Industrial Steam Turbine Research Articles

Development of a new expression for predicting wet steam loss coefficient in steam turbines based on CFD and symbolic regression

A significant portion of the total losses in steam turbines is attributed to the thermodynamic losses of wet steam flow, which makes its prediction crucial in the design process. The Baumann rule has been used extensively in the literature, but its accuracy in some cases needs to be better due to the constant wet steam loss coefficient (α=1). To overcome the shortcomings of this rule, genetic symbolic regression is employed to derive a general expression for predicting wet steam loss for the first time. Therefore, based on Sobol sampling, 2080, two-dimensional CFD simulations for both reaction and impulse blades with different boundary conditions were performed. The data were employed to devise a new expression for wet steam loss prediction. The novel expression is verified using an in-house mean-line code on an industrial steam turbine. The new expression outperforms the Baumann rule by up to 2 % in efficiency prediction and 46 % in design pressure ratio prediction.

Condition Evaluation of Steam Turbine Generator Using Minute-Interval Integrated Vibration Signals

Vibration levels are a primary indicator for condition monitoring of Electrical Machine (EM) systems. This work employs Singular Spectrum Analysis (SSA) to analyze vibration time series of shaft and bearings of a real 250MW industrial power plant steam turbine Synchronous Generator (SG) in Greece. The SG operates under reduced power due to increased vibration conditions according to threshold guidelines. This work attempts to retrieve fault indications from the minute-interval integrated signal sample via its trend and periodicity components to predict vibrations under full load operation and establish correlations to discern whether the reduced output is justified. The successful application of this novel procedure is important due to ease of storing and handling low frequency signals, especially in remote environments. Rules are established using fuzzy logic due to the nature of the dataset and industrial application. The predictor employed is based on Sugeno-based Adaptive Neuro Fuzzy Inference System (ANFIS).

Deep Learning Algorithms to Predict Output Electrical Power of an Industrial Steam Turbine

Among the levers carried in the era of Industry 4.0, there is that of using Artificial Intelligence models to serve the energy interests of industrial companies. The aim of this paper is to estimate the active electrical power generated by industrial units that self-produce electricity. To do this, we conduct a case study of the historical data of the variables influencing this parameter to support the construction of three analytical models three analytical models based on Deep Learning algorithms, which are Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), as well as the hybrid CNN algorithm coupled with LSTM (CNN-LSTM). Subsequently, and thanks to the evaluation of the created models through three mathematical metrics which are Root Mean Square Error (RMSE), Mean Square Error (MSE), and the variance score (R-squared), we were able to make a comparative study between these models. According to the results of this comparison, we attested that the hybrid model is the one that gives the best prediction results, with the following findings: the variance score was about 98.29%, the value of RMSE was exactly 0.1199 MW, and for MSE the error was equal to 0.0143 MW. The obtained results confirm the reliability of the hybrid model, which can help industrial managers save energy by acting upstream of the process parameters influencing the target variable and avoiding substantial energy bills.

On the possibility of using an industrial steam turbine as an air expander in a Compressed Air Energy Storage plant

Small/medium size CAES systems (1–10 MW) could be efficiently and successfully employed for off-grid and self-consumption applications and for the delivery of ancillary services on the lower grid levels. A critical issue affecting the feasibility of such systems is related to the availability of efficient and affordable air expanders. Taking into consideration that typical CAES applications are characterized by inlet pressure levels in the range of 40–60 bar, an attractive opportunity to reduce development efforts and investment costs is to resort to the consolidated steam turbine engineering practice. In the present paper, the possibility of using existing building blocks developed to assemble industrial steam turbines to arrange air expanders for CAES applications is explored. A general model for off-design calculation capable of simulating, for a given turbine geometry, steam and air operations as well, has been developed and applied to a case study. On the basis of available information about an existing industrial steam turbine, a possible arrangement for an air expander has been set up and investigated. Results have evidenced a performance loss in terms of both power output and efficiency with respect to steam operations. Nevertheless, the air expander behavior in a wide range of operation can be considered satisfactory. Therefore, the use of industrial steam turbines technology might be considered interesting for the applications under consideration.

