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Fruit and vegetable processing comes 6th in terms of energy consumption in the agri-food industry. At the same time, 88.4% of the industry's final energy consumption structure is thermal energy, which depends heavily on electricity consumption. In addition, fruit and vegetable processing has a significant impact on the environment due to consumption of significant amounts of water. Reducing these three indicators simultaneously would increase the efficiency of the process while improving environmental protection. This paper proposes neural models of thermal energy, electricity and water consumption for selected major fruit- and vegetable-processing plants in Poland. These models were the basis for formulating a multi-criteria optimization task. Optimization of thermal energy, electricity and water consumption was carried out using genetic algorithms. The optimization results in the sense of Pareto can be the basis for the use of sustainable technology in selected fruit- and vegetable-processing plants. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective optimization method for optimizing the process parameters during friction welding of dissimilar metals. The proposed method combines the response surface methodology (RSM) with an a genetic algorithm (GA) method. Ultimate tensile strength (UTS), flash diameter and the heat affected zone (HAZ) width of friction welded nodular cast iron with low carbon steel joints were investigated considering the following process parameters: friction pressure (FP), friction time (FT) and upsetting pressure (UP). Mathematical models were developed and the responses were adequately predicted. Direct and interaction effects of process parameters on responses were studied by plotting graphs. In the case of UTS, FT has high significance followed by: FP and UP. Friction time has high significance on the flash diameter of nodular cast iron followed by UP and FP. However in the case of the low carbon steel flash diameter, UP has high significance followed by FT and FP. In the case of the HAZ width for nodular cast iron and low carbon steel side, friction time has high significance followed by UP and FT. Multiobjective optimization for maximizing the tensile strength and minimizing the flash diameter and the HAZ width was carried out using mathematical model.

This paper presents a genetic algorithm modelling of temperature distribution during heating and cooling of AZ31B magnesium alloys with 7075 aluminium alloy friction welded joints. The temperature distributions estimated in the joints using K-type thermocouples with the accuracy of ±⚟0.1°C. The thermocouples were installed in 1.2 mm holes at the periphery joint - 5, 10, and 15 mm from the weld interface. Temperature reading was performed with a digital thermometer with the requisition frequency of 1000Hz during friction welding. Maximum temperature measurements in the half-radius of the analysed joints were equal to 305°C and 324°C, for the AZ31B magnesium alloy and 7075 aluminium alloy specimens, respectively. Both temperature and increasing temperature gradient at the axial specimens were higher than those at the half-radius and periphery of the joints. The empirical models for heating T=a/b+exp(ct) and cooling phases T=a-btc were formulated by the authors of this study. These models used to describe the temperature curves of welding process. The goodness of fit of tested mathematical models to the experimental data was evaluated with the coefficient of determination R2. A nonlinear regression analysis was conducted to fit the models by genetic algorithm (GA) using computer program MATLAB.

The aim of the paper is to investigate the effect of drying and rehydration parameters on the quality of rehydrated apples and to optimize these parameters based on quality of the rehydrated products. Hybrid artificial neural network (ANN) and multi-objective genetic algorithm (MOGA) method were successfully developed to model, simulate, and optimize the drying and rehydration parameters. This method was applied to the apple tissue, where the simultaneous maximization of the volume ratio (VR) and water absorption capacity (WAC) and the minimization of colour difference (CD) and dry matter loss (DML) were considered. The range of drying and rehydration parameters was: 50-70°C (drying temperature, Td), 0.01-6 m.s-1(drying air velocity, V), and 20-95°C (rehydration temperature, Tr). Additionally, the mathematical formulas for determing the quality parameters of rehydrated apple were developed. The best solution has been found only for: CD, VR, WAC (50.1°C for Td, 0.03 m.s-1 for V, and 67.5°C for Tr). The values of CD, VR, and WAC were predicted as 1.20, 53.2% and 0.57 respectively. It has been observed that DML was always conflicting with other quality parameters. [ABSTRACT FROM AUTHOR]