A data-driven model for the air-cooling condenser of thermal power plants based on data reconciliation and support vector regression

Li, Xiaoen; Wang, Ningling; Wang, Ligang; Kantor, Ivan; Robineau, Jean-Loup Sylvain; Yang, Yongping

doi:10.1016/j.applthermaleng.2017.10.103

Cited by 46 publications

(18 citation statements)

References 34 publications

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“…The first approach, the analytical approach, uses empirical equations for heat transfer or pressure drop calculations, and ACC design data to describe the performance under off-design conditions [6,7,8]. However, the performance represented by empirical equations and design data, can give erratic indications because they do not take into account the particularity of the ACC installation site and due to performance degradation after a certain period of operation [4].…”

Section: Main Boundary Conditionsmentioning

confidence: 99%

“…steam mass flow rate, Steam turbine power, electrical load of the ACC fans). To understand the condenser's real performance under different situations, it is of great importance to investigate the relationship between the back pressure of the steam turbine and the condenser-related variables [4]. The most common continuous monitoring system usually poses on the assumption of no air accumulation within the condenser.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Air-Cooled Condenser Performance Assessment: Model and Input Variable Selection Comparison

Ghettini¹,

Sorce

Sacile

2020

E3S Web Conf.

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This paper presents a data–driven model for the estimation of the performance of an aircooled steam condenser (ACC) with the aim to develop an efficient online monitoring, summarized by the condenser pressure (or vacuum) as Key Performance Indicator. The estimation of the ACC performance model was based on different dataset from three different combined cycle power plants with a gross power of above 380 MWe each, focusing on stationary condition of the steam turbine. The datasets include both boundary (e.g. Ambient Temperature, Wind Speed) and operative parameters (e.g. steam mass flow rate, Steam turbine power, electrical load of the ACC fans) acquired from the power plants and some derived variable as the incondensable fraction, which calculation is here proposed as additional parameter. After a preliminary sensitivity analysis on data correlation, the paper focuses on the evaluation of different ACC Condenser models: Semi-Empirical model is described trough curves typically based on steam mass flow rate (or condenser load) and the ambient temperature as main parameters. Since monitoring based on ACC design curves Semi-Empirical models, provides biased poor results, with an error of about 15%, the curves parameters were estimated basing on training data set. Other two data driven models were presented, basing on a neural network modelling and multi linear regression technique and compared on the base of the reduced number of input at first and then including aldo the other process variables in the prediction of the condenser back pressure. Estimate the parameters of the Semi-Empirical model, results in a better prediction if just steam mass flow rate and ambient temperature are available, with an error of the 7%, thanks to the knowledge contained within the “curves shapes”, with respect to linear regression (8.3%) and Neural Network models (7.6%). Higher accuracy can be then obtained by considering a larger number of operative parameters and exploiting more complex data-driven model. With a higher number of features, the neural network model has proved a higher accuracy than the linear regression model. In fact, the mean percentage error of the NN model (2.6%), in all plant operating conditions, is slightly lower than the error of the linear regression model, but presents and much lower than the mean error of the Semi-Empirical model thanks to the additional data-based knowledge.

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Section: Main Boundary Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Air-Cooled Condenser Performance Assessment: Model and Input Variable Selection Comparison

Ghettini¹,

Sorce

Sacile

2020

E3S Web Conf.

View full text Add to dashboard Cite

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“…The stream data related to all regenerative heats (RHs) have been listed in Table 1 for the turbine heat rate acceptance (THA) condition, under which the inlet conditions and back pressure of the turbine are nominal with the regenerative feedwater preheaters operating normally. More detailed descriptions of 1000 MW-level coal-fired power plants can be found elsewhere in our publications, e.g., [ 58 , 59 , 60 , 61 , 62 , 63 ], and will not be repeated here.…”

Section: Description Of Case Studiesmentioning

confidence: 99%

Comparative Evaluation of Integrated Waste Heat Utilization Systems for Coal-Fired Power Plants Based on In-Depth Boiler-Turbine Integration and Organic Rankine Cycle