Data-driven modeling method with reverse process

The factors that affect the performance of the equipment are numerous and complicated, which makes it difficult to establish a performance calculation model. This paper puts forward a data-driven modeling method with reverse process for this problem. Based on the partial least squares (PLS) algorithm and the gray relational analysis (GRA) method, the analysis method of the performance related factors, the extraction method of characteristic variables, and the performance modeling method are studied. The related factors of the energy consumption of an industrial steam turbine are analyzed, and an energy consumption calculation model is established, and the effectiveness of the above-mentioned modeling methods is verified with sample data, which provides a basis for the energy-saving optimization of the steam turbine.

Artificial Intelligence Modelling Based Optimization of an Industrial Scale Steam Turbine for Moving Towards Net-Zero in the Energy Sector

Artificial Intelligence Modelling Based Optimization of an Industrial Scale Steam Turbine for Moving Towards Net-Zero in the Energy Sector

Adiabatic compressed air energy storage technology

Adiabatic compressed air energy storage technology

Development of integrated technology for waste heat recovery from humid flue gas of hot water boiler

For coal-fired hot water boilers, we developed a comprehensive waste heat recovery system coupled with a packed tower, industrial steam turbine, and absorption heat pump, which can directly reduce the auxiliary power consumption, realize energy cascade utilization, and waste heat recovery. The ASPEN Plus software was used to simulate the packed tower, absorption heat pump, and industrial steam turbine system. The design parameters of the system were obtained and the operation characteristics of the system under load rates of 25%, 50%, 75%, and 100% were investigated, the feasibility of steam subsystem transformation was also analyzed. Taking a 140 MW coal-fired boiler as an example, in the range of 25%~100% load rate, the direct energy saving can reach 118 to 523 kW∙h, the waste heat recovery power is 2.7 to 11.4 MW, and the coal saving can reach 374 to 1605 kg/h, resulting in energy and resource consumption reduction effect, the system also has CO2 and pollutant emission reduction effect. The RadFrac model can be used in packed tower design, the system can work well under variable load conditions.

Scaling of Steam Turbine Control Valve Guidance Pins

Abstract Severe scaling caused the guiding pin of two control valves of a smaller industrial steam turbine to seize which thus led to a malfunction. The customer sought clarification on whether the oxidation products are really common scale. This could be confirmed.

Investigation of Unsteady Pressure Fluctuations in a Simplified Steam Turbine Control Valve

Abstract Steam turbines are among the most important systems in commercial and industrial power conversion. As the amount of renewable energies increases, power plants formerly operated at steady-state base load are now experiencing increased times at part load conditions. Besides other methods, the use of control valves is a widely spread method for controlling the power output of a steam turbine. In difference to other throttling approaches, the control valve enables fast load gradients as the boiler can be operated at constant conditions and allows a quicker response on variable power requirements. At part load, a significant amount of energy is dissipated across the valve, as the total inlet pressure of the turbine is decreased across the valve. At these conditions, the flow through the valve becomes trans- and supersonic and large pressure fluctuations appear within the downstream part of the valve. As a result, unsteady forces are acting on the valve structure and vibrations can be triggered, leading to mechanical stresses and possible failures of the valve. Besides more complex valve geometries, a spherical valve shape is still often used in smaller and industrial steam turbines. Because of the smooth head contour, the flow is prone to remain attached to the head surface and interact with the flow coming from the opposite side. This behavior is accompanied by flow instabilities and large pressure fluctuations, leading to unsteady forces and possible couplings with mechanical frequencies. The spherical valve shape was therefore chosen as the experimental test geometry for the investigation of the unsteady flow field and fluid–structure interactions within a scaled steam turbine control valve. Using numerical methods, the test valve is investigated and the time dependent pressure distribution in the downstream diffuser is evaluated. The evolution of the flow stability will be discussed for different pressure ratios (PRs). Pressure signals retrieved from the control valve test rig will be used to compare the numerical results with the experimental data.

SMS–A Security Management System for Steam Turbines Using a Multisensor Array

Cyber-physical systems, such as large power plants, apply open networks for monitoring and control purposes. This may increase the risk of cyberattacks to these infrastructures. Cybersecurity methods have been employed as promising techniques to deal with cyber threats and isolate possible cyberattacks. This article introduces a new security management system (SMS) for an industrial steam turbine. As such, the most probable threats, such as denial-of-service (DoS) attack, deception attack, and replay attack in various sensors and actuators of the steam turbine system, are considered. Then, a new SMS system consisting of an attack detection unit and an attack isolation unit is designed. The attack detection unit utilizes a dynamic neural network to detect any potential attack in the system using the concept of residual generation. The attack isolation unit identifies the type of attacks using an integrated feature selection strategy and support vector machine classifier through multisensor information. Several case studies are investigated to evaluate the proposed SMS. The test results show the effectiveness of the proposed SMS with multisensor information when compared to SMS without multisensor array.

