An investigation on data mining and operating optimization for wet flue gas desulfurization systems

Qiao, Zongliang; Wang, Xingchao; Gu, Hui; Tang, Youfei; Si, Fengqi; Romero, Carlos E.; Yao, Xuezhong

doi:10.1016/j.fuel.2019.116178

Cited by 34 publications

(7 citation statements)

References 31 publications

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“…In the operating state, the process involves the transfer of heat and mass, acid–base neutralization, water evaporation, droplet coalescence and fragmentation. Consisting of a variety of equipment and various physical and chemical processes, there are many factors that can affect the operation efficiency and energy consumption of the desulfurization system, such as pH of the tank, volume of circulation slurry and oxidized air, Cl − , and coal species, flue gas parameters at the entrance, and so on (Córdoba, 2015; Qiao et al, 2019). In addition, the desulfurization system generates a large amount of data at all times.…”

Section: Object and Methodsmentioning

confidence: 99%

Error prediction and structure determination for CMAC neural network based on the uniform design method

et al. 2020

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Insufficient study on error bound of cerebellar model articulation controller (CMAC) severely limits its application. To investigate the error prediction and structure determination of CMAC for multi‐dimensional and data‐generation objects, this paper builds a 10‐input 2‐output model for a desulfurization system to test 44,640 sets of operation data. Four test groups and one prediction group are designed and performed based on uniform design method. Regression analysis and curve fitting methods are applied for error analyses. The focus of regression analysis method is the influence of uniform table's level on its prediction formulas' accuracies, whereas curve fitting's is the impact of theoretical memory space (location number) and actual storage space (address number) of CMAC on output error. Based on the results, the following conclusions are obtained. (a) The prediction accuracy of the linear regression equation is not monotonous with the level of the uniform design table, but there is a distinct region with local high precision. (b) Compared with regression analysis and address number fitting methods, location number analysis method has distinct advantages of prediction range, accuracy and flexibility. (c) In terms of location number analysis, different intervals may correspond to different optimal fitting functions, but only power function maintains high prediction accuracy in the whole range where the method works. Besides, the scope of location number analysis is also studied, of which the lower borders are 109 and 1010 approximately for model's two outputs, respectively.

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Section: Object and Methodsmentioning

confidence: 99%

Error prediction and structure determination for CMAC neural network based on the uniform design method

et al. 2020

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“…The experimental results show that this method has strong stability and customized analysis effect [ 13 ]. Qiao and other scholars used different levels of iterative stacking analysis to realize the normalized analysis of different degrees of mental health problems and proposed a hybrid stacking service strategy based on high-intensity memory analysis [ 14 ]. Tsang and other scholars rely on different types of databases to realize customized mental health services for college students of different grades and conduct one-to-one reinforcement analysis according to the participation of college students [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

College Students’ Mental Health Support Based on Fuzzy Clustering Algorithm

2022

Contrast Media & Molecular Imaging

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There are some problems in the active participation of college students in ideological and mental health support services in China, such as low attention, low participation, and high data redundancy. Based on this, this paper studies the active participation of college students’ ideological and mental health support service based on fuzzy cluster analysis algorithm. Compared with the disadvantages of the current mainstream discrete optimization analysis models on mental health (such as high-dimensional enterprise model, Dajiaweikang model, and short-range group control model), which need to set the known data gradient interval, this paper creatively adopts the fuzzy cluster analysis algorithm, based on the characteristics of different types of college students’ ideological and mental health problems. Combined with the improved star discrete analysis model, this paper constructs the active participatory evaluation strategy of college students’ ideological and mental health support services. On this basis, the model can not only record and store the participatory data of ideological and mental health support for students of different grades but also match and track different types of data based on special framework conditions, so as to achieve numerical normal analysis and directional matching for the data coupling mode of college students’ ideological and mental health support services. On the other hand, the Planck constant factor is used to classify different types of ideological and psychological factor data, and combined with the idea of fuzzy clustering, the hierarchical analysis and quantitative calibration of different types of data groups are realized, so as to improve the reliability and authenticity of the active participation in college students’ mental health support services. The results show that this star discrete analysis model can analyze the active participation of college students’ ideological and mental health support services according to the data matching degree of different levels and can effectively improve the analysis efficiency of data vectors. Compared with the traditional research methods on the active participation of college students’ ideological and mental health support services, this method can realize the matching and tracking of different types of data, so as to make a numerical and normal analysis on the data coupling mode of college students’ ideological and mental health support services.

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“…They further determined the optimal control variables and demonstrated the environmental benefits of the optimization. Qiao et al [23] proposed an improved fuzzy clustering algorithm integrating K-means, information entropy, and C-means to select the optimum operation data with minimum cost. The basic idea of this type of approach is to extract the operation parameters with the best performance based on the historical operation data presenting the varying states of the desulfurization process.…”

Section: Literature Reviewmentioning

confidence: 99%

A Many-Objective Optimization for an Eco-Efficient Flue Gas Desulfurization Process Using a Surrogate-Assisted Evolutionary Algorithm

Dong¹,

Wang²,

Peng

et al. 2021

Sustainability

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The flue gas desulfurization process in coal-fired power plants is energy and resource-intensive but the eco-efficiency of this process has scarcely been considered. Given the fluctuating unit load and complex desulfurization mechanism, optimizing the desulfurization system based on the traditional mechanistic model poses a great challenge. In this regard, the present study optimized the eco-efficiency from the perspective of operating data analysis. We formulated the issue of eco-efficiency improvement into a many-objective optimization problem. Considering the complexity between the system inputs and outputs and to further reduce the computational cost, we constructed a Kriging model and made a comparison between this model and the response surface methodology based on two accuracy metrics. This surrogate model was then incorporated into the NSGA-III algorithm to obtain the Pareto-optimal front. As this Pareto-optimal front provides multiple alternative operating options, we applied the TOPSIS to select the most appropriate alternative set of operating parameters. This approach was validated using the historical operation data from the desulfurization system at a coal-fired power plant in China with a 600 MW unit. The results indicated that the optimization would cause an improvement in the efficiency of desulfurization and energy efficiency but a slight increase in the consumption of limestone slurry. This study attempted to provide an effective operating strategy to enhance the eco-efficiency performance of desulfurization systems.

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An investigation on data mining and operating optimization for wet flue gas desulfurization systems

Cited by 34 publications

References 31 publications

Error prediction and structure determination for CMAC neural network based on the uniform design method

Error prediction and structure determination for CMAC neural network based on the uniform design method

College Students’ Mental Health Support Based on Fuzzy Clustering Algorithm

A Many-Objective Optimization for an Eco-Efficient Flue Gas Desulfurization Process Using a Surrogate-Assisted Evolutionary Algorithm

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