Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing

Ashraf, Waqar Muhammad; Rafique, Yasir; Uddin, Ghulam Moeen; Riaz, Fahid; Asim, Muhammad; Farooq, Muhammad Umar; Hussain, Abid; Salman, Chaudhary Awais

doi:10.1016/j.aej.2021.07.039

Cited by 31 publications

(17 citation statements)

References 51 publications

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“…After training the SVM and ANN on the operational data, there is a need to establish an evaluation criterion for the selection of the better-performing AI model. Four statistical parameters, namely, correlation coefficient ( R ), root-mean-square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE), are selected to evaluate the predictive performance of the trained AI models. The mathematical expression of the statistical parameters is as follows R = ∑ i = 1 N false( y i − y̅ i false) false( ŷ i − y i 0em truê ̅ false) ∑ i = 1 N false( y i − y̅ i false) 2 ∑ i = 1 N false( y i 0em truê − y i 0em truê ̅ false) 2 RMSE = 1 N ∑ i = 1 N false( ŷ i − y ...…”

Section: Resultsmentioning

confidence: 99%

“…In the last two decades, AI-based data modeling tools have presented a remarkable performance in developing engineering solutions and optimization strategies for large-scale industrial systems overcoming the limitations of mathematical modeling tools. − Our research group has also reported performance enhancement solutions developed on the component level, system level, and strategic level of a 660 MW coal power plant using advanced AI modeling tools and statistical techniques. ,, AI-based modeling and simulation algorithms can provide accurate results mined out of the high-dimensional and nonlinear interacting features of engineering systems, which can be reliably implemented in the running operation of energy systems . However, asymmetric and high-dimensional space of the data, development of efficient AI models and their validation, domain knowledge-backed experimental designs, and operating strategies are the challenges to be addressed carefully to exploit the true potential of data and AI algorithms.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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Augmentation of energy efficiency in the power generation systems can aid in decarbonizing the energy sector, which is also recognized by the International Energy Agency (IEA) as a solution to attain net-zero from the energy sector. With this reference, this article presents a framework incorporating artificial intelligence (AI) for improving the isentropic efficiency of a high-pressure (HP) steam turbine installed at a supercritical power plant. The data of the operating parameters taken from a supercritical 660 MW coal-fired power plant is well-distributed in the input and output spaces of the operating parameters. Based on hyperparameter tuning, two advanced AI modeling algorithms, i.e., artificial neural network (ANN) and support vector machine (SVM), are trained and, subsequently, validated. ANN, as turned out to be a better-performing model, is utilized to conduct the Monte Carlo technique-based sensitivity analysis toward the high-pressure (HP) turbine efficiency. Subsequently, the ANN model is deployed for evaluating the impact of individual or combination of operating parameters on the HP turbine efficiency under three real-power generation capacities of the power plant. The parametric study and nonlinear programming-based optimization techniques are applied to optimize the HP turbine efficiency. It is estimated that the HP turbine efficiency can be improved by 1.43, 5.09, and 3.40% as compared to that of the average values of input parameters for half-load, mid-load, and full-load power generation modes, respectively. The annual reduction in CO2 measuring 58.3, 123.5, and 70.8 kilo ton/year (kt/y) corresponds to half-load, mid-load, and full load, respectively, and noticeable mitigation of SO2, CH4, N2O, and Hg emissions is estimated for the three power generation modes of the power plant. The AI-based modeling and optimization analysis is conducted to enhance the operation excellence of the industrial-scale steam turbine that promotes higher-energy efficiency and contributes to the net-zero target from the energy sector.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

Self Cite

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“…In recent decades, several machine learning approaches have been explored and incorporated into machine fault diagnosis, as they offer the possibility to adaptively learn the diagnosis knowledge of machinery from previously collected data [5][6][7][8][9][10][11]. Key features can be extracted from a variety of transducer signals and correlated to different machine health states to perform online diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…Key features can be extracted from a variety of transducer signals and correlated to different machine health states to perform online diagnosis. This strategy can be used with classification algorithms, for anomaly detection and to identify certain types of faults [8,9], or with regression algorithms, to model and predict dynamic behavior [6,10,11,12]. Physics-informed machine learning refers to the integration of real world data and mathematical physics models even in partially understood, uncertain contexts [13].…”

