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Dynamic modeling and optimization of a coal-fired utility boiler to forecast and minimize NOx and CO emissions simultaneously

机译：动态建模和优化燃煤电站锅炉，以同时预测和最小化NOx和CO排放

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Increasing penetration of renewable energy sources to the power grid has prompted new ramping scenarios to dispatchable thermal power plants to balance the variability caused by intermittent renewable supplies. With many thermal power plants designed to be base-loaded, ramping of the power output results in increased emission of pollutants. This study develops a dynamic data-driven model of a coal-fired utility boiler that estimates NOx and CO emissions simultaneously. Given a production schedule of a power plant, estimation of NOx and CO emissions for 3 h into the future is performed that can be further utilized in a dynamic optimization algorithm to minimize the emissions over a horizon. It is observed that a dynamic model always has a higher prediction accuracy than a static model, when training and forecasting of the models are concerned. Application of dynamic and steady-state optimization also results in reduced emissions as compared to historical plant emissions. (C) 2019 Elsevier Ltd. All rights reserved.
机译：可再生能源越来越多地渗透到电网中，促使可调度的火力发电厂出现新的升温方案，以平衡由间歇性可再生能源供应引起的可变性。由于许多火力发电厂被设计为基本负荷，因此功率输出的上升会导致污染物排放的增加。这项研究开发了一种动态数据驱动的燃煤电站锅炉模型，该模型可以同时估算NOx和CO排放量。给定发电厂的生产计划，将对未来3小时的NOx和CO排放量进行估算，该估算值可进一步用于动态优化算法中，以最大程度地减少排放量。观察到，当涉及模型的训练和预测时，动态模型总是比静态模型具有更高的预测精度。与历史工厂排放相比，动态和稳态优化的应用还可以减少排放。 （C）2019 Elsevier Ltd.保留所有权利。

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