Estimation of coal moisture content in convective drying process using ANFIS

Pusat, Şaban; Akkoyunlu, Mustafa Tahır; Pekel, Engin; Akkoyunlu, Mehmet Cabir; Özkan, Coşkun; Kara, Selin Soner

doi:10.1016/j.fuproc.2015.12.010

Cited by 52 publications

(18 citation statements)

References 33 publications

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“…The predicted results are shown in Figure as compared with the real values for the Gaussian membership function (gaussmf). Pusat et al () estimated the MC during convective drying for coal by ANFIS. Also, Ziaforoughi, Yousefi, and Razavi () predicted the MR for quince in infrared dryer using ANFIS model.…”

Section: Resultsmentioning

confidence: 99%

“…In general, when an ANFIS model is used with three inputs ( T , P , and DT) and one output ( f ), for example, the if‐then fuzzy rules can be stated in the following forms (Pusat et al, ):

Rule 1 : if T is A_{1}, P is B_{1} and DT is C_{1} then f_{1} = p_{1} T + q_{1} P + r_{1} italicDT + u_{1}

Rule 1 : if T is A_{2}, P is B_{2} and DT is C_{2} then f_{2} = p_{2} T + q_{2} P + r_{2} italicDT + u_{2}

where, T is the input air temperature (°C), P is the duration of ultrasonic pre‐treatment (min) and DT is the drying time (min), f is the MR, A 1 , A 2 , B 1 , B 2 , C 1 , and C 2 are the language indicators, r 1 , r 2 , q 1, q 2 , p 1 , p 2 , u 1 ,and u 2 are the linear coefficients of the output function and f 1 and f 2 are first‐degree polynomials.…”

Section: Methodsmentioning

confidence: 99%

“…Azadeh, Neshat, Kazemi, and Saberi () applied the ANNs and ANFIS to control the drying process. Pusat et al () investigated the MC of coal in convective dryer using ANFIS. Al‐Mahasneh, Aljarrah, Rababah, and Alu'datt () reported the advantages of ANFIS models for food processing and technology.…”

Section: Introductionmentioning

confidence: 99%

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The effect of ultrasound pre‐treatment on quality, drying, and thermodynamic attributes of almond kernel under convective dryer using ANNs and ANFIS network

Kaveh

Jahanbakhshi

Abbaspour‐Gilandeh

et al. 2018

J Food Process Engineering

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In this study, different drying conditions were investigated on quality and thermodynamic properties of almond kernel. Experiments were performed using a convection dryer with ultrasound pretreatment in 40, 50, 60, and 70 °C air temperature, 1 m/s air velocity, and duration of ultrasonic pre‐treatment of 0 min (for control sample), 10, 20, and 40 min. The drying kinetic of the almond kernel was estimated by 15 mathematical models. Furthermore, Artificial Neural Networks (ANNs) and Adaptive Neuro‐Fuzzy Inference Systems (ANFIS) were applied to fit the experimental data on the thin layer drying. The lowest and highest values of the effective moisture diffusivity (Deff) was 1.81 × 10−9 and 9.70 × 10−9 m2/s, respectively. Activation energy (Ea) of the samples was obtained between 26.35 and 36.44 kJ/mol. The highest and lowest values of specific energy consumption (SEC) were calculated 561.72 and 169.88 kW hr/kg, respectively. Maximum (13.14%) and the minimum (7.1%) values of shrinkage were achieved at air temperatures of 70 and 40 °C, respectively. The color changing of dried samples was obtained between 9.14 and 17.96. Furthermore, results revealed that the ANFIS model had the high ability to predict the moisture ratio (R2 = 0.9998 and MSE = 0.0003) during drying. As a result, ANFIS model has the highest ability to evaluate all output as compared with other models and ANNs method. Practical applications Algorithms are modern methods that have been successfully applied to solve the various problems and modeling in engineering and science. Drying is one of the oldest procedures to preserve the food quality. Reduction of moisture content to a certain value can be caused to decay and minimize the microbiological activity and deteriorating chemical reactions in agricultural products, respectively. Determination of almond drying process under convective with ultrasound pre‐treatment dryer in terms of desirable thermal properties (effective moisture diffusivity and energy consumption) provides the high‐quality products. Furthermore, this research can be able to provide a technical basis for almond drying and the related equipment designing.

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Section: Resultsmentioning

confidence: 99%

“…In general, when an ANFIS model is used with three inputs ( T , P , and DT) and one output ( f ), for example, the if‐then fuzzy rules can be stated in the following forms (Pusat et al, ):

Rule 1 : if T is A_{1}, P is B_{1} and DT is C_{1} then f_{1} = p_{1} T + q_{1} P + r_{1} italicDT + u_{1}

Rule 1 : if T is A_{2}, P is B_{2} and DT is C_{2} then f_{2} = p_{2} T + q_{2} P + r_{2} italicDT + u_{2}

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The effect of ultrasound pre‐treatment on quality, drying, and thermodynamic attributes of almond kernel under convective dryer using ANNs and ANFIS network

Kaveh

Jahanbakhshi

Abbaspour‐Gilandeh

et al. 2018

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…They are mainly used for prediction, clustering, classifi cation, and alerting to abnormal patterns (Efendigil et al, 2009; Pusat et al, 2016). An ANN model, which is formed of n layers, presents a different number of computational elements that function like biological neurons and intensive connections between these computational elements among layers.…”

Section: Artificial Neural Network (Anns) 2 Umjetne Neuronske Mrežementioning

confidence: 99%

Application of Artificial Neural Networks Using Bayesian Training Rule in Sales Forecasting for Furniture Industry

2017

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“…In this paper, we use a soft computing approach to select the most influential variables for patent applications. The Adaptive Neuro-fuzzy Inference Technique (ANFIS) [11][12][13][14][15] is applied to the available data sets to select the predominant model variables. Table 1 shows nine input and two output parameters used in this research.…”

Section: Introductionmentioning

confidence: 99%