellent training accuracy, ANN demonstrated more stable and reliable generalization across both training and testing datasets. Sensitivity analysis identified contact time as the most influential parameter governing dye removal. ANN and ANFIS are confirmed as effective modelling tools for predicting and optimizing dye adsorption by CGAC, with ANN showing superior robustness.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1186/s40543-026-00541-4first seen 2026-06-24 04:49:45
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