Mathematical modeling of drying characteristics of Jew’s mallow (Corchorus olitorius) leaves

Omolola, Adewale Olusegun; Kapila, Patrick Francis; Silungwe, Henry

doi:10.1016/j.inpa.2018.08.003

Cited by 19 publications

(16 citation statements)

References 19 publications

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“…), n b is the number of terms taken into consideration, t is the drying time (s), eff D is the effective moisture diffusivity (m 2 /s). Moisture ratio ( MR ) can be simplified to o t M M because e M was relatively small compared to t M and 0 M (Omolola et al, 2018). If the second and following terms in the sum in Eq.…”

Section: Effective Moisture Diffusivitymentioning

confidence: 99%

Optimization of Infrared-convective Drying of White Mulberry Fruit Using Response Surface Methodology and Development of a Predictive Model through Artificial Neural Network

Golpour

Kaveh

Chayjan

et al. 2020

International Journal of Fruit Science

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In this research, a comparative approach was carried out between artificial neural networks (ANNs) and response surface methodology (RSM) to optimize the drying parameters during infrared-convective drying of white mulberry. The drying experiments were performed at different air temperatures (40, 55 and 70 °C), air velocities (0.4. 1 and 1.6 m/s) and infrared radiation power (500, 1000 and 1500 W). RSM focuses on maximization of effective moisture diffusivity ( eff D ) and minimization of specific energy consumption ( SEC ) in the drying process. The optimized conditions were encountered for the air temperature of 70 °C, the air velocity of 0.4 m/s and the infrared power level of 1464.57 W. The optimum values of eff D and SEC were 1.77×10 -9 m 2 /s and 166.554 MJ/kg, respectively, with the desirability of 0.9670. Based on the statistical indices, the results showed that the Feed and Cascade Forward Back Propagation neural systems with application of Levenberg-Marquardt training algorithm and topologies of 3-20-20-1 and 3-10-10-1 were the best neural models to predict eff D and SEC , respectively.

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Section: Effective Moisture Diffusivitymentioning

confidence: 99%

Optimization of Infrared-convective Drying of White Mulberry Fruit Using Response Surface Methodology and Development of a Predictive Model through Artificial Neural Network

Golpour

Kaveh

Chayjan

et al. 2020

International Journal of Fruit Science

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“…The values of dry basis (d.b.) MC and MR of samples were calculated using the following equations (Ceylan & Gürel, 2016; Omolola et al., 2019):

{MC}_{d . b .} = \frac{m ‐ m_{oven}}{m_{oven}}

MR = \frac{MC}{{MC}_{in}}

…”

Section: Methodsmentioning

confidence: 99%

Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared‐assisted continuous hybrid solar dryer

Bakhshipour

Zareiforoush

Bagheri

2020

Food Science & Nutrition

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Drying characteristics of stevia leaves were investigated in an infrared (IR)‐assisted continuous‐flow hybrid solar dryer. Drying experiments were conducted at the inlet air temperatures of 30, 40, and 50°C, air inlet velocities of 7, 8, and 9 m/s, and IR lamp input powers of 0, 150, and 300 W. The results indicated that inlet air temperature and IR lamp input power had significant effect on drying time (p < .05). A comparative study was performed among mathematical, Artificial Neural Networks (ANNs), and Adaptive Neuro‐Fuzzy System (ANFIS) models for predicting the experimental moisture ratio (MR) of stevia leaves during the drying process. The ANN model was the most accurate MR predictor with coefficient of determination (R2), root mean squared error (RMSE), and chi‐squared error (χ2) values of 0.9995, 0.0005, and 0.0056, respectively, on test dataset. These values of the ANFIS model on test dataset were 0.9936, 0.0243, and 0.0202, respectively. Among the mathematical models, the Midilli model was the best‐fitted model to experimental MR values in most of the drying conditions. It was concluded that artificial intelligence modeling is an effective approach for accurate prediction of the drying kinetics of stevia leaves in the continuous‐flow IR‐assisted hybrid solar dryer.

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“…Selanjutnya, kadar air bahan diubah menjadi nilai rasio kelembaban (MR). Jika kelembaban relatif udara pengering berfluktuasi secara terus menerus, maka kadar air kesetimbangan terus berubah sehingga MR dapat ditentukan seperti pada Persamaan 4 (Erbay & Icier, 2010;Amarasinghe et al, 2018;Omolola et al, 2019).…”

Section: Model Matematika Pengeringanunclassified

Pemodelan Matematika Karakteristik Pengeringan Lada Putih Pada Pengering Spouted Bed Dengan Perlakuan Preheating Gelombang Mikro

Mukhlis

Fadhilah

2021

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Untuk mempelajari pengaruh pengeringan terhadap biji lada putih diperlukan pengetahuan yang mendalam tentang kinetika pengeringan. Kinetika pengeringan bahan dapat dijelaskan melalui model matematika yang biasanya digunakan untuk memperkirakan waktu pengeringan bahan. Penelitian ini bertujuan untuk menentukan model matematika pengeringan yang tepat untuk pengeringan spouted bed lada putih dengan perlakuan preheating gelombang mikro. Peralatan yang digunakan dalam penelitian ini adalah alat pengering spouted bed yang dirancang untuk skala laboratorium. Bahan yang digunakan dalam penelitian ini berupa biji lada putih basah yang diperoleh dari perkebunan rakyat di kabupaten Enrekang provinsi Sulawesi Selatan dengan umur kira-kira 8-9 bulan setelah pembungaan. Lada putih langsung dimasukkan ke dalam ruang pengeringan spouted bed untuk perlakuan non-preheating sedangkan untuk perlakuan preheating lada disimpan dalam wadah kemudian dimasukkan ke dalam oven microwave terlebih dahulu selama 2 menit. Selama proses pengeringan, beberapa parameter untuk analisis pengeringan diukur. Terdapat tujuh model matematika pengeringan berbeda yang dievaluasi. Dalam penentuan model matematika yang paling sesuai, diperlukan validasi model melalui metode statistika. Metode statistik yang digunakan adalah analisis korelasi, uji chi-square (χ2) tereduksi dan analisis root mean square error (RMSE). Berdasarkan hasil analisis, model Weibull memenuhi kriteria untuk dijadikan model terbaik dengan koefisien korelasi r (0,99990) adalah yang tertinggi serta dan nilai χ2 (0,00001) serta nilai RMSE (0,00385) adalah yang terendah. Dengan demikian, model Weibull dapat digunakan untuk memprediksi waktu dan kadar air pengeringan.

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Mathematical modeling of drying characteristics of Jew’s mallow (Corchorus olitorius) leaves

Cited by 19 publications

References 19 publications

Optimization of Infrared-convective Drying of White Mulberry Fruit Using Response Surface Methodology and Development of a Predictive Model through Artificial Neural Network

Optimization of Infrared-convective Drying of White Mulberry Fruit Using Response Surface Methodology and Development of a Predictive Model through Artificial Neural Network

Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared‐assisted continuous hybrid solar dryer

Pemodelan Matematika Karakteristik Pengeringan Lada Putih Pada Pengering Spouted Bed Dengan Perlakuan Preheating Gelombang Mikro

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