In this study, an efficient optimisation method by combining response surface methodology (RSM) and genetic algorithm (GA) is introduced to find the optimal topology of artificial neural networks (ANNs) for predicting colour changes in rehydrated apple cubes. A multi-layered feed-forward backpropagation ANN model of algorithms was developed to correlate one output (colour change) to four input variables (drying air temperature, drying air velocity, temperature of distilled water and rehydration time). A predictive model for ANN topology in terms of the best mean squared error (MSE) performance on validation samples was created using RSM. RSM model was integrated with an effective GA to find the optimum topology of ANN. The optimum ANN had minimum MSE when the number of hidden neurons, learning rate, momentum constant, number of epochs and number of training runs were 13, 0.33, 0.89, 3869 and 3, respectively. MSE of optimal ANN topology on validation samples was 0.0072095. It turned out that the optimal ANN topology can be considered as more precise for predicting colour change in the rehydrated apple cubes. Mean absolute error and regression coefficient (R) of the optimal ANN topology were determined as 0.0259 and 0.96475 for training, 0.0399 and 0.95243 for testing and 0.0264 and 0.95151 for validation data sets. The results of the testing model on new samples showed excellent agreement between the actual and predicted data with coefficient of determination R 2 = 0.97.
The effect of convective drying temperature (Td), air velocity (v), rehydration temperature (Tr), and kind of rehydrating medium (pH) was studied on the following apple quality parameters: water absorption capacity (WAC), volume ratio (VR) color difference (CD). To model, simulate, and optimize parameters of the drying and rehydration processes hybrid methods artificial neural network and multiobjective genetic algorithm (MOGA) were developed. MOGA was adapted to the apple tissue, where the simultaneous minimization of CD and VR and the maximization of WAC were considered. The following parameters range were applied, 50 ≤ Td ≤ 70 °C and 0.01 ≤ v ≤ 6 m/s for drying and 20 ≤ Tr ≤ 95 °C for rehydration. Distilled water (pH = 5.45), 0.5% solution of citric acid (pH = 2.12), and apple juice (pH = 3.20) were used as rehydrating media. For determining the rehydrated apple quality parameters the mathematical formulas were developed. The following best result was found. Td = 50.1 °C, v = 4.0 m/s, Tr = 20.1 °C, and pH = 2.1. The values of WAC, VR, and CD were determined as 4.93, 0.44, and 0.46, respectively. Experimental verification was done, the maximum error of modeling was lower than 5.6%.
Water saturation deficit (WSD) is a parameter commonly used for detection of plant tolerance to temporary water shortages. However, this parameter does not meet criteria set for screening. On the other hand, measurement of chlorophyll (Chl) a fluorescence is a fast and high-throughput method. This work presents the application of learning systems to set up a model between WSD and Chl a fluorescence parameters allowed for development of a new screening test. Multilayer perceptron (MLP) was trained to predict WSD values on the basis of Chl a fluorescence. The best MLP consisted of three inputs: maximal quantum yield of PSII photochemistry, approximated number of active PSII reaction centres per absorption, and measure of forward electron transport, three hidden nodes and one output (WSD). The MLP precision was 82% with a correlation coefficient of 0.98. Continuous improvement of MLP structure and model adaptation to new data takes place.
An analysis of the state of affairs in the theory and practice of implementation of technologically integrated projects in various applied fields was carried out. The peculiarities of the implementation of the technologically integrated projects of the “European Green Deal” for the production of ecologically clean fuel from agricultural waste were analyzed. The expediency of developing a method of coordinating the configurations of technologically integrated “European Green Deal” projects for the production of ecologically clean energy from agricultural waste on the territory of a given region, taking into account their specific project environments, was substantiated. As a result of the conducted research, a method of coordinating configurations of the technologically integrated “European Green Deal” projects for the production of ecologically clean energy from agricultural waste in the territory of a given region, taking into account their specific project environment, was developed. This method involves the implementation of five stages, which ensure consideration of the specific design environment of each region and the type of agricultural raw materials for energy production. This method involves the modeling of individual projects, which makes it possible to increase the accuracy of determining their value indicators, taking into account risk. The balancing of the technologically integrated projects of the “European Green Deal” for the production of clean energy from agricultural waste was carried out on the basis of maximizing value for stakeholders and minimizing risk. On the basis of the proposed method, the computer program “Balancing technologically integrated projects” was developed. The use of this computer program for the given project environment (conditions of LLC “Lutsk Agrarian Company” of the Volyn region, Ukraine) made it possible to forecast the specific value and risk of individual projects involving harvesting raw materials from corn waste. The statistical characteristics of the distribution of the projected specific value of the project of harvesting raw materials from corn waste were established: the estimate of mathematical expectation—EUR 9/ton; dispersion—EUR 25/ton; the estimation of root mean square deviation—EUR 5/ton. The technologically integrated projects of the “European Green Deal” for the production of ecologically clean energy from corn waste with the greatest interconnections in terms of value were identified. The ranking of raw material procurement projects from corn waste was carried out according to their specific values and risks. Among the considered projects, priority was given to project #7 and project #1, which provided the greatest values, 37.6% and 36.6%, respectively, of the total value of the considered projects. The obtained results made it possible to establish priority projects and carry out their balancing by value and risk.
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