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)

• Published in: Food science & nutrition Vol. 8; no. 8; pp. 4432 - 4445
• Main Authors: Vahedi Torshizi, Mohammad, Azadbakht, Mohsen, Kashaninejad, Mahdi
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.08.2020; John Wiley and Sons Inc; Wiley
• Subjects: Activation; artificial neural network; Artificial neural networks; Electrical conductivity; Electrical resistivity; Energy efficiency; Exergy; Experiments; Fruit juices; Heat; Heating; Mathematical analysis; Methods; modeling; Modelling; Natural resources; Neural networks; Ohmic heating; Oranges; Original Research; Power consumption; Radial basis function; Researchers; response surface method; Response surface methodology; sour orange; Statistical methods; thermodynamic analysis; Thermodynamics; Weight loss; artificial neural network; response surface method; thermodynamic analysis; modeling; sour orange; Ohmic heating
• ISSN: 2048-7177; 2048-7177
• DOI: 10.1002/fsn3.1741

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. 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. In general, the neural network can have more ability than the response surface method.