A Novel Optimization Algorithm for <i>Echium amoenum</i> Petals Drying

A novel multi-objective optimization algorithm was developed, which was successfully applied in the drying process. The effect of drying parameters (air velocity (<i>v_d</i>), drying temperature (<i>T_d</i>)) on the energy consumption (EC) and the quality parameters of <i>Echium amoenum</i> petals in fluidized drying were experimentally studied. The following quality parameters were examined: the color difference, the bioactive compounds as losses of total antioxidant capacity (TAC) and losses of phenolic (TPC), flavonoids (TFC) and anthocyanin (A). The six optimization objectives included simultaneous minimization of the quality parameters and energy consumption. The objective functions represent relationships between process variables and optimization objectives. The relations were approximated using an Artificial Neural Network (ANN). The Pareto optimal set with a nondominated sorting genetic algorithm was developed. No unequivocal solution to the optimization problem was found. Cannot be obtained <i>E. amoenum</i> petals characterized a low color change at low energy consumption due to its fluidized drying. Unique Pareto optimal solutions were found: <i>T_d</i> = 54 °C and <i>v_d</i> = 1.0 m/s–for the strategy in which the lower losses of TAC, TFC and A are most important, and <i>T_d</i> = 59.8 °C and <i>v_d</i> = 0.52 m/s–for the strategy in which the lower losses of TPC and TFC are important with accepted EC values. The results of this research are essential for the improvement of industrial dehydration of <i>E. amoenum</i> petals in order to maintain their high content of bioactive compounds with low energy consumption and low colour change