聚乙烯醇
亚甲蓝
吸附
羧甲基纤维素
化学工程
自愈水凝胶
废水
膨润土
甲基蓝
水溶液
材料科学
纤维素
石墨烯
化学
复合材料
有机化学
纳米技术
环境工程
环境科学
光催化
钠
催化作用
工程类
作者
Ali Hosin Alibak,Mohsen Khodarahmi,Pooya Fayyazsanavi,Seyed Mehdi Seyed Alizadeh,Arkan Jasim Hadi,Elnaz Aminzadehsarikhanbeglou
标识
DOI:10.1016/j.jclepro.2022.130509
摘要
Almost all industries produce a large quantity of dye-contaminated wastewater. Wastewaters containing a high dosage of methylene blue (MB) are a menace for human beings, the environment, and the ecosystem. Thus, the MB molecules are needed to be removed before wastewater discharge to the environment. Adsorption is the most well-known process for dye removal from water and wastewater. This study focuses on the intelligent simulation of the MB removal by the bio-based hydrogel. Indeed, the cascade correlation neural network (CCNN) employs to simulate the adsorption mechanism of MB molecules by the bio-based (polyvinyl alcohol/carboxymethyl cellulose) hydrogel reinforced by graphene oxide nanoparticles and bentonite. The Levenberg-Marquardt algorithm trains the CCNN to estimate the hydrogel capacity for MB uptake as a function of adsorbent type, temperature, initial dye concentration, pH, and contact time. Trial-and-error analyses justified that the CCNN with one hidden layer containing six neurons is the most reliable model for the given problem. This model predicts the collected experimental data from the literature with excellent agreement (i.e., RMSE = 2.00, AARD = 2.4%, and R2 = 0.9980). Experimental measurements and modeling findings approved that MB uptake at 30–40 °C intensifies by increasing temperature, contact time, pH, and initial dye concentration. Increasing the mobility of the large dye ions at 50 °C reduces the adsorption capacity of all bio-adsorbents. Furthermore, the reinforced bio-based hydrogel with bentonite/GO nanoparticles is the best adsorbent for the methylene blue uptake.
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