文章基本信息

标题：Model Predictive Control of Stochastic Wastewater Treatment Process for Smart Power, Cost-Effective Aeration
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作者：Peter A. Stentoft ; Daniela Guericke ; Thomas Munk-Nielsen 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：1
页码：622-627
DOI：10.1016/j.ifacol.2019.06.132
语种：English
出版社：Elsevier
摘要：AbstractWastewater treatment is an essential process to ensure the good chemical and environmental status of natural water bodies. The energy consumption for wastewater treatment represents an important cost for water utilities. Meanwhile has the increasing fraction of renewable energy sources in the electricity market created the possibility of exploiting cheaper (and greener) electricity. This paper proposes model predictive control driven by stochastic differential equations and genetic optimization to prioritize aeration in periods with low electricity prices thereby reducing costs and empowering smart use of green electricity. This is without violation of legislation and equipment constraints. The method is tested with real plant data and electricity market prices to demonstrate efficiency and feasibility.
关键词：KeywordsPredictive ControlStochastic SystemsGenetic AlgorithmsProcess ControlWastewater TreatmentSmart Power Applications