Techno-economic optimization and Nox emission reduction through steam injection in gas turbine combustion chamber for waste heat recovery and water production

Considering the persistent human need for electricity and fresh water, cogeneration systems based on the production of these two products have attracted the attention of researchers. This study investigates a cogeneration system of electricity and fresh water based on gas turbine (GT) as the prime mover. The wasted energy of the GT exhaust gases is absorbed by a heat recovery steam generator (HRSG) and supplies the superheat steam required by the steam turbine (ST). In order to produce fresh water, a multi-effect desalination (MED) system is applied. The motive steam required is provided by extracting steam from the ST. In order to reduce the environmental pollution of this cogeneration system, the steam injection method is proposed in the GT's combustion chamber (CC). This system is optimized by a multi-objective optimization tool based on the Genetic Algorithm (GA). The design variables include pressure ratio of compressor (CPR), inlet temperature of gas turbine (TIT), steam injection mass flow rate in the CC, HRSG operating pressure, HRSG evaporator pinch point temperature difference (PPTD), steam pressure of the MED ejector, ejector motive steam flow rate, number of MED effects, and return effect. The goals are to minimize the total cost rate (TCR), which includes the cost of initial investment and maintenance of the system, the cost of consumed fuel, and the cost of disposing of CO and NO pollutants, as well as maximizing the exergy efficiency. In the end, it is observed that the steam injection in the CC leads to the reduction of the mentioned pollutant index, and it is proposed as a suitable solution to reduce the pollution of the proposed cogeneration system.

Techno-economic and environmental optimization of a combined regenerated gas turbine and supercritical CO2 cycle based on methane and hydrogen mixture as fuel for environmental remediation

Achieving power generation systems with high efficiency and low emission of environmental pollutants is one of the requirements of a sustainable environment. In this study, a hybrid power generation system based on gas turbine (GT) with regenerator configuration is introduced. A supercritical carbon dioxide (sCO2) cycle is used to recover the waste heat from GT exhaust gases. In order to reduce the emission of environmental pollutants, the combination of hydrogen and methane is used as GT fuel. In order to investigate the effect of using hydrogen in the fuel composition, the fraction of hydrogen is changed between 0 and 50% and its effect on performance, environmental, and economic factors is investigated. Also, the effect of design parameters such as compressor pressure ratio (CPR) and turbine inlet temperature (TIT) of GT and sCO2 cycles on exergy efficiency, total cost rate (TCR), and normalized pollutants emission index are investigated. Then, by performing a bi-objective optimization process with the aim of achieving maximum exergy efficiency and minimum TCR, the optimal operating point is extracted. At the optimal operating point, exergy efficiency is 44% and TCR is 6 dollars/second.

Thermal and environmental optimization of an intercooled gas turbine toward a sustainable environment

In this article, in order to achieve a sustainable environment, the optimization of a GT equipped with intercooling of the compression process is discussed. To limit the exergy destruction in intercooling cooling process and also to reduce the heat dissipation in the environment, an ORC system is applied for heat recovery and more power generation. Decision variables include CPR, first stage CPR, TIT, intercooler effectiveness, HRVG pressure, and superheating degree. During a parametric study, the effect of decision variables on operating factors including exergy efficiency, TCR, and the normalized emission rate of environmental pollutants are investigated. Finally, by performing bi-objective optimization and considering exergy efficiency and TCR as OFs, optimal performance conditions are determined. Finally, it is observed that in optimum conditions, exergy efficiency is 33% and TCR is 0.9 $/s.

Multiobjective optimization of a cogeneration system based on gas turbine, organic rankine cycle and double-effect absorbtion chiller

Combined cooling, heating and power (CCHP) is one of methods for enhancing the efficiency of the energy conversion systems. In this study a CCHP system consisting of a gas turbin (GT) as the topping cycle, and an organic Rankine cycle (ORC) associated with double-effect absorbtion chiller (DEACH) is decisioned as the bottoming cycle to recover the waste heat from GT exhaust gas. The considered CCHP system is investigated to maintain electricity, heating and cooling demand of a town. A parametric study is investigated and the effect decision variables on the performance indicators including exergy efficiency, total cost rate (TCR), cooling capacity, and ORC power generation is examined. Decision variables of the ORC system consist of HRVG pressure, and condenser pressure and the DEACH including evaporator pressure, condseser pressure, concentration of the concentrated solution, concentration of the weak solution, and solution mass flow rate. Finally a multi-objective optimization performed using Genetic Algorithm (GA) and the optimal design point is selected. It is observed at the optimum point the exergy efficiency, TCR, and sustainability index are 17.56%, 74.49 $/h, and 1.21, respectively.