Performance analysis of industrial steam turbines used as air expander in Compressed Air Energy Storage (CAES) systems

The Authors consider that as a mid-term storage technology (from some hours to some days), small/medium size CAES systems (1–10 MW) might be efficiently and profitably used for off-grid and self-consumption applications as well as for the provision of ancillary services on the lower grid levels. A crucial aspect related to the feasibility of the system concerns with the availability of efficient and affordable air expanders. For typical applications, suitable values for pressure at the air expander inlet range from 40 to 60 bar. Since such values are comparable with those featuring industrial steam turbines, an interesting option to reduce development efforts and costs is to take as a reference the common steam turbine engineering practice. The goal of the present paper is to explore the possibility of using existing modules developed by industrial steam turbines manufacturers to assemble the air expander. On the basis of available information, a possible arrangement for an air turbine has been set up and modeled. Results show a loss of performance in terms of efficiency in respect to steam operations. Nevertheless, the performance level in a really wide range of operation can be considered more than satisfactory. Therefore, the use of industrial steam turbines technology might be considered of interest for the intended applications.

Corrosion fatigue failure of steam turbine moving blades: A case study

This paper describes the failure of a rotor of a condensing industrial steam turbine installed in a fertilizer production plant. There was a fracture of the two adjacent rotor blades in their roots. An analysis of the failure cause was carried out, which included the analysis of the fracture location on the steam flow path, the corrosion deposit analysis and the modal analysis of the rotor blades. Modal analysis was performed using the finite element method. It was concluded that the cause of the blades failure was a phenomenon known as corrosion fatigue. To extend the useful fatigue life of rotor blades in the future, the entire turbine stage of broken blades was redesigned. The modified design of the turbine stage has increased the fatigue safety factor by about 50% compared to the original design.

Flutter Analysis of a Transonic Steam Turbine Blade with Frequency and Time-Domain Solvers

The aim of this study was to assess the capabilities of different simulation approaches to predict the flutter stability of a steam turbine rotor. The focus here was on linear and nonlinear frequency domain solvers in combination with the energy method, which is widely used for the prediction of flutter onset. Whereas a GMRES solver was used for the linear problem, the nonlinear methods employed a time-marching procedure. The solvers were applied to the flutter analysis of the first rotor bending mode of the open Durham Steam Turbine test case. This test case is representative of the last stage of modern industrial steam turbines. We compared our results to those published by other researchers in terms of aerodynamic damping and local work per cycle coefficients. Time-domain, harmonic balance, and time-linearised methods were compared to each other in terms of CPU efficiency and accuracy.

Mathematical modelling and optimization for integrated production and energy system in an iron and steel plant

In this paper, we address a simultaneous optimization problem of integrated production and energy system in an iron and steel plant. We develop a novel multi-period mixed integer linear optimization model incorporating many realistic operational features such as industrial boilers, steam turbines, combined heat and power generation units and waste heat recovery and power generation units. A case study of a real iron and steel plant demonstrates that compared to the realistic operational strategy, the total operational cost is reduced by 6.25 % using the proposed model.

A Data-Driven Health Prognostics Approach for Steam Turbines Based on Xgboost and DTW

A steam turbine is one of the critical components in a power generation system whose failure may result in unexpected consequences, even catastrophic losses. Thus, the reliability of steam turbines needs to be guaranteed all the time, which requires that its health state can be monitored and predicted effectively. Due to various failure modes, it is difficult to build physics-of-failure models used for health prognostics for steam turbines. In this paper, a data-driven integrated framework for health prognostics for steam turbines, which is based on extreme gradient boosting (XGBoost) and dynamic time warping (DTW), is proposed. The proposed framework includes two modules: anomaly detection and remaining useful life (RUL) prediction. The anomalies refer to the overall abnormal operation of steam turbines. In the process of anomaly detection, the temporal variables which can represent the operating conditions of the considered steam turbine are selected first. Appropriately selected temporal variables can reduce the input dimension and will improve real-time performance. Then, XGBoost is used to detect anomalies based on learning historical data. In the process of RUL prediction, a similarity-based algorithm with DTW is used to gain the RUL by contrasting the measured temporal variables with those in the historical cases. The similarity-based algorithm can predict the RUL without establishing a degradation path model, which can avoid the difficulties in parameter estimation for the degradation model and model generalization. The proposed framework is validated by real case studies from an industrial steam turbine. The results show that the proposed approach can detect the anomalies successfully and predict the RUL effectively.

