A novel denitration cost optimization system for power unit boilers

Yang, Tingting; Cui, Chao; Shen, Yali; Lv, You

doi:10.1016/j.applthermaleng.2015.11.111

Cited by 20 publications

(4 citation statements)

References 21 publications

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“…The basic RF is enhanced by applying the back propagation (BP) neural network approach. BPmeasures the efficiency of the inputs on the outputs by network weights [22]. Hence in our work every output treeestablishes the input vector and produces the final predicted result of the BP network,…”

Section: = ( )(2)mentioning

confidence: 99%

AN Enhanced Model for Diabetes Prediction using Improved Firefly Feature Selection and Hybrid Random Forest Algorithm

Kumar¹,

Gunavathi²

2019

IJEAT

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Diabetes is a chronic disease that causes numerous amount of death each year. Untreated diabetes disturbs the proper functionality of other organs in human body. Hence early detection is a significant process to have a healthy life style. Usually the performance of the classification is affected due to the existence of high dimensionality in medical data.In this study a system model is proposed on Pima dataset to enhance the classification accuracy by eliminating the irrelevant features. Therefore it is important to choose a suitable feature selection approach that provides the better accuracy in disease prediction compared to prior study.Hencenovel techniquesImprovedFirefly(IFF)andhybrid Random forest algorithmis proposed for feature selection and classification. The present study provides a better result with 96.3% accuracy.The efficiency of the present studyis compared with the prior classification approaches.

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Section: = ( )(2)mentioning

confidence: 99%

AN Enhanced Model for Diabetes Prediction using Improved Firefly Feature Selection and Hybrid Random Forest Algorithm

Kumar¹,

Gunavathi²

2019

IJEAT

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“…Therefore, data-driven modeling and predictive control with high requirements for data volume and computing power are increasingly applied to the improvement of control systems in coal-fired power plants. The data-driven methods have been widely applied in combustion prediction and optimization (Li et al, 2014;Li and Niu, 2016;Cheng et al, 2018), NOx emission prediction and reduction (Smrekar et al, 2013;Song et al, 2016;Yang et al, 2016;Tan et al, 2019;Xie et al, 2020;Kang et al, 2021), wall temperature prediction (Dhanuskodi et al, 2015;Xie et al, 2020), estimation of exhaust steam properties (Guo et al, 2016;Laubscher, 2019), boiler-turbine coordinated control (Wu et al, 2013;Wu et al, 2014a;Wu et al, 2014b), and so on. However, the application of machine learning on steam temperature prediction is rare.…”

Section: Introductionmentioning

confidence: 99%

Multi-Step Ahead Prediction of Reheat Steam Temperature of a 660 MW Coal-Fired Utility Boiler Using Long Short-Term Memory

Tan

Zhu

et al. 2022

Front. Energy Res.

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With increases in the penetration of renewables in grids, there is an increasing demand for coal-fired power plants to operate flexibly. Regulation of reheat steam temperature is of great importance for the safe and efficient operation of coal-fired power plants. However, the difficulty of reheat steam temperature regulation increases largely during flexible operation due to the large delay and nonlinear properties, especially those units designed to shoulder base load and with limited regulating strategy. A multistep prediction model on the reheat steam temperature of a 660-MW coal-fired utility boiler was developed based on long short-term memory. The results show that the multistep prediction model performs well. The average root mean square error and mean absolute percentage error values of the five-step prediction results are less than 0.52°C and 0.07%, respectively. The correlation coefficients of the five-step predictions are all greater than 0.95. With a sample interval of 30 s, the model provides an accurate prediction of reheat steam temperature within 2.5 min, which could supply an important reference for the reheat steam temperature regulation.

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“…Shakuntla et al [2] discussed poly tube availability analysis using the supplementary variable technique. Also, reducing the risk of hazards for optimum reliability was studied by Yang et al [3]. The availability of banking services with a standby system has been evaluated in [4].…”

Section: Introductionmentioning

confidence: 99%

Mathematical model for analysing availability of threshing combine machine under reduced capacity

Singla

Modibbo

Mijinyawa

et al. 2022

YUGOSLAV J OPERATION

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Obtaining system availability in an engineering design is trickish and challenging, especially when there is a reduction in capacity; however, it supports system maintainability. In this paper, a mathematical model for finding the availability under the reduced capacity has been proposed using the Chapman Kolmogorov approach with the help of transition diagrams associated with various possible combinations of probabilities. The paper observes the most critical subsystem by selecting variable failure and repair rates from different subsystems. It deals with the sensitivity analysis of a complex repairable threshing combined machine comprising subsystems in a series configuration and the threshing machine consisting of 21 subsystems. The device works in total capacity when the threshing drum and feeding Hooper work in the complete state, and the concave subsystem and blower work with reduced power. This study dealt with uncertain data and was analyzed analytically using a complex repairable system. The availability of the entire machine has been investigated analytically, and various availability indices such as subsystems extruder have been computed and reported. The study discovered that subsystem extruder has the most impact on some subsystems? overall system availability.

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A novel denitration cost optimization system for power unit boilers

Cited by 20 publications

References 21 publications

AN Enhanced Model for Diabetes Prediction using Improved Firefly Feature Selection and Hybrid Random Forest Algorithm

AN Enhanced Model for Diabetes Prediction using Improved Firefly Feature Selection and Hybrid Random Forest Algorithm

Multi-Step Ahead Prediction of Reheat Steam Temperature of a 660 MW Coal-Fired Utility Boiler Using Long Short-Term Memory

Mathematical model for analysing availability of threshing combine machine under reduced capacity

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