数据驱动优化在垃圾焚烧发电中的应用与实践

Application and Practice of Data-Driven Optimization in Waste Incineration Power Generation

随着数据科学与人工智能的迅速发展,工业过程正朝着大型化、复杂化和精准化的方向进行技术革新。在机理模型和辨识模型不够精确或难以建立的情况下,数据驱动控制和优化理论能够实现对生产过程和设备的有效控制。本文探索了数据驱动优化理论和方法在垃圾焚烧发电中的应用与实践。在垃圾焚烧发电过程中,其生产数据往往具有非线性、强耦合、时变和大滞后的特点。应用数据驱动优化理论与方法,在燃烧物保持稳定的基础上,通过更加精准地控制与优化燃烧过程,可以有效提升约6%的发电量,助力企业实现提质降耗。With the rapid development of data science and artificial intelligence, industrial processes are undergoing technological innovations in the direction of large-scale, complex and precise. When the mechanism model and identification model are not accurate enough or difficult to establish, data-driven control and optimization theory can achieve effective control of production processes and equipment. This paper explores the application and practice of data-driven optimization theory and methods in waste incineration power generation. In the process of waste incineration power generation, its production data often has the characteristics of nonlinearity, strong coupling, time-varying and large lag. By applying data-driven optimization theory and methods, on the basis of maintaining the stability of the combustion materials, through more precise control and optimization of the combustion process, the power generation can be effectively increased by about 6%, helping enterprises to achieve quality improvement and consumption reduction.