Drying of Apple Slices in Combined Heat and Power (CHP) Dryer: Comparison of Mathematical Models and Neural Networks
-
Seyed Hashem Samadi
,
GholamHassan Najafi
Abstract
Consuming a high amount of energy in industrial dryer makes the drying as an important and highly energy-intensive industrial process. In this study, the required energy for drying of apple slice was supplied from the outlet heat of an engine generation set exhaust. The drying behavior of apple slice was studied in a combined heat and power (CHP) dryer system at four engine load levels (25, 50, 75, and 100%) in order to provide different temperatures (50, 65, 80, and 95°C), and at three levels of drying product thickness (3, 5, and 7 mm) with the constant air flow velocity of 1 m/s. The empirical data from experiments with variants of semi-theoretical and empirical models were evaluated and finally, a suitable model proposed by Midilli et al. was selected to be the best model, as for as the RMSE, R2, and χ2 criterion is concerned. Comparing the results from implementing artificial neural network (ANN) and mathematical models, it was found that the dynamic ANNs is more powerful for modeling the drying process of apple slice in a CHP dryer system than static ANNs and mathematical models.
Acknowledgements
The authors wish to thank the Iranian Fuel Conservation Organization (IFCO) of NIOC for the research grant provided to complete this project and Tarbiat Modares University for providing of laboratory facilities.
Nomenclature
| a, b, c | Drying coefficients |
| k | Drying constants |
| MR | Moisture ratio |
| Me | Equilibrium moisture content (kg water/kg solids) |
| MRexp,i | ith moisture ratio value determined experimentally (kg water/kg solids) |
| MRpre,i | ith predicted moisture ratio value (kg water/kg solids) |
| M0 | Initial moisture content (kg water/kg solids) |
| Mt | Moisture content at any given time (kg water/kg solids) |
| Mt+dt | Moisture content at t+dt (kg water/kg solids) |
| m | Number of drying constants |
| N | Number of observations |
| n | Drying constants |
| n0 | Number of neurons in the output layer |
| np | Number of patterns |
| R2 | Determination of coefficient |
| S | Sample thickness (mm) |
| Sip | Desired or actual output |
| RMSE | Root mean square error |
| T | Temperature (°C) |
| t | Drying time (min) |
| Tip | Predicted output for the pattern |
| χ2 | chi-square |
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Articles in the same Issue
- Masthead
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- Ionic Liquids as Green Solvents for the Extraction of Endosulfan from Aqueous Solution: A Quantum Chemical Approach
- Neuro-Fuzzy-Based Control for Parallel Cascade Control
- Modelling the Heat and Mass Transfer during Hot Pressing of Medium Density Fibreboard
- Drying of Apple Slices in Combined Heat and Power (CHP) Dryer: Comparison of Mathematical Models and Neural Networks
- First Principle Modeling and Neural Network–Based Empirical Modeling with Experimental Validation of Binary Distillation Column
- The Correlation of Activity Coefficients of Ionic Species of Aqueous Electrolytes Using a New Model
Articles in the same Issue
- Masthead
- Masthead
- Ionic Liquids as Green Solvents for the Extraction of Endosulfan from Aqueous Solution: A Quantum Chemical Approach
- Neuro-Fuzzy-Based Control for Parallel Cascade Control
- Modelling the Heat and Mass Transfer during Hot Pressing of Medium Density Fibreboard
- Drying of Apple Slices in Combined Heat and Power (CHP) Dryer: Comparison of Mathematical Models and Neural Networks
- First Principle Modeling and Neural Network–Based Empirical Modeling with Experimental Validation of Binary Distillation Column
- The Correlation of Activity Coefficients of Ionic Species of Aqueous Electrolytes Using a New Model
