基于CPS框架的烟气脱硫过程智能优化控制

In the wet flue gas desulfurization method, SO2 is absorbed by the chemical reaction of limestone slurry and flue gas in the absorber, and the by-product gypsum is also obtained. This method not only reduces SO2 emission effectively, but also brings considerable economic benefit to the enterprise. At present, it has become an important means to save energy and reduce emission. The traditional wet flue gas desulfurization system usually focuses on the pH value adjustment and the desulfurization rate improvement, but the problems such as high cost and serious energy waste are neglected in the process of wet flue gas desulfurization. This project comprehensively understands the wet flue gas desulfurization background and process principle. Based on data driven mechanism, the influence of pH value and desulfurization rate by process parameters are analyzed in detail, and mathematical models about pH and desulfurization rate are established. In order to enhance capacity of energy saving and emission reduction of enterprise, and to realize production intelligence, the concept of CPS (Cyber-physical system) is combined with the characteristics of wet flue gas desulfurization system, and a CPS based wet flue gas desulfurization framework is proposed. Data acquisition, communication and integration analysis are realized under the different working conditions. Considering the process of equipment management and quality management, the intelligent optimization controller with the whole process integration is designed, and the intelligent optimization service platform of the wet flue gas desulfurization process based on CPS is formed.

湿法烟气脱硫方法通过石灰石浆液与烟气在吸收塔内的化学反应吸收SO2，并获得石膏等副产品。该方法不但有效降低了SO2排放，还给企业带来了可观的经济效益，目前已经成为企业节能减排的重要手段。传统湿法烟气脱硫系统往往注重吸收过程中浆液pH值的调节和脱硫率的提高，忽略了生产过程成本消耗大、能源浪费严重等问题。本项目全面了解湿法烟气脱硫的背景和工艺原理，详细分析湿法烟气脱硫过程中各工艺参数的变化规律及其对pH值和脱硫率的影响，在大量数据驱动的基础上，构建pH值和脱硫率数学模型。为提升企业节能减排能力，并进一步实现生产智能化，将物理信息系统(CPS)概念与湿法烟气脱硫控制系统特点相结合，提出基于CPS的湿法烟气脱硫系统架构。针对不同工况条件，实现数据的采集、通讯和集成分析，统一考虑设备管理、质量管理过程，设计具有全流程一体化的智能优化控制器，形成基于CPS的湿法烟气脱硫全流程智能优化服务平台。

湿法烟气脱硫是目前应用最多、技术最为成熟的一种烟气脱硫处理工艺，但该系统存在脱硫效果差、生产成本高、智能化水平低等问题，严重制约了该领域节能减排、降本增效目标的实现。本项目从多变量、多工况的复杂动态系统角度，综合运用人工智能、自适应控制、优化控制、信息物理系统等多种基础理论和方法，通过对系统深入的理论和实验研究，取得了如下研究成果：（1）提出了智能湿法烟气脱硫系统数据清洗方法；（2）提出了基于数据的湿法脱硫系统关键变量建模方法；（3）设计了湿法烟气脱硫系统预测控制器；（4）提出了针对多目标的全流程优化控制方案；（5）给出了湿法烟气脱硫系统信息物理系统框架。该研究为推动湿法烟气脱硫系统的智能优化和信息物理系统构建奠定了理论基础。项目开展期间，发表期刊论文共27篇（SCI检索23篇），会议论文共12篇，申请发明专利2项，培养硕士研究生7名，博士研究生2名。

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