Previous hydrocyclone multi-objective optimizations often restricted secondary objectives or focused on two specific objectives, possibly compromising the overall system's effectiveness. This study presents an integrated framework using Pareto-based algorithms and preference-informed decision-making without limiting objective ranges. It conducts a quantitative assessment of Pareto optimal sets from six widely-used multi-objective algorithms, spotlighting the strength Pareto evolutionary algorithm 2 (SPEA2) for its ability in capturing the trade-offs among objectives. The technique for order of preference by similarity to ideal solution (TOPSIS) method is utilized to quantify overall separation performance, facilitating the selection of an optimal hydrocyclone design aligned with specific separation preferences. This method underscores the significance of concurrently optimizing key performance objectives and translates intricate system interactions into a measurable balance between energy consumption and separation efficiency, thereby streamlining the selection process for the most appropriate hydrocyclone design. The efficacy of this method is confirmed through comparative Two-Fluid Model simulations.
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