College of Environmental Science and Engineering, South China University of Technology, Guangzhou, 510640, China.
Environ Sci Pollut Res Int. 2018 Jul;25(21):20956-20967. doi: 10.1007/s11356-018-2056-5. Epub 2018 May 15.
Anaerobic ammonium oxidation (ANAMMOX) has been regarded as an efficient process to treat nitrogen-containing wastewater. However, the treatment process is not fully understood in terms of reaction mechanisms, process simulation, and control. In this paper, a multi-objective control strategy mixed soft-sensing model (MCSSM) is developed to systematically design the operating variations for multi-objective control by integrating the developed model, a least square support vector machine optimized with principal component analysis (PCA-LSSVM) and non-dominated sorting genetic algorithm-II (NSGA-II). The results revealed that the PCA-LSSVM model is a feasible and efficient tool for predicting the effluent ammonia nitrogen concentration ([Formula: see text]) and the total nitrogen removal concentration (C) with determination coefficients (R) were 0.997 for [Formula: see text] and 0.989 for C, and gives us the reasonable solutions in influent by using NSGA-II. To achieve a better removal effect, the influent pH should be kept between 7.50 and 7.52, the COD/TN ratio is suggested to maintain at 0.15 and the NH-N/NO-N ratio is suggested to maintain at 0.61. The developed MCSSM approach and its general modeling framework have a high potential of applicability and guidance to bioprocess in wastewater treatment, and numerical models can be structured for predicting and optimization and experiments can be conducted for data acquisition and model establishment.
厌氧氨氧化（ANAMMOX）已被认为是处理含氮废水的一种有效方法。然而，在反应机制、过程模拟和控制方面，该处理过程尚未得到充分理解。本文提出了一种多目标控制策略混合软测量模型（MCSSM），通过整合开发的模型、基于主成分分析（PCA）优化的最小二乘支持向量机（PCA-LSSVM）和非支配排序遗传算法-II（NSGA-II），系统地设计多目标控制的操作变量。结果表明，PCA-LSSVM 模型是一种可行且高效的工具，可用于预测出水氨氮浓度 ([Formula: see text]) 和总氮去除浓度 (C)，其决定系数 (R) 分别为 0.997 和 0.989，同时利用 NSGA-II 为进水提供合理的解决方案。为了达到更好的去除效果，建议将进水 pH 值保持在 7.50 到 7.52 之间，COD/TN 比建议维持在 0.15，NH-N/NO-N 比建议维持在 0.61。所开发的 MCSSM 方法及其通用建模框架具有很高的适用性和指导废水处理生物过程的潜力，可用于预测和优化的数值模型，以及用于数据采集和模型建立的实验。
