## 3 Results and Discussion
### Objective Functions  
To approximate functional relations between air drying temperature, drying air velocity (\(T_{d}\) and \(v_{d}\)) and energy consumption drying and quality parameters (CD, A\({}_{\text{loss}}\), TAC\({}_{\text{loss}}\), TFC\({}_{\text{loss}}\), TPC\({}_{\text{loss}}\)) of dried _E.__anomem_ petals, different ANN structures (with various transfer functions) were tested. Considering the lowest MSE, the best result (MSE = 0.00029) was obtained for the ANN structure which consisted of eight nodes in the hidden layer (see Figure 1, Table 2). The hidden and output layers of the best ANN structure processed data with a log-sigmoid transfer function both in output and hidden layers, respectively, as demonstrated in Figure 1 (see Table 3, Item 3). The ANN training phase was stopped at the 21 st iteration as shown in Figure 2. It can be seen that test set error and validation error have similar characteristics. The following final MSE values were obtained: 0.00075, 0.00029, 0.0021 for training, validation and test sets, respectively. Therefore, the final mean-square error is small. Linear regression between the network outputs and the corresponding \begin{table}
\begin{tabular}{c c} \hline \hline
**Population Type** & **Double Vector** \\ \hline Crossover function & Intermediate \\ Crossover rate & 85\% \\ Migration & Forward \\ Mutation function & Uniform \\ Mutation rate & 15\% \\ Number of generations & 300 \(\times\) number of variables \\ Pareto front population fraction & 0.8 \\ Population size & 20-number of variables \\ Selection function & Tournament size = 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The Nondominated Sorting Genetic Algorithm (NSGA II) parameters. targets is shown in Figure 3. The following R: 0.9933, 0.9974, and 0.9818 for training, validation and test sets, respectively, indicated that data from the ANN were in agreement with the experimental data. Finally, the \(R\)-value is over 0.99 for the total response (see Figure 3). The ANN training phase was stopped at the 21 st iteration (Figure 2). The following final MSE values were obtained: 0.00075, 0.00029, 0.0021 for training, validation and test sets, respectively. The R of 0.9933, 0.9974, and 0.9818 for training, validation and test sets, respectively, indicated that data from the ANN were in agreement with the experimental data (Figure 3). The hidden and output layers of the best ANN structure processed data with a log-sigmoid transfer function (Figure 1). The energy consumption for drying process was determined with the following formula (from ANN, taking into account multiplication by ECmax)  
\[\text{EC}=\frac{1397.4}{1+\text{exp}^{-(0.9534807.175458007.21+12091257.3+1.08 827104.4^{+2.10201209.65+1.1258411.66-48500887.7-3.50348107.65827)}}} \tag{10}\]  
whereas quality parameters of dried petals from formulas (from ANN, taking into account multiplication by the appropriate quality parameters maximum values)  
\[\text{TAC}_{\text{loss}}=\frac{85.45}{1+\text{exp}^{-(0.9534807.17+2.21271087.2 -3.52348087.3-1.083084.248108203.65-8.8047587.65-1.16718807.7+5.53024827.65 +2.805907.1)}} \tag{11}\]  
Figure 3: The ANN goodness of fit. Figure 2: Changes in the MSE calculated for the following sets: test, training and validation.