Estimation of air dew point temperature using computational intelligence schemes

Baghban, Alireza Akbarzadeh; Bahadori, Mohammad; Rozyn, J; Lee, Moonyong; Abbas, Ali; Bahadori, Alireza; Rahimali, Arash

doi:10.1016/j.applthermaleng.2015.10.056

Cited by 123 publications

(34 citation statements)

References 25 publications

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“…Recently, intelligence schemes have widely applied in different fields in order to modeling nonlinear relationships between concerned variables (Shafiei et al, 2014;Ahmadi and Baghban, 2015;Baghban et al, 2015a;Baghban et al, 2015b;Amedi et al, 2016;Baghban et al, 2016;Bahadori et al, 2016). Artificial neural network, fuzzy logic system, adaptive network-based fuzzy inference system, and support vector machine are for popular and widely used embranchments of intelligence schemes.…”

Section: Introductionmentioning

confidence: 99%

Modeling of true vapor pressure of petroleum products using ANFIS algorithm

Baghban

Bahadori

Ahmad

et al. 2016

Petroleum Science and Technology

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The aim of this contribution was to develop a simple tool based on fuzzy logic concepts to predict true vapor pressure of volatile petroleum products. In this regard, the adaptive neuro fuzzy inference system was evolved to estimate the true vapor pressure of volatile petroleum products as function of temperature and Reid vapor pressure. In addition, to determine optimal membership function parameters, the particle swarm optimization as an amazing evolutionary algorithm was applied. This predictive tool is suggested as a precise technique to measure the true vapor pressures of typical liquefied petroleum gases, natural gasoline, and motor fuel components at broad ranges of temperatures. This technique was trained and tested by 156 set of data points collected from the reference. The temperature range is 253-373 K and the range of Reid vapor pressure is 35-250 KPa. Results obtained from the present tool found to be in acceptable agreement with the actual reported data in the literature. The values of root mean square error and regression coefficient obtained 5.34 and 0.9975, respectively.

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Section: Introductionmentioning

confidence: 99%

Modeling of true vapor pressure of petroleum products using ANFIS algorithm

Baghban

Bahadori

Ahmad

et al. 2016

Petroleum Science and Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, intelligence schemes have widely applied in different fields to model nonlinear relationships between concerned variables (Ahmadi and Baghban, 2015;Amedi et al, 2016;Baghban et al, 2015a;Baghban et al, 2015;Baghban et al, 2015c;Shafiei et al, 2014;Bahadori et al, 2016). Artificial neural network, fuzzy logic system, adaptive network-based fuzzy inference system (ANFIS), and support vector machine are for popular and widely used embranchments of intelligence schemes.…”

Section: Introductionmentioning

confidence: 99%

Estimation of natural gases water content using adaptive neuro-fuzzy inference system

Baghban

Kashiwao

Bahadori

et al. 2016

Petroleum Science and Technology

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To appropriate design and satisfactory performance of utilities in the gas processing and transmission plants, a crucial factor that should be taken in consideration is the natural gas water content. The present research aimed to develop a precise strategy for estimating sour gas/sweet gas water content ratio. This developed predictive tool is based on adaptive neuro-fuzzy inference system (ANFIS). In this regard, a comprehensive data bank that contains 1,126 data points was collected. This model predicts ratio of sour gas to sweet gas as function of pressure, temperature, and equilibrium H 2 S mole fraction. The ranges of pressure and temperature were 200-70000 KPa and 10-150°C, respectively. In addition, the equilibrium H2S mole fraction ranges between 0.076 and 0.492. Results obtained from the ANFIS model confirmed acceptable and reasonable predictive capability of this model. This tool is simple to use and can be help petroleum engineers to predict water content of natural gas at broad ranges of conditions.

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“…Recently, similar studies conducted using extreme learning machine (ELM) [24], adaptive neurofuzzy inference system (ANFIS) [25], support vector machine (SVM) [26], gene expression programming (GEP), and multivariate adaptive regression splines (MARS) [27] estimated/modeled DPT with sufficient levels of accuracy. Genetic algorithm (GA) based least square SVM and ANFIS models developed by Baghban et al (2016) [28] predict the moist air DPT over an extensive range of relative humidity and temperature. Here, GA was employed to optimize the corresponding parameters of ANFIS and LS-SVM models.…”

Section: Introductionmentioning

confidence: 99%

Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

Naganna

Deka

Ghorbani

et al. 2019

Water

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Dew point temperature (DPT) is known to fluctuate in space and time regardless of the climatic zone considered. The accurate estimation of the DPT is highly significant for various applications of hydro and agro–climatological researches. The current research investigated the hybridization of a multilayer perceptron (MLP) neural network with nature-inspired optimization algorithms (i.e., gravitational search (GSA) and firefly (FFA)) to model the DPT of two climatically contrasted (humid and semi-arid) regions in India. Daily time scale measured weather information, such as wet bulb temperature (WBT), vapor pressure (VP), relative humidity (RH), and dew point temperature, was used to build the proposed predictive models. The efficiencies of the proposed hybrid MLP networks (MLP–FFA and MLP–GSA) were authenticated against standard MLP tuned by a Levenberg–Marquardt back-propagation algorithm, extreme learning machine (ELM), and support vector machine (SVM) models. Statistical evaluation metrics such as Nash Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE) were used to validate the model efficiency. The proposed hybrid MLP models exhibited excellent estimation accuracy. The hybridization of MLP with nature-inspired optimization algorithms boosted the estimation accuracy that is clearly owing to the tuning robustness. In general, the applied methodology showed very convincing results for both inspected climate zones.

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Estimation of air dew point temperature using computational intelligence schemes

Cited by 123 publications

References 25 publications

Modeling of true vapor pressure of petroleum products using ANFIS algorithm

Modeling of true vapor pressure of petroleum products using ANFIS algorithm

Estimation of natural gases water content using adaptive neuro-fuzzy inference system

Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

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