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Biomass Combined Heat and Power (CHP) plants connected to district heating networks (DHN) are recognized as a very good opportunity to increase the share of renewable sources into energy systems. However, as CHP plants are not optimized for electricity production, their operation is profitable only if a sufficient heat demand is available throughout the year. On the other hand, these plants often work for baseline operations and back-up boilers are used to supply the peak demand. To extend the use of the CHP plants and reduce costs, conventional fuel use and emissions, it is proposed to study the feasibility of using the DHN itself or additional high temperature heat storage as retrofit of an existing CHP plant. This work is based on a simple and effective methodology that provides accurate estimations of economic, environmental and energetic performances of CHP plants connected to district heating networks. The focus is performed on the integration of the heat storage as retrofit of existing DHN considering the local policies. The DHN of the University in Liege (Belgium) is used as an application framework to demonstrate the effectiveness of the selected approach. The potential energy, pollutant emissions savings and resulting energy costs are estimated and the current policy limitations will be discussed.

Biomass Combined Heat and Power (CHP) plants connected to district heating networks (DHN) are recognized as a very good opportunity to increase the share of renewable sources into energy systems. However, as CHP plants are not optimized for electricity production, their operation is profitable only if a sufficient heat demand is available throughout the year. On the other hand, these plants often work for baseline operation and back-up boilers are used to supply the peak demand. To extend the use of the CHP plants and reduce costs, conventional fuel use and emissions, it is proposed to study the feasibility of using additional high temperature heat storage as retrofit of an existing CHP plant. This work is based on a simple and effective methodology that provides accurate estimations of economic, environmental and energetic performances of CHP plants connected to district heating networks. The DHN of the University in Liege (Belgium) is used as an application framework to demonstrate the effectiveness of the selected approach. The potential energy, pollutant emissions savings and resulting energy costs are estimated and the current policy limitations will be discussed.

Energy performance of buildings generally assesses the energy consumption of buildings such as heating, domestic heat water, ventilation systems, etc. However, this approach is based on the first law of thermodynamics and considers only the quantity of energy used without considering its ‘quality’ and leads to a lack of information about the energy conversion processes. This is particularly true in the new low-energy buildings where sometimes high temperatures sources are used to meet low-temperature needs. The exergy analysis of a system, based on first and second thermodynamic laws, can be used to overcome this. In this work, it is proposed to compare the energy and the exergy consumption and the related CO2 emissions of several kinds of buildings to determine the best systems in terms of energy and exergy needs. The energy demand calculations are performed using the official software available in Belgium and some assumptions are implemented to consider the exergy approach. As exergy calculations require a reference state, some different climatic conditions are also investigated. Finally, some conclusions are discussed to rank the sources of energy and their related exergy losses.

This paper presents temperature measurements in four Borehole Heat Exchangers (BHEs), equipped with fiber optics and located in a semi-urban environment (campus of the University of Liege, Belgium). A 3D numerical model is also presented to simulate the heat loss from the surrounding structures into the subsurface. The mean undisturbed ground temperature was estimated from data during the preliminary phase of a thermal response test (water circulation in the pipe loops), as well as from borehole logging measurements. The measurements during water circulation can significantly overestimate the ground temperature (up to 1.7 °C in this case study) for high ambient air temperature during the test, resulting in an overestimation of the maximum extracted power and of the heat pump coefficient of performance (COP). To limit the error in the COP and the extracted power to less than 5%, the error in the undisturbed temperature estimation should not exceed ±1.5 °C and ±0.6 °C respectively. In urbanised areas, configurations of short BHEs (length

While small and medium size biomass combined heat and power (CHP) plants (i.e., up to 5 MW of electrical rated power) represent an attractive option to exploit locally available biomass resources at low cost, the corresponding investment per unit of rated power significantly rises when the installed power decreases. In these cases, secondary pollutant emissions control measures are most of the time not economically viable and primary emissions control must be used alone to avoid the formation of undesired compounds such as NO x and SO x . Primary control measures require the careful optimization of fuel quality and combustion process. For plant operators, being able to accommodate biomass quality changes in order to minimize the fuel cost can be of great importance in order to guarantee the profitability of the plant. This contribution is dedicated to the development of a zero-dimensional (input–output) combustion simulation model able to predict the pollutants emissions resulting from complete and incomplete combustion with respect to varying combustion operation (ambient temperature, humidity, fumes recirculation, …). This tool is intended to be integrated in a global simulation model of the CHP plant and the attached district heating network installed on the University campus in Liege. Doing so, the plant operation can be optimized with respect to economic as well as environmental and energetic aspects (3E approach) thus ensuring the sustainability of the approach.