Integration of ion transport membrane with conventional powerplant to enhance the plant capacity with improved power production

Ion Transport Membrane (ITM) is an emerging technology for producing O₂ by separating air in its membrane. To decrease energy loss in air separation unit and to increase the overall efficiency of a power generation unit ITM is added with the gasification unit in this model. Ceramic materials are generally used to make the ion transport membrane that produces oxygen by conducting oxygen ions at a specified temperature. Potential advantages can be gained by integrating ITM technology with power generation units as 99% pure oxygen is produced from ITM. Using ITM air separator is more beneficial compared to cryogenic air separation as ITM technology helps to improve IGCC overall efficiency and also reduces plant auxiliaries than that of power generation systems integrated with cryogenic. This paper proposed a novel and effective integration of ITM, gas turbine, HRSG system, gas clean up system and gasification unit to produce sustainable energy. Environmental impacts are considered to design this integrated power generation unit. The proposed model achieved a high gross electric efficiency of 47.58% and high net power of 296730 kW which revealed its potentiality compared to available cryogenic ASU-based combine cycle power plants.

Periodic analysis on gas path fault diagnosis of gas turbines

The gas path fault diagnosis is considered widely to ensure the economy, safety and practicability of gas turbines. Traditional gas path diagnosis methods are vulnerable to various uncertainties, resulting in a deviation between the diagnostic results and the real states, which brings huge potential safety hazard to industrial production. Periodic analysis can suppress the uncertainty interference and extract accurately the features of performance parameters to improve the accuracy of health evaluation. Motivated by these, a novel periodic analysis method is proposed for detecting gas path faults, namely the changing periodicity of performance parameters representing the health state of gas turbine is detected to determine whether gas path fault occurs. It is theoretically analyzed that the relationship between the periodicity of observed performance parameters and that of boundary conditions, system uncertainties, and thermodynamic parameters. The simulation experiments are performed to analyze the effects of gas path faults on periodicity of boundary conditions, system uncertainties and thermodynamic parameters. The results show that most gas path faults break the periodicity of performance parameters, proving that the operating states can be monitored through the periodic analysis of performance parameters. An online diagnosis procedure is further proposed by combining signal decomposition and rolling periodic extraction method to judge whether the gas turbine is in health or not. The validity is verified by comparing the periodicity of performance parameters under healthy and fault states. Periodic analysis suppresses the effects of system and parameter uncertainties and detects sensitively gas path faults, which provides a new idea for the fault diagnosis of gas turbines.

Detection of Unit of Measure Inconsistency in gas turbine sensors by means of Support Vector Machine classifier

The reliability of gas turbine diagnostics clearly relies on reliable measurements. However, raw data reliability can be corrupted by label noise issues, as for instance an erroneous association between data and the respective unit of measure. Such issue, rarely investigated in the literature, is named Unit of Measure Inconsistency (UMI). Machine Learning classifiers are suitable tools to tackle the challenge of UMI detection. Thus, this paper investigates the capability of four Support Vector Machine approaches to detect UMIs. All approaches are tested on a dataset composed of field data taken on a fleet of Siemens gas turbines. The results of this study demonstrate that the Radial Basis Function with One-vs-One decomposition allows higher diagnostic accuracy.

