Thermal food processing optimization: Algorithms and software

Abakarov, A.; Núñez, Martín

doi:10.1016/j.jfoodeng.2012.02.013

Cited by 28 publications

(21 citation statements)

References 35 publications

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“…The same mathematical model made of the ten equations (26), (27), (8), (7), (13), (14), (16), (19), (21), and (28) in Figure 4. The following values were found: determination coefficient 2 = 0.9990, mean relative error MRE = 2.67 ± 2.69%, and mean absolute error MAE = 0.16 To calculate the thermal process time reference, by values from Stumbo's tables [7], the Ball's formula (4) was used.…”

Section: Calculationmentioning

confidence: 99%

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A New Mathematical Model for Food Thermal Process Prediction

Friso

2013

Modelling and Simulation in Engineering

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A mathematical model was developed to correlate the four heat penetration parameters of 57 Stumbo's tables (18,513 datasets) in canned food: (the difference between the retort and the coldest point temperatures in the canned food at the end of the heating process), ℎ / (the ratio of the heating rate index to the sterilizing value), (the temperature change required for the thermal destruction curve to traverse one log cycle), and , (the cooling lag factor). The quantities , , and , are input variables for predicting ℎ / , while , and ℎ / are input variables for predicting the value of , which is necessary to calculate the heating process time , at constant retort temperature, using Ball's formula. The process time calculated using the value obtained from the mathematical model closely followed the time calculated from the tabulated values (root mean square of absolute errors RMS = 0.567 min, average absolute error = 0.421 min with a standard deviation SD = 0.380 min). Because the mathematical model can be used to predict the intermediate values of any combination of inputs, avoiding the storage requirements and the interpolation of 57 Stumbo's tables, it allows a quick and easy automation of thermal process calculations and to perform these calculations using a spreadsheet.

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Section: Calculationmentioning

confidence: 99%

“…The use of mathematical modeling by computational fluid dynamics (CFD) of the heat transfer for evaluating thermal process has been showing to be a powerful tool to assure food safety and nutritional quality [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A New Mathematical Model for Food Thermal Process Prediction

Friso

2013

Modelling and Simulation in Engineering

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“…The searching for sterilization processes of canned food models attracted many scientists and researchers of the late twentieth century [1][2][3][4][5][6][7][8][9][10][11][12]. One of the most famous researchers in this field is Giulio R. Banga, who proposed methods for improving food processing using modern optimization methods [13].…”

Section: Brdem-2019mentioning

confidence: 99%

Development of an Autoclave Thermal Processes Model for the Simulator of Canned Food Sterilization Process

Kaychenov

Vlasov

Maslov

et al. 2020

KLS

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The article describes an autoclave thermal processes model, which is used for the simulator of canned food sterilization process. The simulator is based on a simulation model that adequately describes the reaction of the autoclave to the actions of the control system and the operator of the sterilization unit. The model’s parameters were obtained by means of experimental data processing. The computer program ”autoclave Model” for simulating sterilization process in the steam and water environment is described. The examples of the canned food’s manual control sterilization modeling are shown. The results of numerical mathematical modeling of canned food sterilization processes in the autoclave showed a high degree of the implemented process models quality of approximation. The calculation schemes done as a result of the mathematical models creation were used to develop a hardwaresoftware complex of the sterilization process simulator. The increase of training level on carrying out process of canned goods sterilization will be provided as a result of designing the simulator of sterilization process in educational process. Consequently reducing defects in production and improving the quality of canned products are expected.

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“…In order to satisfy the constraint (6), the following penalty function is proposed (Abakarov & Nuñez, 2012):…”

Section: Penalty Functions Approachmentioning

confidence: 99%

“…This class of algorithms is based on generating the decision variables from a given probability distribution, and the term "adaptive" consists of modifications to the probability distribution used in the searching process, which, throughout the whole search process, locates global solution. A discrete analogue of the normal distribution -the pedestal probability distribution -is included in the adaptive random search algorithm (Abakarov & Nuñez, 2012). During the search process, a random search generates random vector values x 0 , x 1 , ..., x s ; calculates the optimization problem Φ(x) → min x∈X ; accumulates information about the solved problem; and transforms the pedestal frequency distribution according to the computations performed.…”

Section: Adaptive Random Search Algorithmmentioning

confidence: 99%

A multi-criteria optimization and decision-making approach for improvement of food engineering processes

2013

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The objective of this study was to propose a multi-criteria optimization and decision-making technique to solve food engineering problems. This technique was demonstrated using experimental data obtained on osmotic dehydration of carrot cubes in a sodium chloride solution. The Aggregating Functions Approach, the Adaptive Random Search Algorithm, and the Penalty Functions Approach were used in this study to compute the initial set of non-dominated or Pareto-optimal solutions. Multiple non-linear regression analysis was performed on a set of experimental data in order to obtain particular multi-objective functions (responses), namely water loss, solute gain, rehydration ratio, three different colour criteria of rehydrated product, and sensory evaluation (organoleptic quality). Two multi-criteria decision-making approaches, the Analytic Hierarchy Process (AHP) and the Tabular Method (TM), were used simultaneously to choose the best alternative among the set of non-dominated solutions. The multi-criteria optimization and decision-making technique proposed in this study can facilitate the assessment of criteria weights, giving rise to a fairer, more consistent, and adequate final compromised solution or food process. This technique can be useful to food scientists in research and education, as well as to engineers involved in the improvement of a variety of food engineering processes.

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Thermal food processing optimization: Algorithms and software

Cited by 28 publications

References 35 publications

A New Mathematical Model for Food Thermal Process Prediction

A New Mathematical Model for Food Thermal Process Prediction

Development of an Autoclave Thermal Processes Model for the Simulator of Canned Food Sterilization Process

A multi-criteria optimization and decision-making approach for improvement of food engineering processes

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