Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design

Salim, Hosham; Faisal, Khalid; Jawad, Raheel

doi:10.1155/2018/8042498

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…In [17], the study presents the performance of a steam turbine in thermal power plants using an artificial neural network. is method used NARMA to generate data and train network for the controlling model.…”

Section: Related Workmentioning

confidence: 99%

Some Criteria of the Knowledge Representation Method for an Intelligent Problem Solver in STEM Education

Nguyen

Nhon

Tran

et al. 2020

Applied Computational Intelligence and Soft Computing

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Nowadays, building intelligent systems for science, technology, engineering, and math (STEM) education is necessary to support the studying of learners. Intelligent problem solver (IPS) is a system that can be able to solve or tutor how to solve the problems automatically. Learners only declare hypothesis and goal of problems based on a sufficient specification language. They can request the program to solve it automatically or to give instructions that help them to solve it themselves. Knowledge representation plays a vital role in these kinds of intelligent systems. There are various methods for knowledge representation; however, they do not meet the requirements of an IPS in STEM education. In this paper, we propose the criteria of a knowledge model for an IPS in education. These criteria orient to develop a method for knowledge representation to meet actual requirements in practice, especially pedagogical requirements. For proving the effectiveness of these criteria, a knowledge model is also constructed. This model can satisfy these criteria and be applied to build IPS for courses, such as mathematics and physics.

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Section: Related Workmentioning

confidence: 99%

Some Criteria of the Knowledge Representation Method for an Intelligent Problem Solver in STEM Education

Nguyen

Nhon

Tran

et al. 2020

Applied Computational Intelligence and Soft Computing

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“…This technique adjusts the temperature in the steam turbine to maintain it below the threshold value for safety, and at the same time, it maintains the start-up rate [2]. Salim et al [3] used design and implantation of electric circuits for enhanced performance of steam power plants and artificial neural networks as techniques. The novelty in this paper was in applying the NARMA controller for the purpose of enhancing turbine performance.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

Abdullah¹,

Hamoodi²,

Mohammed³

2021

JESA

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Artificial intelligence has proven its effectiveness in many industrial fields to enhance the existing functionality. Artificial intelligence and machine learning algorithms integrated with turbines can be useful in controlling important variables such as pressure, temperature, speed, and humidity. In this research, the Simulink library from MATLAB is used to build an artificial neural network. The NARMA L2 neural controller is used to generate data and for training networks. To obtain the result and compare it with the real-time power plant, data is collected. The input variables provided to the neural network have a large effect on the hidden layer and the output of the neural network. The circuit board used in this research has a DC bridge, a transformer and voltage regulators. The result comparison shows that the integration of artificial neural networks and electric circuits shows enhanced performance with high accuracy of prediction. It was observed that the ANN integration system and electric circuit design have a result deviation of less than 1%. This shows that the integration of ANN improves the performance of turbines.

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“…Their work highlights the method's ability to perform in highly fluctuating environments with multiple inputs affecting the output, as is common with thermal combustion systems. Other uses of machine learning in power plants include system enhancement and optimisation [10], health monitoring [11] and complex thermal management [5].…”

Section: Introductionmentioning

confidence: 99%

Prediction of boiler gas side effective heat transfer coefficients using mixture density networks and historic plant data

Raidoo

Laubscher

2021

MATEC Web Conf.

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Machine learning has received increased recognition for applications in engineering such as the thermal engineering discipline due to its abilities to circumvent thermodynamic simulation approaches and capture complex inter-dependencies. There have been recent headways to couple deep learning models to process simulations, given the deeper insight they can provide. The present study entails the development of a mixture density network (MDN) capable of predicting effective heat transfer coefficients for the various heat exchanger components of a utility scale boiler. Large boilers are susceptible to dead zones and other anomalous phenomena that influence performance and manifest as multimodalities in the measured data, which system-level 1D process models struggle to capture. The overall water-side heat load calculation, as well as mass and energy balances around the components were done to determine the heat transfer coefficients at each stage of the boiler using historic sensor data. The measured data was then used to train a deep learning model capable of outputting predicted heat transfer coefficients and local model uncertainty. The predictive model can be coupled to a water circuit process model which can be used to study aspects such as metal temperatures and operating philosophies at the different operating loads of the plant.

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Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design

Cited by 8 publications

References 10 publications

Some Criteria of the Knowledge Representation Method for an Intelligent Problem Solver in STEM Education

Some Criteria of the Knowledge Representation Method for an Intelligent Problem Solver in STEM Education

Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

Prediction of boiler gas side effective heat transfer coefficients using mixture density networks and historic plant data

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