Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Shamshirband, Shahab; Hadipoor, Masoud; Baghban, Alireza Akbarzadeh; Mosavi, Amir; Bukor, József; Várkonyi-Kóczy, Annamária R.

doi:10.20944/preprints201905.0124.v3

Cited by 21 publications

(9 citation statements)

References 105 publications

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“…According to this fact, the sensitivity analysis is employed to investigate effect of concentration of components in gaseous mixture, ionic strength of solution, temperature and pressure on solubility of hydrocarbons in aqueous electrolyte systems. To this end, relevancy factor should be determined as following for each input parameter [46][47][48][49][50][51][52] (12) In which and ̅ denote the 'i' th output and output average.…”

Section: Resultsmentioning

confidence: 99%

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Nabipour¹,

Mosavi²,

Baghban³

et al. 2020

Preprint

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Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases including methane, ethane, propane and butane in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points concluded to R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility leaded to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.

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Section: Resultsmentioning

confidence: 99%

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Nabipour¹,

Mosavi²,

Baghban³

et al. 2020

Preprint

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“…There has been no standard method for splitting training and testing data. For example, Nabipour et al [54] utilized 70% of their data for training, whereas Mohammadzadeh et al [55], Shamshirband et al [93], and Samadarianfard et al [94] applied 70%, 67%, and 80% of total data to develop their models. In this study, the dataset includes 118 pavement segments where approximately 70% of the data (i.e., 83 segments) are utilized for training, and the remaining 35 segments are used for testing.…”

Section: Resultsmentioning

confidence: 99%

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

Karballaeezadeh¹,

Mohammadzadeh²,

Moazami³

et al. 2020

Preprint

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The construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavement condition index (PCI), structural condition index (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.

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“…The third layer has constant nodes denoted by N. The corresponding node functions are applied to normalize the firing by dividing the i th node's firing strength value by the all firing strength values' summation [28].…”

Section: Description Of Modelsmentioning

confidence: 99%

On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models

Yang

Zheng

et al. 2021

BioMed Research International

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This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters. The values that are calculated for the mean relative error (MRE %) and mean squared error (MSE) were 29.318 and 0.0039 for ANFIS, and 2.951 and 0.0001 for LSSVM which shows that the latter model has a better ability to predict the target data. Finally, in order to have additional certainty, two analyses of outlier identification and sensitivity were performed on the input parameter data that proved the proposed model in this paper has higher reliability in assessing output values compared with the previous model.

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Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Cited by 21 publications

References 105 publications

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models

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