Section: Introductionmentioning

confidence: 99%

Online Unbalance Detection and Diagnosis on Large Flexible Rotors by SVR and ANN trained by Dynamic Multibody Simulations

Peyrano

Juan

Mayer

et al. 2022

JDMD

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Multiple-stage steam turbine generators, like those found in nuclear power plants, pose special challenges with regards to mechanical unbalance diagnosis. Several factors contribute to a complex vibrational response, which can lead to incorrect assessments if traditional condition monitoring strategies are used without considering the mechanical system as a whole. This, in turn, can lead to prolonged machinery downtime. Several machine learning techniques can be used to integrally correlate mechanical unbalance along the shaft with transducer signals from rotor bearings. Unfortunately, this type of machinery has scarce data regarding faulty behavior. However, a variety of fault conditions can be simulated in order to generate these data using computational models to simulate the dynamic response of individual machines. In the present work, a multibody model of a 640 MW steam turbine flexible rotor is employed to simulate mechanical unbalance in several positions along the shaft. Synchronous components of the resulting vibration signals at each bearing are obtained and utilized as training data for two regression models designed for mechanical unbalance diagnosis. The first approach uses an artificial neural network and the second one utilizes a support vector regression algorithm. In order to test their performance, the stiffness of each bearing in the multibody simulation was altered between 50% and 150% of the training model values, random noise was added to the signal and several dynamic unbalance conditions were simulated. Results show that both approaches can reliably diagnose dynamic rotor unbalance even when there is a typical degree of uncertainty in bearing stiffness values.

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“…For example, tourists are increasingly inclined to plan their attractions and routes for flexible self-help travel, tourism application services are gradually transferred to mobile, and smart tourism is deepening and expanding. To achieve efficient and accurate mobile travel recommendation service and improve user experience, in addition to users' personal information and interest preferences, mobile travel recommendation also needs to pay attention to the condition of attractions, geographic location, situational factors, and contextual information in real time, to facilitate tourists to make route changes at any time [ 3 ]. Touring is the core part of the travel recommendation service.…”

Section: Introductionmentioning

confidence: 99%

Design of Travel Route Identification and Scheduling System Based on Artificial Intelligence-Aided Image Segmentation

Hou

2022

Computational Intelligence and Neuroscience

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This study designs a travel recognition and scheduling system using artificial intelligence and image segmentation techniques. To address the problem of low division quality of current point division algorithms, this study proposes a streaming graph division model based on a sliding window (GraphWin), which dynamically adjusts the amount of information (vertex degree information and adjacency information) referenced at each division according to the current division quality and division time by introducing a sliding window mechanism, to achieve the highest possible division while allowing loss of certain division efficiency. The goal is to improve the division quality as much as possible while allowing a certain loss of division efficiency. To meet the user’s need to travel through multiple destinations with the shortest route, this thesis proposes a deep reinforcement learning actor-critic (AC)-based multiobjective point path planning algorithm. The algorithm builds a strategy network and an evaluation network based on actor-critic’s multiobjective point path planning, updates the strategy network and evaluation network parameters using AC optimization training, reduces the reliance of the algorithm model on a large amount of high-quality label data, and speeds up the convergence speed of the deep reinforcement learning algorithm by pretraining, finally completing the multiobjective point access sequential path planning task. Finally, the personalized travel recommendation system is designed and implemented, and the system performance analysis is conducted to clarify the system requirements in terms of functional and nonfunctional aspects: the system architecture, system functional modules, and database tables are designed to conduct use case testing of the main functional modules of the system, and the usability of the attraction recommendation algorithm is verified through the concrete implementation of the functional modules such as attraction recommendation in the system.

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Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing

Cited by 31 publications

References 51 publications

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

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

Online Unbalance Detection and Diagnosis on Large Flexible Rotors by SVR and ANN trained by Dynamic Multibody Simulations

Design of Travel Route Identification and Scheduling System Based on Artificial Intelligence-Aided Image Segmentation

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