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Item type:Publication, Valorization of Black Plum Peel in Spread Formulation: Optimization of Physicochemical and Sensory Properties via RSM(MDPI AG, 2025-12-23); Plum peel is a major by-product of plum processing and a rich source of nutrients and bioactive compounds. This study aimed to optimize a black plum peel spread formulated with apple puree (20–40%) and plum puree (10–30%) using response surface methodology (RSM). Increasing apple puree up to 30% reduced acidity, firmness, cohesiveness, and consistency while improving sourness and overall acceptability. At 40%, apple puree decreased total acceptability and sourness while slightly increasing texture parameters. Increasing plum puree up to 20% lowered acidity, firmness, consistency, cohesiveness, viscosity, and sourness, but further increases to 30% reversed these effects. Both apple and plum purees enhanced antioxidant capacity in a concentration-dependent manner. The interaction between apple and plum purees notably affected the viscosity of the spread. Overall, plum puree had the strongest influence on texture and color, while apple puree primarily affected sensory acceptance. The optimal formulation was 32.01% apple puree and 28.16% plum puree (R2 = 0.999). Developing a spread from black plum peel demonstrates a sustainable strategy for upcycling fruit-processing waste into nutritious, high-value products. This approach reduces environmental impact, supports circular food production, and creates new opportunities for functional spreads in the health-oriented food markets. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Metaheuristic-Based PID Controller Design with MOOD Decision Support Applied to Benchmark Industrial SystemsThis paper presents a comprehensive methodology for the multiobjective tuning of MIMO proportional integral derivative (PID) controllers using advanced metaheuristic strategies. The proposed approach formulates a cost function based on two conflicting performance criteria—the integral of absolute error (IAE) and the integral of absolute derivative of control (IADU)—to explore the trade-off between tracking performance and control effort systematically. Three metaheuristic techniques are employed: stochastic hill climbing, a Voronoi-based heuristic, and the Nondominated Sorting Genetic Algorithm (NSGA-II). A novel Multiobjective Optimization Design (MOOD)-based classification framework is incorporated to facilitate decision making across the Pareto front. The methodology is validated on three benchmark MIMO plants, demonstrating its robustness and generalizability. The results highlight that the NSGA-II controller achieves the lowest IADU value of 0.3694 in the mass damper system while maintaining acceptable performance metrics. The inclusion of a PID-split strategy further enhances system flexibility. This study emphasizes the value of metaheuristics in navigating complex design spaces and delivering tailored control solutions for multiobjective scenarios. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Effect of Optimizing the Stripping and Drying Parameters During Industrial Extraction on the Physicochemical Properties of Soybean Oil(MDPI AG, 2025-02-14); Soybean oil is the second most consumed vegetable oil worldwide and is recognized as a source of heart-healthy polyunsaturated fatty acids. Optimizing the extraction process in the oil industry is essential for both economic and environmental sustainability. This research aimed to determine the optimal conditions for various extraction parameters—stripper temperature (110–140 °C), stripper pressure (150–210 mbar), and dryer pressure (60–120 mbar)—and their effects on the physicochemical properties of soybean oil. These properties include oil-insoluble fine substances, acidity, the color index, peroxide value, oxidative stability, and moisture content. The results indicated that the stripper temperature significantly influenced oil-insoluble fine substances, acidity, the color index, and peroxide value (p < 0.05). The optimal conditions for oil extraction were found to be a stripper temperature of 110 °C, a stripper pressure of 150 mbar, and a dryer pressure of 120 mbar. Under these conditions, the oil-insoluble fine substances, acidity, the color index, peroxide value, oxidative stability, and moisture content of soybean oil were in the ranges of 0.2–0.58%, 0.63–1.15%, 4.3–5.5, 0.67–1.23 meqO2/kg, 3–5.5, and 0.05–0.11%, respectively. These findings provide valuable insight for optimizing soybean oil extraction processes to enhance quality and efficiency. Future advancements in industrial oil extraction are expected to focus on integrating efficient, eco-friendly technologies and enhancing precision through automation and data analytics to optimize yield and minimize waste.