Applying a network of high frequency ultrasonic transducers for removal of Reactive Red 120 dye from aqueous solution: experimental design and statistical analysis

Sajad Khorshidi, Akbar Mohammadidoust
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Abstract

In this study a network of high frequency ultrasonic’s transducers without additives was introduced for removing the Reactive Red 120 dye from aqueous solution. pH, irradiation time, initial concentration and number of piezoelectric were input variables at constant temperature of 25 °C. The results revealed that the ultrasonic waves played an important role in cracking the hydrocarbon bonds due to the cavitation phenomenon and OH° attacks. The effects of the variables and their interactions were investigated by the central composite design (CCD) method as one of the response surface methodologies (RSM). Maximum dye removal’s efficiency (76.05%) was attained at initial concentration of 5 mg/l, irradiation time of 50 min; pH 10 and 5 ultrasonic’s transducers. It was in a good agreement with the experimental, 78%. Finally, to more evaluates, the RSM model was compared to the artificial neural network (ANN) model. Performance’s functions reported that the RSM was better than the ANN in predicting the dye removal’s efficiency (R%).
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应用高频超声换能器网络从水溶液中去除活性红120染料:实验设计和统计分析
本文介绍了一种无添加剂高频超声换能器网络,用于去除水中的活性红120染料。在25℃恒温条件下,pH、辐照时间、初始浓度和压电片数量为输入变量。结果表明,由于空化现象和OH°攻击,超声波对烃键的断裂起着重要作用。作为响应面法(RSM)的一种,采用中心复合设计(CCD)方法研究了各变量及其相互作用的影响。当初始浓度为5 mg/l、辐照时间为50 min时,去除率最高(76.05%);pH值10和5超声波换能器。结果与实验结果一致,为78%。最后,将RSM模型与人工神经网络(ANN)模型进行了比较。性能函数表明,RSM比人工神经网络更能预测脱色效率(R%)。
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