Universidade Federal de Pernambuco, Departamento de Engenharia Química; Av. Prof. Artur de Sá, SN., Cidade Universitária, CEP: 50740-521, Recife, PE, Brazil E-mail:
Refinaria Abreu e Lima; Rodovia PE60, CEP 55590-000, Suape, PE, Brazil.
Water Sci Technol. 2014;69(11):2279-86. doi: 10.2166/wst.2014.158.
This work is concerned with the intrinsic reaction kinetic of the degradation of atrazine (ATZ) using H2O2-UVC. Experimental runs were carried out in annular photoreactor. The initial concentration of ATZ was 2.2 × 10(-2) mol m(-3) while the H2O2-ATZ molar ratio range was 0-578 mol H2O2 mol(-1) ATZ. The ATZ molecules are decomposed by means of free-radical attack (95.2%) and direct photolysis (4.8%). There is an optimal H2O2/ATZ molar ratio (ROP = 347 H2O2 mol(-1) ATZ) which maximizes the initial degradation rate and conversion at 300 s at 83% and 77%, respectively. The process is economically feasible as the values of the energy requirement, energy and H2O2 costs at ROP are 0.14 KWh m(-3) order(-1), US$0.02 kWh(-1) m(-3) and US$1.0 m(-3), respectively. The kinetic model proposed is based on Lea's reaction scheme for the H2O2 direct photolysis, the hypothesis that unknown ATZ sub-products that absorb UVC radiation are generated, and the local volumetric rate of photon absorption. The radiation transport equation was solved and the linear spherical source emission model was used to represent the lamp emission. Intrinsic reaction kinetic parameters were estimated and the model was validated. The model predicted the data in a range of 90 to 98%.
本工作研究了使用 H2O2-UVC 降解莠去津（ATZ）的本征反应动力学。实验在环形光反应器中进行。ATZ 的初始浓度为 2.2×10(-2) mol m(-3)，而 H2O2-ATZ 的摩尔比范围为 0-578 mol H2O2 mol(-1) ATZ。ATZ 分子通过自由基攻击（95.2%）和直接光解（4.8%）分解。存在最佳的 H2O2/ATZ 摩尔比（ROP = 347 H2O2 mol(-1) ATZ），可在 300 s 时使初始降解速率和转化率分别达到 83%和 77%的最大值。该工艺具有经济可行性，因为在 ROP 时，能量需求、能量和 H2O2 成本的值分别为 0.14 KWh m(-3) order(-1)、US$0.02 kWh(-1) m(-3)和 US$1.0 m(-3)。所提出的动力学模型基于 Lea 对 H2O2 直接光解的反应方案、假设吸收 UVC 辐射的未知 ATZ 亚产物被生成，以及局部光子吸收体积速率。辐射传输方程得到了解决，线性球源发射模型被用于表示灯的发射。估计了本征反应动力学参数并验证了模型。模型在 90%至 98%的范围内预测了数据。
