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Evaluation of relative importance of ultrasound reactor parameters for the removal of estrogen hormones in water

机译:超声反应器参数对去除水中雌激素的相对重要性的评估

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The growing interest in sonochemistry as a tool for environmental remediation leads to the need for process optimization. Sonochemistry is a complex process, which depends on physical parameters and also on the process conditions. Physical parameters are interrelated and therefore a systematic approach has to be taken to optimize the process. The effect of physical parameters on the destruction of seven estrogen hormones (17α-estradiol, 17β-estradiol, estriol, 17α-ethinylestradiol, 17α- dihydroequilin, estrone and equilin) is reported in this study. Artificial neural networks (ANN) was used as a tool to identify the correlations between these process parameters. ANN enabled the establishment of relationship between sonication parameters such as power density, power intensity, ultrasound amplitude, as well as the reactor design parameters. The major significance was attributed to the area-specific power density and the volume-specific power intensity. The results of this work provide a sound basis to design pilot and full-scale ultrasound treatment systems. Process optimization lead to a 5-fold decrease in energy consumption as compared to the commercially available reactors, thereby making the process attractive for field applications.
机译:对声化学作为环境修复工具的兴趣日益浓厚,因此需要进行工艺优化。声化学是一个复杂的过程,它取决于物理参数以及工艺条件。物理参数是相互关联的,因此必须采用系统的方法来优化过程。这项研究报道了物理参数对破坏七种雌激素(17α-雌二醇,17β-雌二醇,雌三醇,17α-乙炔雌二醇,17α-二氢安息香,雌酮和雌马酚)的影响。人工神经网络(ANN)被用作识别这些过程参数之间相关性的工具。人工神经网络可以建立超声参数之间的关系,例如功率密度,功率强度,超声振幅以及反应堆设计参数。主要意义归因于面积比功率密度和体积比功率强度。这项工作的结果为设计中试和全尺寸超声治疗系统提供了良好的基础。与市售反应器相比,工艺优化导致能耗降低了5倍,从而使该工艺对现场应用具有吸引力。

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