Parametric thermodynamic analysis and economic assessment of a novel solar heliostat-molten carbonate fuel cell system for electricity and fresh water production

With the ever-rising paces of fuel consumption and CO₂ emission, the urge for renewable energy resources is becoming a challenge in today's world; especially for Iran that has started to reduce its fuel subsidies. The need for electricity and fresh water in the southern coastal regions of the country is increasing with the increase in the population. The high solar radiation level in the region is a promising alternative to mitigate the fuel consumption of the conventional power or desalination plants by the solar thermal source through the concentrated solar technology. In addition, the CO₂ emission of the aforementioned plants significantly diminishes by using the molten carbonate fuel cell that is suitable for the CO₂ capture. Furthermore, the combination of different power and water technologies, which are operating at different temperatures and pressures, leads to enhance the overall efficiency of the integrated systems. To this end, a novel integrated power/water plant comprising a solar tower, a molten carbonate fuel cell, a gas turbine, a solar Rankine cycle, an organic Rankine cycle, a multi-effect distillation, and reverse osmosis desalination was techno-economically investigated. The multi-objective genetic algorithm was used to find the optimum configuration of the system with the low amount of CO₂ emissions, and low unit costs of the electricity and fresh water. The results showed that the most effective parameter on system performance is the operating pressure of the molten carbonate fuel cell. For the optimum configurations of the system, the electricity unit of the cost was found as a value between 0.022 and 0.025 $/kWh. Part of the electricity unit of the cost that is associated with the output power that is generated based on solar thermal energy was obtained as a value between 0.08 and 0.092 $/kWh. In addition, the average unit cost of fresh water was obtained as 1.21 $/m³. The payback period of the system was obtained as 10.44 years if the electricity and fresh water are sold as 0.023 $/kWh and 1.21 $/m³. This can be reduced to 2.88 years for the electricity and fresh water selling prices of 0.069 $/kWh and 1.40 $/m³, respectively. Based on the results, the system with the solar thermal resource will be economically justifiable if the fuel price is increased.

Fault detection and isolation of gas turbine using series-parallel NARX model

This paper describes the design and implementation of intelligent dynamic models for fault detection and isolation of V94.2(5)/MGT-70(2) single-axis heavy-duty gas turbine system. The series-parallel structure of nonlinear autoregressive exogenous (NARX) models are used for fault detection, which initiate greater robustness and stability against uncertainties and perturbations. Moreover, to improve the fault detection robustness against uncertainties, the Monte Carlo technique is used in the proposed fault detection structure to select the best threshold. The analysis of fault detectability and fault detection sensitivity are accomplished to analyze the performance of the suggested technique. The fault isolation process is also achieved by using the residual classification approach. The results show the feasibly, robustness, and performance of the presented approach for fault diagnosis of nonlinear systems in the presence of uncertainties.

A model for booster station matching of gas turbine and gas compressor power under different ambient conditions

Transporting natural gas across different locations require compressor stations to provide the pressure needed to keep the gas moving. This paper presents a model for matching the gas turbine and gas compressor power required under different environmental conditions to support the continuous gas transmission across other locations. The trans-Saharan gas pipeline (TSGP) project proposed to transport gas from Nigeria to Algeria has been used as a case study in this paper. The TSGP project is a Nigerian Government initiative to rejig its gas development and transportation infrastructure to meet its internal and external market demand. The numerical method used in this paper integrates the effect of the ambient temperature in the power matching of the gas turbine and gas compressors. There are 18 compressor stations across the TSGP network, and compressor station 2 is used as the reference point. The daily temperature fluctuation is segmented into hours of the day, emphasising considerable ambient temperature variation at 3:00 h, 9:00 h, 15:00 h. One benefit of the model against others in the open literature is accounting for changes in the ambient temperature along the pipeline network and gas compression stations. Accounting for changes in ambient temperature provides accuracy to near real-life operational experience for gas distribution via pipelines. The model also accounts for variations in turbine entry temperature (TET) to compensate for changes in the ambient conditions to meet the power requirements of the gas turbine and the gas compressor. The results show that for every 1% increase in ambient temperature, a 3.5% increase in power is required to drive the gas compressor and a 1% decrease in gas turbine output power. The effect of the 1% increase in ambient would require a 3.5% increase in TET to meet both the gas turbine and gas compressor requirement.

