13,345 research outputs found

    Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants

    Get PDF
    An effective modeling technique is proposed for determining baseline energy consumption in the industry. A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the current consumption and production in the event that no energy-saving measures had been implemented. Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable accuracy levels of prediction are detected, confirming good capability of the models for predicting plant behavior and their suitability for baseline energy consumption determining purposes. High level of robustness is observed for ANN against uncertainty affecting measured values of variables used as input in the models. The study demonstrates ANN great potential for assessing baseline consumption in energyintensive industry. Application of ANN technique would also help to overcome the limited availability of on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes

    Dispersed storage and generation case studies

    Get PDF
    Three installations utilizing separate dispersed storage and generation (DSG) technologies were investigated. Each of the systems is described in costs and control. Selected institutional and environmental issues are discussed, including life cycle costs. No unresolved technical, environmental, or institutional problems were encountered in the installations. The wind and solar photovoltaic DSG were installed for test purposes, and appear to be presently uneconomical. However, a number of factors are decreasing the cost of DSG relative to conventional alternatives, and an increased DSG penetration level may be expected in the future

    An agent-based implementation of hidden Markov models for gas turbine condition monitoring

    Get PDF
    This paper considers the use of a multi-agent system (MAS) incorporating hidden Markov models (HMMs) for the condition monitoring of gas turbine (GT) engines. Hidden Markov models utilizing a Gaussian probability distribution are proposed as an anomaly detection tool for gas turbines components. The use of this technique is shown to allow the modeling of the dynamics of GTs despite a lack of high frequency data. This allows the early detection of developing faults and avoids costly outages due to asset failure. These models are implemented as part of a MAS, using a proposed extension of an established power system ontology, for fault detection of gas turbines. The multi-agent system is shown to be applicable through a case study and comparison to an existing system utilizing historic data from a combined-cycle gas turbine plant provided by an industrial partner

    Monitoring and operation of a synchronous generator on short circuit on the rotor of a combined cycle power plant

    Get PDF
    The aim of this project is the monitoring of a round rotor synchronous generator in a C.C.G.T. power plant, through an innovative digital supervision system that utilizes a magnetic flux probe and a grounding device. Substantially the monitoring system will deploy a magnetic flux probe because, from an evaluation of the flux generated by the rotor winding (and other signals), it is possible to understanding if there are shorted turns on the rotor windingopenEmbargo per motivi di segretezza e/o di proprietà dei risultati e informazioni di enti esterni o aziende private che hanno partecipato alla realizzazione del lavoro di ricerca relativo alla tes

    NARX models for simulation of the start-up operation of a single-shaft gas turbine

    Get PDF
    In this study, nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine (GT) are developed and validated. The GT is a power plant gas turbine (General Electric PG 9351FA) located in Italy. The data used for model development are three time series data sets of two different maneuvers taken experimentally during the start-up procedure. The resulting NARX models are applied to three other experimental data sets and comparisons are made among four significant outputs of the models and the corresponding measured data. The results show that NARX models are capable of satisfactory prediction of the GT behavior and can capture system dynamics during start-up operation

    Health and safety: Preliminary comparative assessment of the Satellite Power System (SPS) and other energy alternatives

    Get PDF
    Data readily available from the literature were used to make an initial comparison of the health and safety risks of a fission power system with fuel reprocessing; a combined-cycle coal power system with a low-Btu gasifier and open-cycle gas turbine; a central-station, terrestrial, solar photovoltaic power system; the satellite power system; and a first-generation fusion system. The assessment approach consists of the identification of health and safety issues in each phase of the energy cycle from raw material extraction through electrical generation, waste disposal, and system deactivation; quantitative or qualitative evaluation of impact severity; and the rating of each issue with regard to known or potential impact level and level of uncertainty

    Waste heat recovery via organic rankine cycle: results of a era-SME technology transfer project

    Get PDF
    The main goal of the EraSME project “Waste heat recovery via an Organic Rankine Cycle”, completed by partners Howest (Belgium), Ghent University (Belgium) and University of Applied Sciences Stuttgart (Germany) between 1 January 2010 and 31 December 2012, was to find an entrance in Flanders for the Organic Rankine Cycle (ORC) technology in applications with sufficient amounts of waste heat at high enough temperatures. The project was preceded by a similar study that focused on renewable energy sources. Several tools were developed to aid in the viability assessment, the selection, and the sizing of ORC installations. With these methods, a fast determination of feasibility is possible. The outcome is based on the size, nature and temperature of the waste heat stream as well as the electricity price. An estimate can be given of the net power output, the investment costs and the economic feasibility. The tool is linked to a database of ORC manufacturer specifications. Another objective of the project was to keep track of the evolution in ORC market supply, both commercial and precommercial. We also looked beyond the product line of the main manufacturers. Some ORCs are developed for specific applications. ORC technology was benchmarked against alternatives for waste heat recovery, such as: steam turbines, heat pumps and absorption cooling. ORC in or as a combined heat and power (CHP) system was also examined. A laboratory test unit of 10kWe nominal power was installed during the project, which is now used in further research on dynamic behavior and control. It is still the only ORC demonstration unit in Flanders and has been very instructive in introducing representatives from industry, researchers and students to the technology. A considerable part of the project execution consisted of case studies in response to industrial requests from several sectors. Detailed and concrete feasibility studies allowed us to define the current application area of waste heat recovery ORC in a better way. A knowledge center for waste heat recovery (www.wasteheat.eu) was initiated to consolidate the know-how and to advise potential users

