壳聚糖-溶解-果胶气凝胶去除亚甲基蓝:用人工神经元网络建模和优化

IF 2.8 Q2 ENGINEERING, CHEMICAL ChemEngineering Pub Date : 2023-09-11 DOI:10.3390/chemengineering7050082
Jean Flores-Gómez, Mario Villegas-Ruvalcaba, José Blancas-Flores, Juan Morales-Rivera
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引用次数: 0

摘要

本研究采用冷冻干燥技术,通过溶胶-凝胶反应,制备了一种新型壳聚糖-溶解-果胶气凝胶(Cs - r - P),并将其用于亚甲基蓝(MB)的脱除。此外,利用一种被称为人工神经网络(ANN)的人工智能技术,对这种材料进行了建模和优化。用FTIR和XPS表征了其物理形态和化学成分,并对其吸附性能进行了分析。为了模拟吸附过程,使用了三个主要参数:壳聚糖-分解果胶浓度(45-75%),热处理(6-36 h)和已知亚甲基蓝浓度(25-50和100 mg/L),建立在Box-Behnken设计上。将人工神经网络与改进的灰狼优化(IWGO)元启发式算法相结合,相关系数R2 = 0.99。表征结果表明,该气凝胶表面具有微孔和介孔结构,溶质具有良好的物理稳定性,多糖碱提供了吸附染料所需的官能团;气凝胶是成功的染料吸附剂,其吸附量为12.44 mg/g。最后,通过准二级吸附动力学确定了吸附的物理和化学性质。从化学成分上看,通过阳离子交换和氢结合,明显发生了甲基溴的吸附。采用灰狼优化的人工神经网络预测去除MB的最佳操作参数,在CS-R-P气凝胶浓度(52/30/18)、热处理时间(9.12 h)和亚甲基蓝初始浓度(37 mg/L)下,去除率为94.6%。这些结论表明,利用人工智能(如人工神经网络)可以为最大化壳聚糖新型气凝胶的去除作用提供一个高效实用的模型。
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Chitosan–Resole–Pectin Aerogel in Methylene Blue Removal: Modeling and Optimization Using an Artificial Neuron Network
In this study, a novel chitosan–resole–pectin aerogel (CS–R–P) was created from a sol–gel reaction with a solution of Cs and P with resole by a freeze-drying technique, and this adsorbent was proposed for the removal of methylene blue (MB). In addition, with the use of an artificial intelligence technique known as an artificial neural network (ANN), this material was modeled and optimized. Its physical morphology and chemical composition were also characterized with FTIR and XPS, and its adsorption properties were analyzed. For modeling the adsorption process, three main parameters were used: the chitosan–resole–pectin concentration (45–75%), thermal treatment (6–36 h), and known concentrations of methylene blue (25–50 and 100 mg/L), established on the Box–Behnken design. The ANN was coupled with the improved gray wolf optimization (IWGO) metaheuristic algorithm, achieving a correlation coefficient of R2 = 0.99. The characterization indicates that the surface of the aerogels was micro- and mesoporous, the resole gave physical stability, and the polysaccharide base delivered the functional groups necessary for dye adsorption; the aerogels were successful dye adsorbents with a qe of 12.44 mg/g. Finally, the physical and chemical sorption was ascertainable with an adsorption that followed pseudo-second-order kinetics. The MB adsorption was clearly occurring though cation exchange and hydrogen binding as observed in the chemical composition. The ANN with the gray wolf optimizer was used for the prediction of the best operating parameters for MB removal, applying the following conditions—the CS–R–P aerogel concentration (52/30/18), the thermal treatment (9.12 h), and the initial concentration of methylene blue (37 mg/L)—achieving a 94.6% removal. These conclusions suggest that using artificial intelligence such as an ANN can provide an efficient and practical model for maximizing the removal action of new aerogels based on chitosan.
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来源期刊
ChemEngineering
ChemEngineering Engineering-Engineering (all)
CiteScore
4.00
自引率
4.00%
发文量
88
审稿时长
11 weeks
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