Exergy and energy analyses of liquid food in an Ohmic heating process: A case study of tomato production

Darvishi, Hosain; Hosainpour, Adel; Nargesi, Farzad; Fadavi, Ali

doi:10.1016/j.ifset.2015.06.012

Cited by 53 publications

(44 citation statements)

References 44 publications

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“…Furthermore, SPC was affected by S/L ratio and the lowest SPC was obtained at 1:40, although there were not any significant differences between 1:10, 1:20, and 1:30 S/L ratios. Darvishi et al () reported that SPCs for evaporating water from tomato samples by ohmic heating system were in the range of 67.07–85.40% and decreased with increasing of voltage gradient. Icier and Ilicali () reported that the SPCs for orange juice, peach puree, and apricot puree depend significantly on the voltage gradient applied and the highest SPC was observed at the lowest voltage gradient.…”

Section: Resultsmentioning

confidence: 99%

“…Since low E loss would display a system with a high performance, a system performance coefficient or SPC was defined as following equation (Darvishi, Hosainpour, Nargesi, & Fadavi, ):

normalS normalP normalC = true(\frac{Q_{t}}{E_{g}} true) \times 100

…”

Section: Methodsmentioning

confidence: 99%

Section: Spc During Ohmic Heatingmentioning

confidence: 99%

See 2 more Smart Citations

The potential of ohmic heating for pectin extraction from orange waste

Saberian

Hamidi‐Esfahani

Gavlighi

et al. 2017

J Food Process Preserv

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The effect of voltage gradient, pH and S/L ratio during ohmic heating up to 90 °C on the heating rate, system performance coefficients (SPCs), yield, and quality of pectin was investigated. The results showed that the rate of heating was increased with increasing of voltage gradient (from 5 to 30 V/cm) and decreased with decreasing of pH (from 4 to 1.5). With increasing of voltage gradient and decreasing of pH and S/L ratio, SPC was increased and the highest SPC was 84.4%. The yield of pectin was increased sharply from 1.17 to 10.69 g/100 g d.m. by decreasing of pH from 4 to 1.5. Finally, the highest SPC and yield of pectin with good quality (high DE and galacturonic acid) were obtained at the voltage gradient of 30 V/cm, pH of 1.5 and S/L ratio of 1:20 g/ml. Practical applications Ohmic heating technology has a good potential to extract some components from plant waste because it heats the material rapidly with high energy efficiency (SPC) in comparison to the conventional heating, therefore it can be an appropriate alternative method for extraction of pectin industrially. In addition to obtaining the highest pectin with good quality, determination of ohmic heating rate (or time of heating) and SPC at different conditions is necessary to design the industrial ohmic extraction.

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Section: Resultsmentioning

confidence: 99%

“…Since low E loss would display a system with a high performance, a system performance coefficient or SPC was defined as following equation (Darvishi, Hosainpour, Nargesi, & Fadavi, ):

normalS normalP normalC = true(\frac{Q_{t}}{E_{g}} true) \times 100

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The potential of ohmic heating for pectin extraction from orange waste

Saberian

Hamidi‐Esfahani

Gavlighi

et al. 2017

J Food Process Preserv

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“…What is needed is a simplified, lower-cost approach to this process one that will be replicable in a range of situations (Azadbakht, Vahedi Torshizi, Noshad, & Rokhbin, 2018). The general equilibrium energy can be expressed by using Equation (2) that the input energy is equal to the output energy (Darvishi, Hosainpour, Nargesi, & Fadavi, 2015).…”

Section: Energy Analysismentioning

confidence: 99%

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)

Torshizi

Azadbakht

Kashaninejad

2020

Food Science & Nutrition

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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.

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“…In spite of the fact that no pretreament of the feed water is required and there is no scaling, the energy consumption (minimum of 218 kWh/m 3 ) for this technology is much larger than conventional thermal evaporation processes, making it economically uncompetitive for brine concentration (66,67). Niche applications of ohmic heating have been found in the food industry for concentrating sour cherry juice (68), pomegranate juice (69) and tomato dewatering (70), where it offers the advantage of high heating rate and maintenance of colour and nutritional value of food.…”

Section: Thermal Evaporation and Crystallisationmentioning

confidence: 99%