Wastewater treatment process (WWTPs) is an enormous and complex system, it involves multi-disciplines and multi-fields, which is a greatly challenge for industrial Information Integration Engineering (IIIE). In this paper, an integrated control framework for WWTPs is proposed using intelligent optimization to solve the problem of excessive pollutant and high energy consumption. It realizes the effective integration of data model, intelligent decision making, dynamic optimization and control. Firstly, a self-organization soft sensing method is adopted. It can predict the concentrations of ammonia nitrogen and total nitrogen in advance. And the early warning is provided for decision making. Secondly, for the complex dynamic characteristics of multi-performance indexes intercoupling in WWTPs, the dynamic multi-objective immune optimization algorithm is designed. And an optimized set point with high quality in real time is to be obtained. In addition, according to different over standard states, the intelligent decision scheme of WWTPs is designed. It can achieve the effective cooperation of over standard suppression and energy saving. Finally, BSM1 simulation model is used to verify the method. The experimental results show that the integrated framework can achieve the whole process of WWTPs real-time standards and can effectively reduce energy consumption, so it has competitive practical application prospect for IIIE.

污水处理过程（WWTPs）是一个庞大而复杂的系统，涉及多学科、多领域，对工业信息集成工程（IIIE）提出了极大的挑战。本文提出了一种利用智能优化的污水处理厂综合控制框架，以解决污染物超标和能耗高的问题。实现了数据模型、智能决策、动态优化和控制的有效集成。首先，采用自组织软感知方法。它可以提前预测氨氮和总氮的浓度。并为决策提供预警。其次，针对污水处理厂多性能指标相互耦合的复杂动态特性，设计了动态多目标免疫优化算法。并且将实时获得具有高质量的优化设置点。此外，根据不同的超标状态，设计了污水处理厂的智能决策方案。可实现超标抑制与节能的有效配合。最后利用BSM1仿真模型对该方法进行验证。实验结果表明，该集成框架能够实现污水处理厂全过程实时标准，并能有效降低能耗，对IIIE具有极具竞争力的实际应用前景。可实现超标抑制与节能的有效配合。最后利用BSM1仿真模型对该方法进行验证。实验结果表明，该集成框架能够实现污水处理厂全过程实时标准，并能有效降低能耗，对IIIE具有极具竞争力的实际应用前景。可实现超标抑制与节能的有效配合。最后利用BSM1仿真模型对该方法进行验证。实验结果表明，该集成框架能够实现污水处理厂全过程实时标准，并能有效降低能耗，对IIIE具有极具竞争力的实际应用前景。