Parametric based thermo-environmental and exergoeconomic analyses of a combined cycle power plant with regression analysis and optimization

Memon, Abdul Ghafoor; Memon, Rizwan Ahmed; Harijan, Khanji; Uqaili, Muhammad Aslam

doi:10.1016/j.enconman.2014.12.033

Cited by 26 publications

(12 citation statements)

References 22 publications

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“…The prognostic approach to estimate the remaining useful life of GT engines before their next major overhaul was overcome in [10], where a combination of regression techniques was proposed to predict the remaining useful life of GT engines. In [11] it was showed that regression models were good estimators of the response variables to carry out parametric based thermo-environmental and exergoeconomic analyses of CCPPs. The same authors were involved in [12] when using multiple polynomial regression models to correlate the response variables and predictor variables in a CCPP to carry out a thermo-environmental analysis.…”

Section: Related Workmentioning

confidence: 99%

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

et al. 2020

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The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.

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Section: Related Workmentioning

confidence: 99%

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

et al. 2020

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“…They [4] concluded that 80% of the total exergy destruction mainly occurs in steam boiler, and 83.5% of this destruction is unavoidable. Memon et al [5] developed regression model that relates the thermo-environmental quantities of a combined cycle to the operating parameters. They presented an exergoeconomic analysis to determine the cost of electricity production.…”

Section: Introductionmentioning

confidence: 99%

Exergy and exergoeconomic analysis of sustainable direct steam generation solar power plants

Elsafi

2015

Energy Conversion and Management

100

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“…It was revealed that the thermal and exergetic efficiencies could be raised with the application of the combination. Memon et al performed exergo‐economical, statistical, and thermo‐environmental analyses for a combined cycle power plant. The results indicated that the exergetic efficiencies raised and cost rates abated by choosing the optimum conditions.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of a mercury-steam combined-energy-generation system (MES)

Gonca

Şahi̇n

2019

Int J Energy Res

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Summary In this study, exergy and thermo‐ecology–based performance analyses of a mercury‐steam combined–energy‐generation system are comprehensively presented. Performance characteristics such as net‐power output, net‐power density, exergy efficiency, exergy destruction, and ecological coefficient of performance have been obtained by developing a novel numerical model. The impacts of the pressures of open feed water heater and steam turbines, temperatures of mercury boiler, and mercury turbines on the performance characteristics have been investigated. The results indicated that the performance characteristics of the system have been remarkably affected by the pressure and temperature variations of the system components.

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Parametric based thermo-environmental and exergoeconomic analyses of a combined cycle power plant with regression analysis and optimization

Cited by 26 publications

References 22 publications

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

Exergy and exergoeconomic analysis of sustainable direct steam generation solar power plants

Performance evaluation of a mercury-steam combined-energy-generation system (MES)

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