Optimized scheme in coal-fired boiler combustion based on information entropy and modified K-prototypes algorithm

Gu, Hui; Zhu, Hongxia; Cui, Yanfeng; Si, Fengqi; Xue, Rui; Han, Xiaoxu; Zhang, Jiayu

doi:10.1016/j.rinp.2018.04.045

Cited by 17 publications

(8 citation statements)

References 31 publications

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“…In this paper, thirteen control variables were initially selected to model the 660 MW e supercritical coal power plant's η thermal in Sahiwal, Pakistan. The variables were selected based upon the recommendation of experienced plant managers of the power plant and a comprehensive literature review [33,[64][65][66][67][68]. Some control variables were controllable by the operator, e.g., the main steam temperature (MST), reheat steam temperature (RST), and oxygen content in flue gas at the boiler outlet (O 2 ).…”

Section: Variables Selection For Ai Process Modelingmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

et al. 2020

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This paper presents a comprehensive step-wise methodology for implementing industry 4.0 in a functional coal power plant. The overall efficiency of a 660 MWe supercritical coal-fired plant using real operational data is considered in the study. Conventional and advanced AI-based techniques are used to present comprehensive data visualization. Monte-Carlo experimentation on artificial neural network (ANN) and least square support vector machine (LSSVM) process models and interval adjoint significance analysis (IASA) are performed to eliminate insignificant control variables. Effective and validated ANN and LSSVM process models are developed and comprehensively compared. The ANN process model proved to be significantly more effective; especially, in terms of the capacity to be deployed as a robust and reliable AI model for industrial data analysis and decision making. A detailed investigation of efficient power generation is presented under 50%, 75%, and 100% power plant unit load. Up to 7.20%, 6.85%, and 8.60% savings in heat input values are identified at 50%, 75%, and 100% unit load, respectively, without compromising the power plant’s overall thermal efficiency.

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Section: Variables Selection For Ai Process Modelingmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

et al. 2020

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“…In this paper, twenty-four thermo-electric operating parameters of the power plant are taken to model the generator power under various power generation scenarios. The thermo-electric operating parameters are selected based on the operation engineers' experience and the comprehensive literature review [30][31][32][33][34][35]. The operating parameters are taken from the boiler, turbine, and generator sides of the power plant and are critically controlled within the operating ranges.…”

Section: Training Data For Process Modelingmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Ashraf

Uddin

Kamal³

et al. 2020

Energies

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Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making and process optimization strategies for complex industrial operations. In this paper, modern and reliable process modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LSSVM), are employed and comprehensively compared as reliable and robust process models for the generator power of a 660 MWe supercritical coal combustion power plant. Based on the external validation test conducted by the unseen operation data, LSSVM has outperformed the MLR and ANN models to predict the power plant’s generator power. Later, the LSSVM model is used for the failure mode recovery and a very successful operation control excellence tool. Moreover, by adjusting the thermo-electric operating parameters, the generator power on an average is increased by 1.74%, 1.80%, and 1.0 at 50% generation capacity, 75% generation capacity, and 100% generation capacity of the power plant, respectively. The process modeling based on process data and data-driven process optimization strategy building for improved process control is an actual realization of industry 4.0 in the industrial applications.

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“…Du et al proposed a novel density peaks clustering algorithm for mixed data (DPC-MD) which uses a new similarity criterion to handle the three types of data: numeric, categorical, or mixed data for improving the original density peaks clustering algorithm [40]. Gu et al proposed a modified k-prototypes algorithm to obtain the capacity of self-adaptive in discrete interval determination, which has overcome the shortcomings from common methods in conditional complementary entropy [41]. Davoodi et al proposed a deep rule-based fuzzy system (DRBFS) which deals with a large amount of data with numeric and categorical attributes.…”

Section: Clustering For Mixed Datamentioning

confidence: 99%

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

Jang

Kim

et al. 2018

Sustainability

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Data mining plays a critical role in sustainable decision-making. Although the k-prototypes algorithm is one of the best-known algorithms for clustering both numeric and categorical data, clustering a large number of spatial objects with mixed numeric and categorical attributes is still inefficient due to complexity. In this paper, we propose an efficient grid-based k-prototypes algorithm, GK-prototypes, which achieves high performance for clustering spatial objects. The first proposed algorithm utilizes both maximum and minimum distance between cluster centers and a cell, which can reduce unnecessary distance calculation. The second proposed algorithm as an extension of the first proposed algorithm, utilizes spatial dependence; spatial data tends to be similar to objects that are close. Each cell has a bitmap index which stores the categorical values of all objects within the same cell for each attribute. This bitmap index can improve performance if the categorical data is skewed. Experimental results show that the proposed algorithms can achieve better performance than the existing pruning techniques of the k-prototypes algorithm.

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Optimized scheme in coal-fired boiler combustion based on information entropy and modified K-prototypes algorithm

Cited by 17 publications

References 31 publications

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

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