A study on the energy and exergy of Ohmic heating (OH) process of sour orange juice using an artificial neural network (ANN) and response surface methodology (RSM)
© 2020 The Authors. Food Science & Nutrition published by Wiley Periodicals LLC..
The nonmodern statistical methods are often unusable for modeling complex and nonlinear calculations. Therefore, the present research modeled and investigated the energy and exergy of the ohmic heating process using an artificial neural network and response surface method (RSM). The radial basis function (RBF) and the multi-layer perceptron (MLP) networks were used for modeling using sigmoid, linear, and hyperbolic tangent activation functions. The input consisted of voltage gradient; weight loss percentage, duration ohmic, Input flow, Power consumption, electrical conductivity and system performance coefficient and the output included the energy efficiency, exergy efficiency, exergy loss, and improvement potential. The response surface method was also used to predict the data. According to the result, the best prediction amount for energy and exergy efficiencies, exergy loss and improvement potential were in RBF network by sigmoid activation function and after this network, RSM had the best amount for energy efficiency, Also for exergy efficiencies, exergy loss and improvement potential obtained acceptable results in MLP network by a linear activation function. The worst amount was at MLP network by tangent hyperbolic. In general, the neural network can have more ability than the response surface method.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:8 |
|---|---|
| Enthalten in: |
Food science & nutrition - 8(2020), 8 vom: 01. Aug., Seite 4432-4445 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Vahedi Torshizi, Mohammad [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Artificial neural network |
|---|
| Anmerkungen: |
Date Revised 29.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1002/fsn3.1741 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM314575642 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM314575642 | ||
| 003 | DE-627 | ||
| 005 | 20240329234837.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1002/fsn3.1741 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1354.xml |
| 035 | |a (DE-627)NLM314575642 | ||
| 035 | |a (NLM)32884723 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Vahedi Torshizi, Mohammad |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A study on the energy and exergy of Ohmic heating (OH) process of sour orange juice using an artificial neural network (ANN) and response surface methodology (RSM) |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 29.03.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2020 The Authors. Food Science & Nutrition published by Wiley Periodicals LLC. | ||
| 520 | |a The nonmodern statistical methods are often unusable for modeling complex and nonlinear calculations. Therefore, the present research modeled and investigated the energy and exergy of the ohmic heating process using an artificial neural network and response surface method (RSM). The radial basis function (RBF) and the multi-layer perceptron (MLP) networks were used for modeling using sigmoid, linear, and hyperbolic tangent activation functions. The input consisted of voltage gradient; weight loss percentage, duration ohmic, Input flow, Power consumption, electrical conductivity and system performance coefficient and the output included the energy efficiency, exergy efficiency, exergy loss, and improvement potential. The response surface method was also used to predict the data. According to the result, the best prediction amount for energy and exergy efficiencies, exergy loss and improvement potential were in RBF network by sigmoid activation function and after this network, RSM had the best amount for energy efficiency, Also for exergy efficiencies, exergy loss and improvement potential obtained acceptable results in MLP network by a linear activation function. The worst amount was at MLP network by tangent hyperbolic. In general, the neural network can have more ability than the response surface method | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Ohmic heating | |
| 650 | 4 | |a artificial neural network | |
| 650 | 4 | |a modeling | |
| 650 | 4 | |a response surface method | |
| 650 | 4 | |a sour orange | |
| 650 | 4 | |a thermodynamic analysis | |
| 700 | 1 | |a Azadbakht, Mohsen |e verfasserin |4 aut | |
| 700 | 1 | |a Kashaninejad, Mahdi |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Food science & nutrition |d 2013 |g 8(2020), 8 vom: 01. Aug., Seite 4432-4445 |w (DE-627)NLM238025020 |x 2048-7177 |7 nnns |
| 773 | 1 | 8 | |g volume:8 |g year:2020 |g number:8 |g day:01 |g month:08 |g pages:4432-4445 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1002/fsn3.1741 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 8 |j 2020 |e 8 |b 01 |c 08 |h 4432-4445 | ||