Modeling and comparison of fuzzy and on/off controller in a mushroom growing hall

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

doi:10.1016/j.measurement.2016.04.050

Cited by 42 publications

(13 citation statements)

References 17 publications

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“…On the other hand, this would have a direct impact on the energy consumption of the control system and would lead to more sustainable control in the control system. This conclusion has also been confirmed in a study by Ardabili et al (2016) [69] to design a fuzzy control system in a mushroom growing hall. Also, the operator's dominance and focus also increase with the use of fuzzy systems.…”

Section: Advanced Anfis/neuro-fuzzy: Hybridssupporting

confidence: 70%

Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

et al. 2019

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Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines.

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Section: Advanced Anfis/neuro-fuzzy: Hybridssupporting

confidence: 70%

Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

et al. 2019

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“…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 . In recent years there were developed various studies based on intelligent methods to predict the CN value .…”

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

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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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“…There are several modeling methods, the simplest of which is the mathematical approach, which is considered the classical system (Faizollahzadeh Ardabili, 2014;. Intelligent systems enable the user to perform very complex tasks with high accuracy, but without the need to devise the mathematical equations in the system (Faizollahzadeh Ardabili, Mahmoudi, & Mesri Gundoshmian, 2016;Najafi, Faizollahzadeh Ardabili, Mosavi, Shamshirband, & Rabczuk, 2018). These methods have been successfully employed in waste management (Nabavi-Pelesaraei, Bayat, Hosseinzadeh-Bandbafha, Afrasyabi, & Chau, 2017), groundwater modeling (Gholami, Chau, Fadaee, Torkaman, & Ghaffari, 2015), the assessment of river water quality (Wang, Xu, Chau, & Lei, 2014) and river stage forecasting (Chau, 2007).…”

Section: Introductionmentioning

confidence: 99%

Application of ANNs, ANFIS and RSM to estimating and optimizing the parameters that affect the yield and cost of biodiesel production

Najafi

Ardabili

Shamshirband

et al. 2018

Engineering Applications of Computational Fluid Mechanics

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Biodiesel can easily be used as an alternative fuel in diesel engines. It is environmentally friendly and can be produced from low-cost feedstocks such as waste cooking oil (WCO). WCO contains a significant amount of free fatty acid, which is extracted by a two-step process of converting the free fatty acid by acid catalysis (H 2 S0 4) and converting the triglycerides using an NaOH catalyst. Currently, the major challenge for the industrial production of biodiesel is optimizing the yield while meeting American Society for Testing and Materials (ASTM) standards. In this study, experiments were performed to optimize the reaction conditions. The studied experimental parameters were the alcohol types (methanol, ethanol), the alcohol-to-oil molar ratio (AOMR; 3:1, 6:1, 9:1), the amount of catalyst (0.5, 1.0, 1.5 wt% of the oil), the temperature of the reaction (50, 60, 70, 80°C), the mixing intensity (300, 600, 900 rpm), and the reaction time (30, 60, 90 min). The biodiesel production yield (BPY) was optimized based on the experimental data. The optimum value of the BPY based on methanol is 95.92%, which is obtained at 73.80°C, with a reaction time of 74.02 min, an AOMR of 6.58:1, a catalyst concentration of 1.13 and a mixing intensity of 824.45 rpm. In the case of ethanol, the optimum BPY is 95.53%. which is obtained at 64.96°C, with a reaction time of 88.02 min, an AOMR of 7.005:1, a catalyst concentration of 1.25 and a mixing intensity of 592.18 rpm. These results of biodiesel production were confirmed by the experimental data.

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Modeling and comparison of fuzzy and on/off controller in a mushroom growing hall

Cited by 42 publications

References 17 publications

Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

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

Application of ANNs, ANFIS and RSM to estimating and optimizing the parameters that affect the yield and cost of biodiesel production

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