Performance of Fuzzy Inference Systems to Predict the Surface Temperature of Broiler Chickens

Bahuti, Marcelo; Abreu, Lucas Henrique Pedrozo; Yanagi, Tadayuki; Lima, Renato Ribeiro de; Campos, Alessandro T.

doi:10.1590/1809-4430-eng.agric.v38n6p813-823/2018

Cited by 9 publications

(6 citation statements)

References 29 publications

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“…The output of the buck converter is given as V out = D.V n , where D = duty cycle assuming a 16% duty cycle with V in = 76 V and V out = 12 V. The output current I out = V out /R. The inductance of the buck converter is presented in Equation (26).…”

Section: Buck Converter Designmentioning

confidence: 99%

“…The primary challenge posed by the proposed technique is the movement of the receiver coil due to the changing load. This can be resolved by using a high-level control to enhance the efficiency of the power transfer [24][25][26]. Moreover, with the intention of improving its action, the shape of the transmitter coil can be designed to be circular or rectangular [27,28].…”

Section: Introductionmentioning

confidence: 99%

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Novel Electrical Modeling, Design and Comparative Control Techniques for Wireless Electric Vehicle Battery Charging

El-Shahat

Ayisire²

2021

Electronics

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Dynamic wireless power systems are an effective way to supply electric vehicles (EVs) with the required power while moving and to overcome the problems of low mileage and extensive charging times. This paper targets modeling and control for future dynamic wireless charging using magnetic resonance coupling because of the latter’s efficiency. We present a 3D model of transmitter and receiver coils for EV charging with magnetic resonance wireless power developed using ANSYS Maxwell. This model was incorporated into the physical design of the magnetic resonance coupling using ANSYS Simplorer in order to optimize the power. The estimated efficiency was around 92.1%. The transient analysis of the proposed circuit was investigated. A closed-loop three-level cascaded PI controller- was utilized for wireless charging of an EV battery. The controller was designed to eliminate the voltage variation resulting from the variation in the space existing between coils. A single-level PI controller was used to benchmark the proposed system’s performance. Furthermore, solar-powered wireless power transfer with a maximum power point tracker was used to simulate the wireless charging of an electric vehicle. The simulation results indicated that the EV battery could be charged with a regulated power of 12 V and 5 A through wireless power transfer. Fuzzy logic and neuro-fuzzy controllers were employed for more robustness in the performance of the output. The neuro-fuzzy controller showed the best performance in comparison with the other designs. All the proposed systems were checked and validated using the OPAL Real-Time simulator. The stability analysis of the DC–DC converter inside the closed-loop system was investigated.

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Section: Buck Converter Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Electrical Modeling, Design and Comparative Control Techniques for Wireless Electric Vehicle Battery Charging

El-Shahat

Ayisire²

2021

Electronics

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“…Studies showed the Ts changes with air temperature (Ta) and relative humidity (RH). An increase in Ta and RH increases the Ts of broilers of age 1-35 d [21][22][23]. The Ts variation with all these environmental factors was assessed for the birds of age up to 42 d. Hence, although Ts can be used as an indicator of stress, the evaluations are not available for heavier and bigger birds of current market size.…”

Section: Introductionmentioning

confidence: 99%

“…Since Ts is useful in assessing the thermal stress of broiler chickens [18,21], several approaches were taken to build models and methods that can be easily used to practically predict the Ts of broilers [20,23,28]. However, limitations in Ts measurement, changes of Ts with the environment, and variations of Ts at various ages underestimated the Ts of broiler chicken.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Air Velocity Treatments under Summer Condition: Part I—Heavy Broiler’s Surface Temperature Response

