A multi-layer feed forward neural network model for accurate prediction of flue gas sulfuric acid dew points in process industries

ZareNezhad, Bahman; Aminian, Ali

doi:10.1016/j.applthermaleng.2009.11.017

Cited by 59 publications

(30 citation statements)

References 8 publications

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“…From Table (3), the three numbers of neuron numbers in the three layers that achieve best performance in terms of MSE mean are (1, 2, 1), (3, 1, 1), (9, 5, 2), (19, 2,4), (17, 1, 1), (16,2,5), and (11, 1, 1) sequentially.…”

Section: Experiments and Resultsamentioning

confidence: 99%

“…Table2shows the pair of number of neurons in the two layers that achieve the minimum MSE mean. Table (2), the pairs of numbers of neurons that achieve best performance in terms of MSE mean are (17, 1) (11, 1), (3,1), (19, 2), (16,2), (9,5), (1,2).…”

Section: Experiments and Resultsamentioning

confidence: 99%

“…In this paper Wisconsin Breast Cancer Data (WBCD) is used, which have been analyzed by various researchers of medical diagnosis of breast cancer in the neural network literature [5], [16], [17], [18]. This data set contains 699 instances.…”

Section: Methodsmentioning

confidence: 99%

“…These efficient networks are widely used to solve complex problems by modeling complex input-output relationships [4], [5].…”

Section: Introductionmentioning

confidence: 99%

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Determining the Efficient Structure of Feed-Forward Neural Network to Classify Breast Cancer Dataset

Ahmed¹,

Noureldien²

2014

ijacsa

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Abstract-Classification is one of the most frequently encountered problems in data mining. A classification problem occurs when an object needs to be assigned in predefined classes based on a number of observed attributes related to that object.Neural networks have emerged as one of the tools that can handle the classification problem. Feed-forward Neural Networks (FNN's) have been widely applied in many different fields as a classification tool.Designing an efficient FNN structure with optimum number of hidden layers and minimum number of layer's neurons, given a specific application or dataset, is an open research problem.In this paper, experimental work is carried out to determine an efficient FNN structure, that is, a structure with the minimum number of hidden layer's neurons for classifying the Wisconsin Breast Cancer Dataset. We achieve this by measuring the classification performance using the Mean Square Error (MSE) and controlling the number of hidden layers, and the number of neurons in each layer.The experimental results show that the number of hidden layers has a significant effect on the classification performance and the best classification performance average is attained when the number of layers is 5, and number of hidden layer's neurons are small, typically 1 or 2.

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Section: Experiments and Resultsamentioning

confidence: 99%

Section: Experiments and Resultsamentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…These efficient networks are widely used to solve complex problems by modeling complex input-output relationships [4], [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determining the Efficient Structure of Feed-Forward Neural Network to Classify Breast Cancer Dataset

Ahmed¹,

Noureldien²

2014

ijacsa

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“…(ZareNezhad and Aminian 2010;Gutiérrez Ortiz and Ollero 2008;Zhao et al 2011a, b). However, it also brings about serious corrosion problems to the inner wall of stacks.…”

Section: Introductionmentioning

confidence: 99%

Prevention of stack corrosion under wet flue gas desulfurization conditions in a coal-fired power plant: performance analysis and comparative study

Zhao

Han

et al. 2016

Environ Syst Res

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Background: This study investigated the prevention of stack corrosion under wet flue gas desulfurization conditions in a coal-fired power plant. The performance analysis and comparative studies of six materials for the prevention of stack corrosion were investigated. Results:The ion chromatography analysis showed the acid condensation contained fluoride, chloride, nitrate, sulphate, and sulphite. The weight loss method showed titanium alloy and foam glass blocks were heat and acid resistant. The scanning electron microscopy indicated the morphologies were pits, cracks, and flakes for sulfuric acid dew corrosion resistant steel, and X-ray diffraction showed the corrosion products mainly consisted of Fe 2 O 3 , FeSO 4 , FeOOH, with some Fe 3 O 4 or FeF 3 . The comparative study indicated that cyclic wet-dry conditions resulted in more aggressive corrosion to the stack than acid condensation. Conclusions:Titanium alloy and foam glass blocks had the best performance and could be applied in the stack to prevent corrosion. The effects of cyclic wet-dry conditions should be taken into account to mitigate stack corrosion in coal-fired power plants.

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A new steel with good low‐temperature sulfuric acid dew point corrosion resistance

Cheng

Sun

et al. 2011

Materials & Corrosion

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In this work, new steels (1#, 2#, and 3#) were developed for low‐temperature sulfuric acid dew point corrosion. The mass loss rate, macro‐ and micro‐morphologies and compositions of corrosion products of new steels in 10, 30, and 50% H2SO4 solutions at its corresponding dew points were investigated by immersion test, scanning electron microscopy (SEM) and energy‐dispersive spectrometry (EDS). The results indicated that mass loss rate of all the tested steels first strongly increased and then decreased as H2SO4 concentration increased, which reached maximum at 30%. Corrosion resistance of 2# steel is the best among all specimens due to its fine and homogeneous morphologies of corrosion products. The electrochemical corrosion properties of new steels in 10 and 30% H2SO4 solutions at its corresponding dew points were studied by potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) techniques. The results demonstrated that corrosion resistance of 2# steel is the best among all the experimental samples due to its lowest corrosion current density and highest charge transfer resistance, which is consistent with the results obtained from immersion tests.

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A multi-layer feed forward neural network model for accurate prediction of flue gas sulfuric acid dew points in process industries

Cited by 59 publications

References 8 publications

Determining the Efficient Structure of Feed-Forward Neural Network to Classify Breast Cancer Dataset

Determining the Efficient Structure of Feed-Forward Neural Network to Classify Breast Cancer Dataset

Prevention of stack corrosion under wet flue gas desulfurization conditions in a coal-fired power plant: performance analysis and comparative study

A new steel with good low‐temperature sulfuric acid dew point corrosion resistance

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