Control performance enhancement of gas turbines in the minimum command selection strategy

Three novel methods, named α, ζ and ϵ, are suggested in this paper to recover the performance loss during switching in the gas turbine control systems. The Minimum Command Selection (MCS) in the gas turbine control systems prompts this performance loss. Any step towards more productivity with less aging factors have a great impact on the gas turbine's lifetime profit and vice versa. Although many hardware upgrades have been studied and applied to accomplish this, in many cases a low-risk manipulation in the software may yield equivalent achievement. State of the art gas turbine control systems are supposed to handle various forms of disturbances, several operation modes and relatively high transients of the gas turbines. The proposed methods dynamically limit the inactive control loop command and utilize the corresponding loop error to optimally switch the loops. The optimality infers a fuzzy choice based on the designated performance criteria. They demonstrate enhanced performance in comparison with conventional techniques such as static or dynamic saturation proportion to active command, integrator fast rewind, and PI tracking mode. An identified model of W251-B2 gas turbine with robust controllers is exploited to evaluate the empirical authenticity. They exhibit superior performance in comparison with traditional MCS and decrease the over-temperature around 9^oC[2%], as the load control switches to the temperature control. The proposed methods provide pragmatic and promising tools in the designer's hands to adapt the methods based on the application requirements.

A comparative study of the energy, exergetic and thermo-economic performance of a novelty combined Brayton S-CO2-ORC configurations as bottoming cycles

This paper presents a comparative study on the energy, exergetic and thermo-economic performance of a novelty thermal power system integrated by a supercritical CO2 Brayton cycle, and a recuperative organic Rankine cycle (RORC) or a simple organic Rankine cycle (SORC). A thermodynamic model was developed applying the mass, energy and exergy balances to all the equipment, allowing to calculate the exergy destruction in the components. In addition, a sensitivity analysis allowed studying the effect of the primary turbine inlet temperature (TIT, PHIGH, rP and TC) on the net power generated, the thermal and exergy efficiency, and some thermo-economic indicators such as the payback period (PBP), the specific investment cost (SIC), and the levelized cost of energy (LCOE), when cyclohexane, acetone and toluene are used as working fluids in the bottoming organic Rankine cycle. The parametric study results show that cyclohexane is the organic fluid that presents the best thermo-economic performance, and the optimization with the PSO method conclude a 2308.91 USD/kWh in the SIC, 0.22 USD/kWh in the LCOE, and 9.89 year in the PBP for the RORC system. Therefore, to obtain technical and economic viability, and increase the industrial applications of these thermal systems, thermo-economic optimizations must be proposed to obtain lower values of the evaluated performance indicators.

Nonlinear robust fault diagnosis of power plant gas turbine using Monte Carlo-based adaptive threshold approach

This paper addresses the robust fault diagnosis of power plant gas turbine as an uncertain nonlinear system using a new adaptive threshold method. In order to determine the bounds of the adaptive threshold and to identify neural network thresholds modelling, an approach based on Monte Carlo simulation is employed. To evaluate the performance of the proposed fault detection method, a fault sensitivity analysis is provided. In addition, the neural network-based estimators are considered to estimate the magnitude of faults according to the values of residuals. The proposed fault diagnosis system is evaluated during different scenarios. The obtained results indicate the high sensitivity, accuracy, and robustness of the proposed method for fault detection and isolation in the nonlinear uncertain systems, even in dealing with small faults.

A unified acceptance test framework for power plant gas turbine control systems

Renovation and retrofit of gas turbine control systems yield significant economic savings, enhanced reliability, and improved performance. In recent years, the gas turbine industry is increasingly facing the need to well-established procedures for the acceptance tests of renovated control systems. This paper proposes a unified framework to evaluate the performance of renovated gas turbine control systems. Under a set of assumptions on the ambient and fuel conditions, a low-complexity modular model is presented and identified using optimization-oriented identification techniques. The accuracy of the proposed model is validated through experimental studies in full-load, min-load, and no-load operating conditions. Subsequently, a model-based analysis framework is proposed to determine realistic levels of tracking performance, robustness margin and disturbance attenuation by utilizing the supporting tools in robust control theory. Quantitative and qualitative performance indices are introduced to provide acceptance criteria for the existing control loops as compared to the optimal ones. The proposed procedure is applied to a W251-B2 gas turbine manufactured by Westinghouse company, and the results show that the optimal control system outperforms the existing controllers based on quantitative and qualitative indices. The proposed procedure determines whether the overall performance of the renovated control system sufficiently meets the requirements.