    REAL TIME PROGNOSTIC STRATEGIES APPLICATION TO GAS TURBINES

    Get PDF
    Gas turbines are increasingly deployed throughout the world to provide electrical and mechanical power in consumer and industrial sectors. The efficiency of these complex multi-domain systems is dependant on the turbine\u27s design, established operating envelope, environmental conditions, and maintenance schedule. A real-time health management strategy can enhance overall plant reliability through the continual monitoring of transient and steady-state system operations. The availability of sensory information for control system needs often allow diagnostic/prognostic algorithms to be executed in a parallel fashion which warn of impending system degradations. Specifically, prognostic strategies estimate the future plant behavior which leads to minimized maintenance costs through timely repairs, and hence, improved reliability. A health management system can incorporate prognostic algorithms to effectively interpret and determine the healthy working span of a gas turbine. The research project\u27s objective is to develop real-time monitoring and prediction algorithms for simple cycle natural gas turbines to forecast short and long term system behavior. Two real-time statistical and wavelet prognostic methods have been investigated to predict system operation. For the statistical approach, a multi-dimensional empirical description reveals dominant data trends and estimates future behavior. The wavelet approach uses second and fourth-order Daubechies wavelet coefficients to generate signal approximations that forecast future plant operation. To complement the empirical models, a real-time analytical, lumped parameter mathematical model has been developed that describes normal transient and steady-state gas turbine system operation. The model serves as the basis to understand a simple cycle gas turbine\u27s operation, and may be utilized in model-based diagnostic algorithms. To validate the model and the prognostic strategies, extensive data has been gathered for a 4.5 MW Solar Mercury 50 and a 85 MW General Electric 7EA simple cycle gas turbine. For the dynamic gas turbine model, the comparison between the field data and simulation results for five Mercury 50 gas turbine signals (e.g., shaft speed, power, fuel flow, turbine rotor inlet temperature, and compressor delivery pressure) demonstrate a high degree of correspondence. Although there are some deviations between the analytical and experimental results during the transient phase, the estimated steady state results are within 2.0% of the actual data. The direct comparison of the two forecasting methods revealed that the wavelet method is superior since the forecasting error is 2.4% versus 4.0% for the statistical method on the Mercury 50 simple cycle gas turbine steady-state signals (e.g., compressor delivery pressure and turbine rotor inlet temperature). Similarly, the General Electric 7EA steady-state signal (e.g., turbine inlet temperature) offered a forecasting error of 9.23% for the wavelet and 11.47% for the statistical methods, respectively. The developed approaches successfully estimate and predict the system operation and may be used with a diagnostic algorithm to monitor gas turbine system health. An excellent opportunity exists to apply the algorithms to gas turbines for improved operation and reliability

    Integrated control and health management. Orbit transfer rocket engine technology program

    Get PDF
    To insure controllability of the baseline design for a 7500 pound thrust, 10:1 throttleable, dual expanded cycle, Hydrogen-Oxygen, orbit transfer rocket engine, an Integrated Controls and Health Monitoring concept was developed. This included: (1) Dynamic engine simulations using a TUTSIM derived computer code; (2) analysis of various control methods; (3) Failure Modes Analysis to identify critical sensors; (4) Survey of applicable sensors technology; and, (5) Study of Health Monitoring philosophies. The engine design was found to be controllable over the full throttling range by using 13 valves, including an oxygen turbine bypass valve to control mixture ratio, and a hydrogen turbine bypass valve, used in conjunction with the oxygen bypass to control thrust. Classic feedback control methods are proposed along with specific requirements for valves, sensors, and the controller. Expanding on the control system, a Health Monitoring system is proposed including suggested computing methods and the following recommended sensors: (1) Fiber optic and silicon bearing deflectometers; (2) Capacitive shaft displacement sensors; and (3) Hot spot thermocouple arrays. Further work is needed to refine and verify the dynamic simulations and control algorithms, to advance sensor capabilities, and to develop the Health Monitoring computational methods

    Performance based diagnostics of a twin shaft aeroderivative gas turbine: water wash scheduling

    Get PDF
    Aeroderivative gas turbines are used all over the world for different applications as Combined Heat and Power (CHP), Oil and Gas, ship propulsion and others. They combine flexibility with high efficiencies, low weight and small footprint, making them attractive where power density is paramount as off shore Oil and Gas or ship propulsion. In Western Europe they are widely used in CHP small and medium applications thanks to their maintainability and efficiency. Reliability, Availability and Performance are key parameters when considering plant operation and maintenance. The accurate diagnose of Performance is fundamental for the plant economics and maintenance planning. There has been a lot of work around units like the LM2500® , a gas generator with an aerodynamically coupled gas turbine, but nothing has been found by the author for the LM6000® . Water wash, both on line or off line, is an important maintenance practice impacting Reliability, Availability and Performance. This Thesis aims to select and apply a suitable diagnostic technique to help establishing the schedule for off line water wash on a specific model of this engine type. After a revision of Diagnostic Methods Artificial Neural Network (ANN) has been chosen as diagnostic tool. There was no WebEngine model available of the unit under study so the first step of setting the tool has been creating it. The last step has been testing of ANN as a suitable diagnostic tool. Several have been configured, trained and tested and one has been chosen based on its slightly better response. Finally, conclusions are discussed and recommendations for further work laid out
    corecore