ABSTRACT
To optimize the ultraviolet (UV) water disinfection process, it is crucial to determine the ideal geometric dimensions of a corresponding model that enhance performance while minimizing the impact of uncertain photoreactor inputs. As water treatment directly affects people's lives, it is crucial to eliminate the risks associated with the non-ideal performance of disinfection photoreactors. Input uncertainties greatly affect photoreactor performance, making it essential to develop a robust optimization algorithm in advance to mitigate these effects and minimize the physical and financial resources required for constructing the photoreactors. In the suggested algorithm, a two-objective genetic algorithm is integrated with a non-intrusive polynomial chaos expansion (PCE) technique. Additionally, the Sobol sampling method is employed to select the necessary samples for understanding the system's behavior. An artificial neural network surrogate model is trained using sufficient data points derived from computational fluid dynamics (CFD) simulations. A novel type of UV photoreactors working based on exterior reflectors is chosen to optimize the process with three uncertain input parameters, including UV lamp power, UV transmittance of water, and diffusive fraction of the reflective surface. In addition, four geometrical design variables are considered to find the optimal configuration of the photoreactor. The standard deviation (SD) and the reciprocal of log reduction value (LRV) are set as the objective functions, calculated using PCE. The optimal design provides a LRV of 3.95 with SD of 0.2. The coefficient of variation (CoV) of the model significantly declines up to 7%, indicating the decreased sensitivity of the photoreactor to the input uncertainties. Additionally, it is discovered that the robust model exhibits minimal sensitivity to changes in reflectivity in various flow rates, and its output variability aligns with the SD obtained through robust optimization.
ABSTRACT
Ultraviolet (UV) water disinfection method has emerged as an alternative to chemical methods of disinfection. In typical UV photoreactors for water treatment, water flows in the space between the lamp's sleeve and outer shell. The contact of water and sleeve causes fouling, which reduces the effectiveness of UV. To clean the photoreactor, the quartz sleeve must be replaced; this may lead to quartz or lamp breakage and mercury leakage into water during cleaning. In this study, a novel type of multi-lamp UV photoreactors is proposed, in which the UV lamps are placed out of the water channel and their UV irradiation is redirected into the channel using an outer cylindrical reflector. This allows for the installment of a self-cleaning mechanism for the water channel. A well-validated three-dimensional CFD model is utilized to model the performance of this photoreactor for microbial inactivation. The impacts of several geometrical and optical parameters are investigated on the inactivation of microorganisms. The results revealed that the difference in log reduction values (LRV) between fully specular and fully diffuse reflector ranges from 10 to 47% as the lamp-to-channel distance increases. For the volumetric flow rate of 25 GPM, the LRV of a photoreactor with fully diffuse reflector can be 46% higher than a fully specular one. In addition, the performance of the proposed photoreactor is compared against a classic L-shaped annular photoreactor. The results show that the new design can provide equal or better microbial performance compared to the classic photoreactor, but it removes many of their common issues such as quartz fouling, lamp overheating at low flow rates, and sleeve breakage during lamp replacement.
