Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada.
Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada.
Water Res. 2015 Sep 15;81:101-12. doi: 10.1016/j.watres.2015.03.023. Epub 2015 May 15.
UV irradiation and advanced oxidation processes have been recently regarded as promising solutions in removing polycyclic aromatic hydrocarbons (PAHs) from marine oily wastewater. However, such treatment methods are generally not sufficiently understood in terms of reaction mechanisms, process simulation and process control. These deficiencies can drastically hinder their application in shipping and offshore petroleum industries which produce bilge/ballast water and produced water as the main streams of marine oily wastewater. In this study, the factorial design of experiment was carried out to investigate the degradation mechanism of a typical PAH, namely naphthalene, under UV irradiation in seawater. Based on the experimental results, a three-layer feed-forward artificial neural network simulation model was developed to simulate the treatment process and to forecast the removal performance. A simulation-based dynamic mixed integer nonlinear programming (SDMINP) approach was then proposed to intelligently control the treatment process by integrating the developed simulation model, genetic algorithm and multi-stage programming. The applicability and effectiveness of the developed approach were further tested though a case study. The experimental results showed that the influences of fluence rate and temperature on the removal of naphthalene were greater than those of salinity and initial concentration. The developed simulation model could well predict the UV-induced removal process under varying conditions. The case study suggested that the SDMINP approach, with the aid of the multi-stage control strategy, was able to significantly reduce treatment cost when comparing to the traditional single-stage process optimization. The developed approach and its concept/framework have high potential of applicability in other environmental fields where a treatment process is involved and experimentation and modeling are used for process simulation and control.
紫外线照射和高级氧化工艺最近被认为是去除海洋含油废水中多环芳烃（PAHs）的有前途的解决方案。然而，就反应机制、过程模拟和过程控制而言，这些处理方法通常没有得到充分的理解。这些缺陷极大地阻碍了它们在航运和近海石油工业中的应用，这些工业产生舱底/压载水和采出水作为海洋含油废水的主要流。在本研究中，进行了析因实验设计以研究在海水中紫外线照射下典型 PAH（即萘）的降解机制。基于实验结果，开发了一个三层前馈人工神经网络模拟模型来模拟处理过程并预测去除性能。然后提出了一种基于模拟的动态混合整数非线性规划（SDMINP）方法，通过集成开发的模拟模型、遗传算法和多阶段规划来智能控制处理过程。通过案例研究进一步测试了所开发方法的适用性和有效性。实验结果表明，辐照率和温度对萘去除的影响大于盐度和初始浓度的影响。所开发的模拟模型可以很好地预测不同条件下的紫外线诱导去除过程。案例研究表明，SDMINP 方法在多阶段控制策略的辅助下，与传统的单阶段过程优化相比，能够显著降低处理成本。所开发的方法及其概念/框架在涉及处理过程且使用实验和建模进行过程模拟和控制的其他环境领域具有很高的适用性潜力。