In the frame of turbine engine performance monitoring, system identification procedures are often used to adapt a simulation model of the engine to some observed data through a set of so-called health parameters. Doing so, the values of these health parameters are intended to represent the actual health condition of the engine. The Kalman filter has been widely used to achieve the identification procedure in real-time onboard applications. However, to achieve a proper filtering of the measurement noise, the health parameters are often assumed to vary in time relatively slowly, preventing any abrupt accidental events from being tracked effectively. This contribution presents a procedure called adaptive filtering. Based on a covariance-matching method, it is intended to automatically release the health parameters once an accidental event is detected. This enables the Kalman filter to deal with both continuous and abrupt fault conditions.

Based on actual operational and economic data of an existing Rankine cycle cogeneration plant connected to a district heating network, a detailed economic and exergetic evaluation is carried out in order to study the influence of the network temperature level of the cogeneration plant efficiency, namely the sum of the electrical and thermal efficiency.

Module performance analysis is a well-established framework to assess changes in the health condition of the components of the engine gas-path. The primary material of the technique is the so-called vector of residuals, which are built as the difference between actual measurement taken in the gas-path and the values predicted by means of an engine model. Obviously, the quality of the assessment of the engine condition depends strongly on the accuracy of the engine model. The present paper proposes a new approach for data-driven modeling of a fleet of engines of a given type. Such black-box models can be designed by operators, such as airlines and third-party companies. The fleet-wide modeling process is formulated as a regression problem that provides a dedicated model for each engine in the fleet, while recognizing that all engines are of the same type. The methodology is applied to a virtual fleet of engines generated within the Propulsion Diagnostic Methodology Evaluation Strategy (ProDiMES) environment. The set of models is assessed quantitatively through the coefficient of determination and is further used to perform anomaly detection.

A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Network (BBN) is presented. A soft-constrained Kalman filter uses a priori information derived by a BBN at each time step, to derive estimations of the unknown health parameters. The resulting algorithm has improved identification capability in comparison to the stand-alone Kalman filter. The paper focuses on a way of combining the information produced by the BBN with the Kalman filter. An extensive set of fault cases is used to test the method on a typical civil turbofan layout. The effectiveness of the method is thus demonstrated, and its advantages over individual constituent methods are presented.

Gas-path measurements used to assess the health condition of an engine are corrupted by noise. Generally, a data cleaning step occurs before proceeding with fault detection and isolation. Classical linear filters such as the exponentially weighted moving average filter are traditionally used for noise removal. Unfortunately, these low-pass filters distort trend shifts indicative of faults, which increases the detection delay. The present paper investigates two new approaches to non-linear filtering of time series. On one hand, the synthesis approach reconstructs the signal as a combination of elementary signals chosen from a pre-defined library. On the other hand, the analysis approach imposes a constraint on the shape of the signal (e.g., piecewise constant). Both approaches incorporate prior information about the signal in a different way, but they lead to trend filters that are very capable at noise removal while preserving at the same time sharp edges in the signal. This is highlighted through the comparison with a classical linear filter on a batch of synthetic data representative of typical engine fault profiles.

For turbine engine performance monitoring purposes, system identification techniques are often used to adapt a turbine engine simulation model to some measurements performed while the engine is in service. Doing so, the simulation model is adapted through a set of so-called health parameters whose values are intended to represent a faithful image of the actual health condition of the engine. For the sake of low computational burden, the problem of random errors contaminating the measurements is often considered to be zero mean, white, and Gaussian random variables. However, when a sensor fault occurs, the measurement errors no longer satisfy the Gaussian assumption and the results given by the system identification rapidly become unreliable. The present contribution is dedicated to the development of a diagnosis tool based on a Kalman filter whose structure is slightly modified in order to accommodate sensor malfunctions. The benefit in terms of the diagnostic reliability of the resulting tool is illustrated on several sensor faults that may be encountered on a current turbofan layout.