Effect of an upstream bulge configuration on film cooling with and without mist injection

To meet the economic requirements of power output, the increased inlet temperature of modern gas turbines is above the melting point of the material. Therefore, high-efficient cooling technology is needed to protect the blades from the hot mainstream. In this study, film cooling was investigated in a simplified channel. A bulge located upstream of the film hole was numerically investigated by analysis of the film cooling effectiveness distribution downstream of the wall. The flow distribution in the plate channel is first presented. Comparing with a case without bulge, different cases with bulge heights of 0.1d, 0.3d and 0.5d were examined with blowing ratios of 0.5 and 1.0. Cases with 1% mist injection were also included in order to obtain better cooling performance. Results show that the bulge configuration located upstream the film hole makes the cooling film more uniform, and enhanceslateral cooling effectiveness. Unlike other cases, the configuration with a 0.3d-height bulge shows a good balance in improving the downstream and lateral cooling effectiveness. Compared with the case without mist at M = 0.5, the 0.3d-height bulge with 1% mist injection increases lateral average effectiveness by 559% at x/d = 55. In addition, a reduction of the thermal stress concentration can be obtained by increasing the height of the bulge configuration.

A hybrid clustering approach for multivariate time series - A case study applied to failure analysis in a gas turbine

A clustering problem involving multivariate time series (MTS) requires the selection of similarity metrics. This paper shows the limitations of the PCA similarity factor (SPCA) as a single metric in nonlinear problems where there are differences in magnitude of the same process variables due to expected changes in operation conditions. A novel method for clustering MTS based on a combination between SPCA and the average-based Euclidean distance (AED) within a fuzzy clustering approach is proposed. Case studies involving either simulated or real industrial data collected from a large scale gas turbine are used to illustrate that the hybrid approach enhances the ability to recognize normal and fault operating patterns. This paper also proposes an oversampling procedure to create synthetic multivariate time series that can be useful in commonly occurring situations involving unbalanced data sets.

Assessment of Finite Rate Chemistry Large Eddy Simulation Combustion Models

Large Eddy Simulations (LES) of a swirl-stabilized natural gas-air flame in a laboratory gas turbine combustor is performed using six different LES combustion models to provide a head-to-head comparative study. More specifically, six finite rate chemistry models, including the thickened flame model, the partially stirred reactor model, the approximate deconvolution model and the stochastic fields model have been studied. The LES predictions are compared against experimental data including velocity, temperature and major species concentrations measured using Particle Image Velocimetry (PIV), OH Planar Laser-Induced Fluorescence (OH-PLIF), OH chemiluminescence imaging and one-dimensional laser Raman scattering. Based on previous results a skeletal methane-air reaction mechanism based on the well-known Smooke and Giovangigli mechanism was used in this work. Two computational grids of about 7 and 56 million cells, respectively, are used to quantify the influence of grid resolution. The overall flow and flame structures appear similar for all LES combustion models studied and agree well with experimental still and video images. Takeno flame index and chemical explosives mode analysis suggest that the flame is premixed and resides within the thin reaction zone. The LES results show good agreement with the experimental data for the axial velocity, temperature and major species, but differences due to the choice of LES combustion model are observed and discussed. Furthermore, the intrinsic flame structure and the flame dynamics are similarly predicted by all LES combustion models examined. Within this range of models, there is no strong case for deciding which model performs the best.

Optimization of CCGT power plant and performance analysis using MATLAB/Simulink with actual operational data

In the Modern scenario, the naturally available resources for power generation are being depleted at an alarming rate; firstly due to wastage of power at consumer end, secondly due to inefficiency of various power system components. A Combined Cycle Gas Turbine (CCGT) integrates two cycles- Brayton cycle (Gas Turbine) and Rankine cycle (Steam Turbine) with the objective of increasing overall plant efficiency. This is accomplished by utilising the exhaust of Gas Turbine through a waste-heat recovery boiler to run a Steam Turbine. The efficiency of a gas turbine which ranges from 28% to 33% can hence be raised to about 60% by recovering some of the low grade thermal energy from the exhaust gas for steam turbine process. This paper is a study for the modelling of CCGT and comparing it with actual operational data. The performance model for CCGT plant was developed in MATLAB/Simulink.