The assessment of response surface methodology (RSM) and artificial neural network (ANN) modeling in dry flue gas desulfurization at low temperatures.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-01-01 DOI:10.1080/10934529.2023.2174334

Robert Makomere, Hilary Rutto, Lawrence Koech

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引用次数: 1

Abstract

The performance of a flue gas desulfurization (FGD) system is characterized by SO₂ removal efficiency ( $Y_{1}$ ) and reagent conversion ( $Y_{2}$ ). Achieving a near-perfect reaction environment has been of concern in dry FGD (DFGD) due to the low reactivity compared to the wet and semi-dry units. This study will appraise output responses using modeling by response surface methodology (RSM) and artificial neural networks (ANN) approaches. The impacts of input parameters like hydration time, hydration temperature, diatomite to hydrated lime (Ca(OH)₂), sulfation temperature and inlet gas concentration will be studied using a randomized central composite design (CCD). ANN fitting tool mapped the CCD metadata using the Levenberg-Marquardt (LM) algorithm activated by the hyperbolic tangent (tansig) function. The hidden cells ranged from 7 to 10 to ascertain the effect node architecture on modeling accuracy. Validation of each procedure was assessed using root mean square error (RMSE), mean square error (MSE) and R-Squared studies. The outcomes presented a more accurate 5-10-2 ANN model in the mapping of the DFGD from R² data of $Y_{1}$ = 0.993 and $Y_{2}$ = 0.9986 with a mapping deviation from the RMSE values of $Y_{1}$ = 0.48465; $Y_{2}$ = 0.44971 and MSE results of $Y_{1}$ = 0.23488; $Y_{2} .$ = 0.20229.

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响应面法(RSM)和人工神经网络(ANN)建模在低温干法烟气脱硫中的评价。

烟气脱硫(FGD)系统的性能表征是SO2去除率(Y1)和试剂转化率(Y2)。由于与湿式和半干式装置相比，反应性较低，因此实现近乎完美的反应环境一直是干式烟气脱硫(DFGD)关注的问题。本研究将使用响应面法(RSM)和人工神经网络(ANN)方法建模来评估输出响应。采用随机中心复合设计(CCD)研究水化时间、水化温度、硅藻土对水化石灰(Ca(OH)2)、磺化温度和进口气体浓度等输入参数的影响。人工神经网络拟合工具使用双曲正切函数激活的Levenberg-Marquardt (LM)算法对CCD元数据进行映射。为了确定节点结构对建模精度的影响，隐藏的单元格从7到10不等。采用均方根误差(RMSE)、均方误差(MSE)和R-Squared研究评估每个程序的有效性。结果表明，从R2数据Y1 = 0.993和Y2 = 0.9986中映射DFGD的5-10-2神经网络模型更为准确，映射偏差与RMSE值Y1 = 0.48465;Y2 = 0.44971, MSE结果Y1 = 0.23488;Y2。= 0.20229。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.