The article unveils the mystery of cogeneration. Cogeneration is an add-on or embedded activity in thermal power plants, with as merit the use of part or whole of their point source heat exhausts. EUs talk of high-efficiency cogeneration is an unfounded transfer of responsibility from the hosting thermal power generation plant onto CHP (Combined Heat & Power) activity. The quality of a CHP activity is univocally defined by its design power-to-heat ratio s, a tombstone parameter derived from the design characteristics of the power plant. A thermal power plant may house more than one cogeneration activity. Identifying s requires positioning the bliss point in the electricityeheat production possibility set of the cogeneration activity. The bliss point is where after electric output is maximized, the sum of that output and the maximum recoverable quantity of heat occurs. Once CHPs mystery of virtual bliss points is unveiled, the proper s are found. With known s by CHP activity, the quantity of cogenerated electricity is reliably assessed as best indicator of cogeneration performance. Our analysis is applicable on all relevant thermal power cycles that host CHP activities, and illustrated with a numerical example. Our lean method is necessary and sufficient for proper CHP regulation.

Least-squares health parameter identification techniques, such as the Kalman filter, have been extensively used to solve diagnosis problems. Indeed, such methods give a good estimate provided that the discrepancies between the model prediction and the measurements are zero-mean, white, Gaussian random variables. In a turbine engine diagnosis, however, this assumption does not always hold due to the presence of biases in the model. This is especially true for a transient operation. As a result, the estimated parameters tend to diverge from their actual values, which strongly degrades the diagnosis. The purpose of this contribution is to present a Kalman filter diagnosis tool where the model biases are treated as an additional random measurement error. The new methodology is tested on simulated transient data representative of a current turbofan engine configuration. While relatively simple to implement, the newly developed diagnosis tool exhibits a much better accuracy than the original Kalman filter in the presence of model biases. DOI: 10.1115/1.2833491

A common assumption made in the performance assessment of a turbine engine for aircraft propulsion consists in restricting the data processing to steady-state data. This especially holds for onboard performance monitoring of a commercial aircraft which spends up to 90% of the time in cruise flight where such conditions are satisfied. The present contribution is intended to investigate the ability of a diagnosis method to process unsteady data rather than steady-state data. The aim of this unsteady approach is to strongly reduce the time and the efforts spent to obtain a reliable diagnosis. In order to assess the improvements in terms of diagnosis efficiency and engine operability, the resulting diagnostic method is tested for different degradations that can be expected on commercial turbofans. The results are also compared to those obtained from cruise flight steady-state data in order to balance the two approaches.Copyright © 2005 by ASME

The purpose of this contribution is to apply ridge regression to Kalman filtering in order to stabilize a health parameter identification under low or negative redundancy. The resulting algorithm achieves a so-called soft-constrained recursive health parameter identification, i.e. constraints are applied to parameters in a statistical way, contrary to hard-constrained algorithms based on strong equality or inequality constrains. The method is tested on data generated by a steady state turbofan engine model and representing typical component faults. The benefits that can be realized in terms of stability and accuracy are highlighted and some limits of the method are also mentioned.Copyright © 2004 by ASME

In this contribution, an on-line engine performance monitoring is carried out through an engine health parameter estimation based on several gas path measurements. This health parameter estimation makes use of the analytical redundancy of an engine model and therefore implies the knowledge of the engine state. As the latter is a priori not known the second task is therefore an engine state variable estimation. State variables here designate working conditions such as inlet temperature, pressure, Mach number, rotational speeds, [[ellipsis]] Estimation of the state variables constitutes a general application of the Extended Kalman Filter theory, while the health parameter estimation is a classical recurrent regression problem. Recent advances in stochastic methods [1] show that both problems can be solved by two Kalman filters working jointly. Such filters are usually named Dual Kalman Filters. The present contribution aims at using a dual Kalman filter modified to provide robustness. This procedure should be able to cope with as much as 20 to 30% of faulty data. The resulting online method is applied to a turbofan model developed in the frame of the OBIDICOTE 1 project. Several tests are carried out to check the performance monitoring capability and the robustness that can be achieved.Copyright © 2003 by ASME

New heat conversion technologies need to be developed and improved to take advantage of the necessary increase in the supply of renewable energy. The Organic Rankine Cycle is well suited for these applications, mainly because of its ability to recover low-grade heat and the possibility to be implemented in decentralized lower-capacity power plants.In this paper, an overview of the different ORC applications is presented. A market review is proposed including cost figures for several commercial ORC modules and manufacturers. An in-depth analysis of the technical challenges related to the technology, such as working fluid selection and expansion machine issues is then reported. Technological constraints and optimization methods are extensively described and discussed. Finally, the current trends in research and development for the next generation of Organic Rankine Cycles are presented.