Huang

Tan

et al. 2018

Entropy

Self Cite

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To maximize the system-level heat integration, three retrofit concepts of waste heat recovery via organic Rankine cycle (ORC), in-depth boiler-turbine integration, and coupling of both are proposed, analyzed and comprehensively compared in terms of thermodynamic and economic performances. For thermodynamic analysis, exergy analysis is employed with grand composite curves illustrated to identify how the systems are fundamentally and quantitatively improved, and to highlight key processes for system improvement. For economic analysis, annual revenue and investment payback period are calculated based on the estimation of capital investment of each component to identify the economic feasibility and competitiveness of each retrofit concept proposed. The results show that the in-depth boiler-turbine integration achieves a better temperature match of heat flows involved for different fluids and multi-stage air preheating, thus a significant improvement of power output (23.99 MW), which is much larger than that of the system with only ORC (6.49 MW). This is mainly due to the limitation of the ultra-low temperature (from 135 to 75 • C) heat available from the flue gas for ORC. The thermodynamic improvement is mostly contributed by the reduction of exergy destruction within the boiler subsystem, which is eventually converted to mechanical power; while the exergy destruction within the turbine system is almost not changed for the three concepts. The selection of ORC working fluids is performed to maximize the power output. Due to the low-grade heat source, the cycle with R11 offers the largest additional net power generation but is not significantly better than the other preselected working fluids. Economically, the in-depth boiler-turbine integration is the most economic completive solution with a payback period of only 0.78 year. The ORC concept is less attractive for a sole application due to a long payback time (2.26 years). However, by coupling both concepts, a net power output of 26.51 MW and a payback time of almost one year are achieved, which may promote the large-scale production and deployment of ORC with a cost reduction and competitiveness enhancement.

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“…Ambient temperature (°C) Figure 6. The validation between the calculated and the measured values (also discussed in [41]). …”

Section: The Offline Phase For Performance Prediction Of the Turbine mentioning

confidence: 99%

“…For online analysis and decisions (step B2), the optimal controlling strategy of air fan frequency is an optimization problem and can be formulated as follows: Figure 6. The validation between the calculated and the measured values (also discussed in [41]). …”

Section: The Online Phase and Decision Making Of Optimal Air-fan Freqmentioning

confidence: 99%

The Application of Cyber Physical System for Thermal Power Plants: Data-Driven Modeling

Yang

et al. 2018

Energies

Self Cite

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Optimal operation of energy systems plays an important role to enhance their lifetime security and efficiency. The determination of optimal operating strategies requires intelligent utilization of massive data accumulated during operation or prediction. The investigation of these data solely without combining physical models may run the risk that the established relationships between inputs and outputs, the models which reproduce the behavior of the considered system/component in a wide range of boundary conditions, are invalid for certain boundary conditions, which never occur in the database employed. Therefore, combining big data with physical models via cyber physical systems (CPS) is of great importance to derive highly-reliable and -accurate models and becomes more and more popular in practical applications. In this paper, we focus on the description of a systematic method to apply CPS to the performance analysis and decision making of thermal power plants. We proposed a general procedure of CPS with both offline and online phases for its application to thermal power plants and discussed the corresponding methods employed to support each sub-procedure. As an example, a data-driven model of turbine island of an existing air-cooling based thermal power plant is established with the proposed procedure and demonstrates its practicality, validity and flexibility. To establish such model, the historical operating data are employed in the cyber layer for modeling and linking each physical component. The decision-making procedure of optimal frequency of air-cooling condenser is also illustrated to show its applicability of online use. It is concluded that the cyber physical system with the data mining technique is effective and promising to facilitate the real-time analysis and control of thermal power plants.

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A data-driven model for the air-cooling condenser of thermal power plants based on data reconciliation and support vector regression

Cited by 46 publications

References 34 publications

Data-Driven Air-Cooled Condenser Performance Assessment: Model and Input Variable Selection Comparison

Data-Driven Air-Cooled Condenser Performance Assessment: Model and Input Variable Selection Comparison

Comparative Evaluation of Integrated Waste Heat Utilization Systems for Coal-Fired Power Plants Based on In-Depth Boiler-Turbine Integration and Organic Rankine Cycle

The Application of Cyber Physical System for Thermal Power Plants: Data-Driven Modeling

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