a State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control , Zhejiang University , Hangzhou , People's Republic of China.
b Zhejiang University Energy Engineering Design and Research Institute Co., Ltd , Hangzhou , People's Republic of China.
J Air Waste Manag Assoc. 2019 May;69(5):565-575. doi: 10.1080/10962247.2018.1551252. Epub 2019 Mar 13.
Sulfur dioxide (SO) is one of the main air pollutants from many industries. Most coal-fired power plants in China use wet flue gas desulfurization (WFGD) as the main method for SO removal. Presently, the operating of WFGD lacks accurate modeling method to predict outlet concentration, let alone optimization method. As a result, operating parameters and running status of WFGD are adjusted based on the experience of the experts, which brings about the possibility of material waste and excessive emissions. In this paper, a novel WFGD model combining a mathematical model and an artificial neural network (ANN) was developed to forecast SO emissions. Operation data from a 1000-MW coal-fired unit was collected and divided into two separated sets for model training and validation. The hybrid model consisting a mechanism model and a 9-input ANN had the best performance on both training and validation sets in terms of RMSE (root mean square error) and MRE (mean relative error) and was chosen as the model used in optimization. A comprehensive cost model of WFGD was also constructed to estimate real-time operation cost. Based on the hybrid WFGD model and cost model, a particle swarm optimization (PSO)-based solver was designed to derive the cost-effective set points under different operation conditions. The optimization results demonstrated that the optimized operating parameters could effectively keep the SO emissions within the standard, whereas the SO emissions was decreased by 30.79% with less than 2% increase of total operating cost. Implications: Sulfur dioxide (SO) is one of the main pollutants generated during coal combustion in power plants, and wet flue gas desulfurization (WFGD) is the main facility for SO removal. A hybrid model combining SO removal mathematical model with data-driven model achieves more accurate prediction of outlet concentration. Particle swarm optimization with a penalty function efficiently solves the optimization problem of WFGD subject to operation cost under multiple operation conditions. The proposed model and optimization method is able to direct the optimized operation of WFGD with enhanced emission and economic performance.
二氧化硫（SO）是许多工业生产过程中主要的空气污染物之一。中国大多数燃煤电厂采用湿法烟气脱硫（WFGD）作为去除 SO 的主要方法。目前，WFGD 的运行缺乏准确的模型预测出口浓度的方法，更不用说优化方法了。因此，WFGD 的运行参数和运行状态是根据专家的经验进行调整的，这就有可能造成材料浪费和过度排放。本文提出了一种将数学模型与人工神经网络（ANN）相结合的新型 WFGD 模型，用于预测 SO 排放。采集了一台 1000MW 燃煤机组的运行数据，并将其分为两组，分别用于模型训练和验证。在训练集和验证集上，由机理模型和 9 个输入 ANN 组成的混合模型在 RMSE（均方根误差）和 MRE（平均相对误差）方面表现最好，因此被选为优化模型。同时，还构建了一个全面的 WFGD 成本模型来估计实时运行成本。基于混合 WFGD 模型和成本模型，设计了基于粒子群优化（PSO）的求解器，以在不同运行条件下得出经济有效的设定点。优化结果表明，优化后的运行参数可以有效地将 SO 排放控制在标准范围内，同时，在总运行成本增加不到 2%的情况下，将 SO 排放量降低了 30.79%。意义：二氧化硫（SO）是电厂燃煤过程中产生的主要污染物之一，湿法烟气脱硫（WFGD）是去除 SO 的主要装置。将 SO 去除数学模型与数据驱动模型相结合的混合模型，实现了对出口浓度更准确的预测。带有罚函数的粒子群优化有效地解决了在多种运行条件下以运行成本为约束的 WFGD 优化问题。提出的模型和优化方法能够指导 WFGD 的优化运行，提高排放和经济性能。