Akter

Cheng

West

et al. 2022

Animals

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Heavy broilers exposed to hot summer conditions experience fluctuations in surface temperatures due to heat stress, which leads to decreased performance. Maintaining a bird’s homeostasis depends on several environmental factors (temperature, relative humidity, and air velocity). It is important to understand the responses of birds to environmental factors and the amount of heat loss to the surrounding environment to create thermal comfort for the heavy broilers for improved performances and welfare. This study investigates the variation in surface temperatures of heavy broilers under high and low air velocity treatments. Daytime, age and bird location’s effect on the surface temperature variation was also examined. The experiment was carried out in the poultry engineering laboratory of North Carolina State University during summers of 2017, 2018, and 2019 as a part of a comprehensive study on the effectiveness of wind chill application to mitigate heat stress on heavy broilers. This live broiler heat stress experiment was conducted under two dynamic air velocity treatments (high and low) with three chambers per treatment and 44 birds per chamber. Surface temperatures of the birds were recorded periodically through the experimental treatment cycles (flocks, 35–61 days) with infrared thermography in the morning, noon, evening, and nighttime. The overall mean surface temperature of the broilers under two treatments was found to be 35.89 ± 2.37 °C. The variation in surface temperature happened due to air temperature, thermal index, air velocity, bird’s age, daytime, and position of birds inside the experimental chambers. The surface temperatures were found lower under high air velocity treatment and higher under low air velocity treatment. During the afternoon time, the broilers’ surface temperatures were higher than other times of the day. It was also found that the birds’ surface temperature increased with age and temperature humidity indices. Based upon the experimental data of five flocks, a simple linear regression model was developed to predict surface temperature from the birds’ age, thermal indices, and air velocity. It will help assess heavy broilers’ thermal comfort under heat stress, which is essential to provide a comfortable environment for them.

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“…The concepts of fuzzy logic are applied in principles that traditional logic does not allow explanations. Together, the fuzzy theory makes it possible to perform reasoning close to that of human beings in environments with uncertainties (Senna et al, 2015, Raj & Mohan, 2016, Sousa et al, 2006 The use of fuzzy theory is used in several areas of knowledge, such as in the poultry area, in modeling the impacts generated on the surface of the broiler by different temperatures (Bahuti et al, 2018), in the sphere of medicine in analyzing the need to administer vasopressors in ICU patients (Salgado et al, 2016), in the agricultural sector in obtaining reference evapotranspiration, replacing the conventional methodology (Kisi, 2013), among other compositions involving mathematical and computational methods that this logic is capable of to develop.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Modeling of the Effects of Different Irrigation Depths on the Radish Crop. Part I: Productivity Analysis

et al. 2021

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The productivity of a crop is related to the water demand inserted in its development. The measurement of water and its optimization directly influences the final costs of crop production for agricultural producers. In this sense, the objective of this study is evaluating the fuzzy modeling in estimating the productivity of the radish crop (fresh phytomass of the tuberous root) affected by different irrigation depths (25%, 50%, 75%, 100%, and 125%), based on evapotranspiration of the crop (ETc). To measure the results, two fuzzy systems (with triangular and Gaussian membership functions respectively) and a polynomial regression model were developed to perform model validation comparisons. The fuzzy modeling showed a better fit of the data compared to the polynomial regression model, with reduced errors (RMSE with values 6.3 and 6.9 in the fuzzy models versus 8.8 in the regression model) and higher correlation coefficient (0.54 and 0.5 fuzzy versus 0.1 regression). The triangular fuzzy model estimated the best crop yield (31.9 g of fresh phytomass) when using a 100% ETc depth. Also, the curve generated by the fuzzy model accurately represents all the productivity averages in each depth, in addition to this model presenting the smallest errors (compared to the triangular model and the regression model) and the highest R². However, the Gaussian fuzzy model proved to be more efficient in representing the agronomic reality, as it does not have peaks and valleys, and it is a smooth model in both growth and degrowth.

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Performance of Fuzzy Inference Systems to Predict the Surface Temperature of Broiler Chickens

Cited by 9 publications

References 29 publications

Novel Electrical Modeling, Design and Comparative Control Techniques for Wireless Electric Vehicle Battery Charging

Novel Electrical Modeling, Design and Comparative Control Techniques for Wireless Electric Vehicle Battery Charging

Impacts of Air Velocity Treatments under Summer Condition: Part I—Heavy Broiler’s Surface Temperature Response

Fuzzy Modeling of the Effects of Different Irrigation Depths on the Radish Crop. Part I: Productivity Analysis

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