A generic algorithm of sustainability (GAS) function for industrial complex steam turbine and utility system optimisation

The GAS-function methodology is introduced in this paper in order to identify the sustainability objective function for optimisation of multiple interconnected complex steam turbines and utility network (ICSTUN) systems. Also, the complex steam turbine was modelled based on induction machine operation which enhances its performance. The boiler models were identified as the sustainability objective function which was further investigated under given operating constraints for optimisation of the ICSTUN system. The validated results show that relative error between field operation and simulation data were less than 1% on average, which is acceptable for engineering applications. The optimisation results indicate that contrary to previous authors' results and by comparing with actual field operational data information, coal flow rates for total site utility system of this investigated ICSTUN can be significantly reduced. It was therefore concluded that a significant reduction in the coal flow rate amounts is practicable in order to significantly reduce operating costs as well as the environmental and social issues associated with utilising fossil-fuels, while still satisfying the demand-side management objectives for the plant.

A Pioneering Method for Reducing Water Droplet Erosion

Based on computational fluid dynamics and the finite volume method, water droplet erosion in the last stage of an industrial steam turbine was researched and the trajectories of the water droplets were traced by using the Lagrange method. Under steady conditions, the influence of variant bowed vane designs was compared based on the distribution and movement trends of the secondary water droplets. In addition, the effects of the bowed blades on water movement at the vane surfaces and their impact areas and intensity on the blades were analyzed. The results showed that: (1) a negatively bowed blade can reduce the speed of the secondary water droplets at the mid span of the blade, which are also effective for water droplets on the surface of the vanes and (2) a negatively bowed blade improves the speed of the secondary droplets on the end walls of vanes, which is advantageous to the secondary droplets through blade passage and reduction of secondary droplet impulse on the blades.

Fracture of an Industrial Steam Turbine Horizontal Joint Nut Upon Tightening

Abstract The nut of a horizontal joint fastener cracked upon tightening during assembly in an industrial steam turbine factory. It was previously used in an over-pressure test, but was otherwise not yet used in service. Nut and bolt were made of the nickel-based superalloy Nimonic 80A, a precipitation-hardenable wrought high-strength alloy with excellent creep and corrosion properties. Such alloys usually get a complex heat treatment after hot-rolling, comprising homogenizing and multiple ageing cycles. The subject nut failed due to an extreme case of mixed grain size which detrimentally affected mechanical properties and was attributed to an insufficient degree of deformation during hot rolling.

Using a Bowed Blade to Improve the Supersonic Flow Performance in the Nozzle of a Supersonic Industrial Steam Turbine

For solar plants, waste-energy recovery, and turbogenerators, there is a considerable amount of waste energy due to low mass flow rate. Owing to the high specific power output and large pressure ratios across the turbine, a supersonic industrial steam turbine (IST) is able to utilize the waste energy associated with low mass flow rate. Supersonic IST has fewer stages than conventional turbines and a compact and modular design, thus avoiding the excessive size and manufacturing cost of conventional IST. Given their flexible operation and ability to function with loads in the range of 50–120% of the design load, supersonic IST offers significant advantages compared to conventional IST. The strong shock-wave loss caused by supersonic flows can be reduced by decreasing the shock intensity and reducing its influence; consequently, a supersonic IST can reach higher efficiency levels. Considering the demonstrated utility of bowed blades in conventional IST, this paper presents a study of the use of bowed blades in a supersonic IST. For this purpose, first, the shock-wave structure in the supersonic flow field was analyzed and compared with experimental results. Then, four different bowed blades were designed and compared with a straight blade to study the influence of bowed blades on the shock-wave structure and wetness. The results indicate that S-shaped bowing can improve the efficiency of supersonic turbines, and the energy-loss coefficient of the stators can be decreased by 2.4% or more under various operating conditions.