Modeling and simulation controlling system of HVAC using fuzzy and predictive (radial basis function, RBF) controllers

Ardabili, Sina Faizollahzadeh; Mahmoudi, Asghar; Gundoshmian, Tarahom Mesri

doi:10.1016/j.jobe.2016.04.010

Cited by 60 publications

(37 citation statements)

References 13 publications

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“…Prediction accuracy was measured by the MAPE. The differences between the predicted and target values of CN measured by the RMSE and linearity of predicted and target values was evaluated by r .…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, intelligent methods can do complex tasks without the need to know the mathematical model of the system. In recent years, intelligent and soft computing methods were used in all fields of science . Intelligent methods include the wide spectrum of soft computing methods from fuzzy logic to artificial neural networks (ANNs) that are used for prediction, classification, robust, and non‐linear control techniques .…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy logic method for the prediction of cetane number using carbon number, double bounds, iodic, and saponification values of biodiesel fuels

Ardabili

Najafi

Shamshirband³

2019

Env Prog and Sustain Energy

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The aim of present study is to develop an accessible accurate estimation of CN based on fatty acid methyl esters and to provide a proper solution for presenting a user‐friendly method. In fact, this study calculates the density, viscosity, and HHV based on FAMEs and predicts the CN by employing fuzzy method within a set. This is an interesting approach that has not been used in similar articles. Gaussian membership functions with 81 roles were employed to develop the fuzzy model. The model was developed based on Carbon number, Double bond, Saponification number, and Iodine value to predict the CN. Performance factors of r, RMSE, MAE, and R2 were calculated as 0.9912, 1.0723, 0.63427, and 0.9828, respectively to predict the CN. The results of FAMEs effect on properties of biodiesel showed, increasing Carbon number of FAMEs increases the CN, viscosity, and HHV, but increasing the number of Double bonds decreases CN, viscosity, and HHV. While the effect of increasing Carbon number of FAMEs on density was vice versa. Based on results, C16:00, C18:00, C18:1, and C18:02 FAMEs are approximately in components of all conventional oils; therefore, they can be effective on physical properties of biodiesels. © 2019 American Institute of Chemical Engineers Environ Prog, 38: 584–599, 2019

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy logic method for the prediction of cetane number using carbon number, double bounds, iodic, and saponification values of biodiesel fuels

Ardabili

Najafi

Shamshirband³

2019

Env Prog and Sustain Energy

Self Cite

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“…where A is the target value, P is the predicted value, and N is the number of data points. MAE and RMSE calculate the difference of the target and predicted values and r is a measure of the linearity between target and predicted values [20,21].…”

Section: Evaluation Of Developed Modelsmentioning

confidence: 99%

“…There are several modeling methods and the simplest method of modeling is a mathematical approach (or sometimes is remembered as a classic model) [19]. Modeling methods contain a wide range of methods from mathematical to soft computing methods, but modeling an ill-defined system, such as a biodiesel production process, becomes even more pronounced using mathematical and statistical techniques due to the complexity and the biological nature of biodiesel [20,21].…”

Section: Introductionmentioning

confidence: 99%

Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters

et al. 2018

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The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. %, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. %, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6% for ethyl ester and 3.1% for methyl ester, compared with those for the experimental data.

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“…ANNs is a efficient techniques as compared to analytical methods (20). Neural network are able to predict and approximate any nonlinear function (6). ANNs do not require any awareness of fitting function.…”

Section: Introductionmentioning

confidence: 99%

Application of Soft Computing Methods and Spectral Reflectance Data for Wheat Growth Monitoring

a¹

2019

Iraqi J. Agric. Sci.

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Technology of precision agriculture has caused to the remote sensors development that compute Normalized Difference Vegetation Index (NDVI) parameters. Vegetation indices obtained from remote sensing data can help to summarize climate conditions. Artiﬁcial Neural Networks (ANNs), as a soft computing methods, are one of the most efﬁcient methods for computing as compared to the statistical and analytical techniques for spectral data. This study was employed experimental radial basis function (RBF) of ANN models and adaptive neural-fuzzy inference system (ANFIS) to design the network in order to predict the soil plant analysis development (SPAD), protein content and grain yield of wheat plant based on spectral reflectance value and to compare two models. Results indicated that the obtained results of RBF method with high average correlation coefficient (0.984, 0.981 and 0.9807 in 2015 for SPAD, yield and protein, respectively and 0.979, 0.9805 and 0.984 in 2016) and low RMSE (0.271, 103.315 and 0.111 in 2015 for SPAD, yield and protein, respectively and 0.407, 105.482 and 0.121 in 2016) has the high accuracy and high performance compared to ANFIS models.

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Modeling and simulation controlling system of HVAC using fuzzy and predictive (radial basis function, RBF) controllers

Cited by 60 publications

References 13 publications

Fuzzy logic method for the prediction of cetane number using carbon number, double bounds, iodic, and saponification values of biodiesel fuels

Fuzzy logic method for the prediction of cetane number using carbon number, double bounds, iodic, and saponification values of biodiesel fuels

Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters

Application of Soft Computing Methods and Spectral Reflectance Data for Wheat Growth Monitoring

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