Genetic-Algorithm-Based Optimization of an Industrial Age-Hardening Operation for Packed Bundles of Aluminum Rods

Industrial thermal processing operations are highly energy intensive, and must be efficiently operated, especially under the current global energy crisis. However, optimization of such complex operations is a nontrivial task and must be formally approached. In the present work, an integrated model has been used in conjunction with a genetic algorithm to optimize industrial age-hardening of packed bundles of aluminum alloy rods. The presence of peak yield strength and the strong dependence of peak time on age-hardening temperature make this operation interesting for optimization studies. Multiobjective optimization problems have been formulated and solved for energy minimization, productivity maximization, and variability reduction during age-hardening by an elitist nondominated sorting genetic algorithm. The Pareto-front for optimum furnace temperature, time, and quality variation has been determined for isothermal as well as multisegment nonisothermal furnace temperature profiles. Subsequently, optimum multisegment furnace temperature-time set-points were obtained and compared with equivalent isothermal profile. The advantage of multisegment nonisothermal profile over isothermal profile in reducing the energy consumption has been demonstrated.

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