Applying calculation methods and computer simulation in the canned products sterilization processes

The final stage in the development of new canned foods is heat treatment. It is carried out in order to inactivate the microflora that somehow got into the product. This process requires finding a balance between the positive and negative effects of high temperatures on the product. The positive effect consists in the inactivation of microorganisms present in food products and negatively affecting consumer health and product safety. The negative impact is expressed in a decrease in nutritional value, the loss of nutrients that are unstable to high temperatures (for example, vitamins, etc.). In the study of the dynamics of heating of canned products, a pronounced unevenness of heat treatment, the duration of thermal exposure and high energy costs are noted. The current heat treatment modes can exceed the standard lethality by 30-50 %, and sometimes several times. Carrying out a large number of product warm-ups in order to find the optimal ratio of temporary exposure and temperature is very expensive. The most appropriate is the use of calculation methods and computer simulation of heat treatment processes in order to determine the optimal combination of the duration of the temperature effect on the product. The analysis of the given literary sources indicates several directions for determining the optimization of the modes of thermal sterilization of food products (calculation by the difference in the values of the least heated point and the heating temperature of the containers, by the values between the calculated and received lethality, calculation of the mode taking into account the data of the Stumbo tables, calculation by indicators of the expended energy). Conducting research to determine the dynamics of heating in the least heated zone in various food systems on lethality and the influence of the patterns of thermal conductivity of food systems on the nature of heat transfer will further develop a method for predicting and optimizing sterilization processes.