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Kinetic modeling for phenol degradation using photo-impinging streams reactor

机译:使用光撞击流反应器的苯酚降解动力学模型

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In the present study, a novel kinetic model has been proposed for photocatalytic degradation of wastewater. In the first step, statistical experimental designs have been used to optimize the process of phenol degradation in a photo-impinging streams reactor. The crucial parameters, namely phenol concentration, catalyst loading, pH, and slurry flow rate, were selected for process optimization, applying central composite design. The analysis results indicated that interactions between catalyst loading and pH significantly affect phenol degradation. The predicted data showed that the maximum removal efficiency of phenol (99 %) could be obtained under the optimum operating conditions (phenol concentration = 50 mg l(-1), catalyst loading = 2.1 g l(-1), pH 6.2, and slurry flow rate = 550 ml min(-1)). These predicted values were then verified by certain validating experiments. Residence time distribution (RTD) of the slurry phase within the reactor was then measured using the impulse tracer method. A number of different assumptions were made, i.e., continuous stirred tank reactors (CSTRs) in series model and gamma distribution model with bypass (GDB). A comparison made between the sum of the square errors for experimental and predicted RTD values in case of each flow model revealed that both CSTRs in series model and GDB were proper descriptions for reactor behavior. The CSTRs in series model and RTD data were applied in conjunction with the phenol degradation kinetic model to predict the coefficients of the reaction rate.
机译:在本研究中,提出了一种新的动力学模型用于废水的光催化降解。第一步,已使用统计实验设计来优化光撞击流反应器中苯酚的降解过程。应用中心复合设计,选择了关键参数,即苯酚浓度,催化剂载量,pH和浆料流速,以进行工艺优化。分析结果表明,催化剂负载量与pH值之间的相互作用会显着影响苯酚的降解。预测数据表明,在最佳操作条件下(苯酚浓度= 50 mg l(-1),催化剂负载量= 2.1 gl(-1),pH 6.2和浆液),可以获得最大的苯酚去除效率(99%)。流速= 550毫升min(-1))。然后,通过某些验证实验来验证这些预测值。然后使用脉冲示踪法测量淤浆相在反应器中的停留时间分布(RTD)。做出了许多不同的假设,即串联模型和带旁路的伽马分布模型(GDB)的连续搅拌釜反应器(CSTR)。在每种流动模型的情况下,实验和预测RTD值的平方误差总和之间的比较表明,串联模型中的CSTR和GDB都是反应堆行为的正确描述。将CSTR串联模型和RTD数据与苯酚降解动力学模型一起应用,以预测反应速率的系数。

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