Optimization of operating conditions for steam turbine using an artificial neural network inverse

Hamzaoui, Youness El; Rodríguez, José Ignacio Herrera; Hernández, J.A.; Salazar, Víctor F. Pacheco

doi:10.1016/j.applthermaleng.2014.09.065

Cited by 29 publications

(6 citation statements)

References 26 publications

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“…From the reading of the previous documents, a total of 11 articles were eliminated because they were outside the scope. For example, for dealing with steam turbine operating conditions [59] or improving the energy use of a farm [60]. Thus, the articles that were finally used for the construction of the framework are shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

Sendra

Heras

Ávila-Gutiérrez

et al. 2020

Sensors

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This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.

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Section: Resultsmentioning

confidence: 99%

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

Sendra

Heras

Ávila-Gutiérrez

et al. 2020

Sensors

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show abstract

“…The ANN inputs information for this sort of welding process are typically the temperature coefficient, the weld volt, the welder's electricity, and the blistering velocity. Again, the multilayer perceptron was the most often used ANN in this situation [10,[13][14][15][16][17][18][19][20][21]. Their usage was shown to be extremely effective in predicting weld flaws, geometrical parameters such as bead width, head level, and penetrating.…”

Section: Figure 1 Artificial Neural Network's Function and Industrial...mentioning

confidence: 99%

Application of Artificial Neural Network into Manufacturing Processes

Mnkandla

Mulongo

2022

ijcs

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The neural network model is an advanced and effective tool aims at simulating the manufacturing operations. An important number of researchers have utilized artificial neural network (ANN) to optimising multiple response metrics in manufacturing applications. In the majority of situations, the use of ANN enables the prediction of the mechanical and physical properties of manufacturing goods based on provided technical data. To this end, the deployment of ANN in manufacturing sector is tremendously significant in terms of cost and material resource savings. Thus, Artificial neural network as a key component regarding the optimization of the manufacturing processes.

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“…Inverse artificial neural network algorithms are tools with the capacity to optimise processes based on experimental data. They are used to improve system performance or estimate predictor variables (Hamzaoui et al, 2015). According to Solís-Pérez et al, (2019), optimisation is the estimation of a value(s) to obtain the minimum or maximum potential in the performance of a process.…”

Section: Introductionmentioning

confidence: 99%

An inverse artificial neural network algorithm for retrieval of sunshine hours from ground-based global solar irradiation measurements

Nzala¹,

Muyimbwa²,

Ssebiyonga³

et al. 2022

EAJSTI

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Availability of meteorological parameter values is important in applications that require solar irradiation. Meteorological parameters such as sunshine hours are best provided by measuring equipment mainly stationed at weather stations. The cost of purchasing the measuring equipment, and setting up and maintaining weather stations is enormous and cannot be easily afforded by many developing countries like Uganda. Furthermore, in Uganda, we have some weather stations which have been measuring global horizontal solar irradiation for quite some time but lacked sunshine duration sensors, and at some others, the sunshine measuring equipment malfunctioned. In this work we present an inverse artificial neural network algorithm that predicts sunshine hours based on horizontal global solar irradiation, the algorithm caters to cases where global horizontal solar irradiation is present but sunshine hour measuring equipment is missing or malfunctioned. A radial basis function neural network (RBF-NN) was trained for forward computation of global horizontal solar irradiation from sunshine hour values. The inverse modelling algorithm employs multidimensional unconstrained non-linear optimization to retrieve sunshine hours. The correlation coefficient (r) between the measured sunshine hour values and those predicted by the inverse artificial neural network algorithm was found to be 0.924. The average value of the ratios of the inverse artificial neural network algorithm computed values to the sunshine duration sensor measurements was 1.043. The algorithm predicted sunshine hour values with a mean bias and relative root mean square error of 0.043 and 0.394, respectively. The algorithm developed provides an affordable, fast and reliable method for determining sunshine hour values based on global horizontal solar irradiation. However, sunshine hour values prediction for low values of global horizontal solar irradiation was less precise, and this could be attributed to high levels of cloudiness. It is recommended that an inverse algorithm that retrieves sunshine hours under cloudy conditions be constructed

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Optimization of operating conditions for steam turbine using an artificial neural network inverse

Cited by 29 publications

References 26 publications

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

Application of Artificial Neural Network into Manufacturing Processes

An inverse artificial neural network algorithm for retrieval of sunshine hours from ground-based global solar irradiation measurements

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