Hydrocyclone optimization is typically performed under fixed-condition assumptions, making its performance highly sensitive to changes in the feed particle-size distribution (PSD) and evolving process priorities. This study presents a prototype adaptive and preference-aware multi-objective optimization and control framework that adjusts inlet velocity (V) and feed solids concentration (C) in response to variations in PSD. The framework consists of four main steps: (1) Surrogate model development: A CFD-trained response-surface methodology predicts key performance objectives, including cut size (d50), separation sharpness (Ep), underflow water-split ratio (Rf), pressure drop (dP), and throughput (Q). (2) Multi-objective optimization: The NSGA-II algorithm is employed to identify Pareto-optimal trade-offs. (3) Decision-making: The TOPSIS method is used to select the optimal operating point based on user-defined weights. (4) Supervisory module: This module continuously monitors PSD and weight vectors, triggering re-optimization when predefined thresholds are exceeded, and adjusting (V, C) to align with updated priorities. PSDs are modeled using a modified Johnson-SB distribution, defined by median size (d50) and a dispersion/tail coefficient (6j, ranging from 0.40 to 1.00), where d50 determines location and 6j controls the distribution's width and tails. In 25 PSD scenarios, adaptive set-point updates resulted in a 17-27% reduction in d50, a 14-25% improvement in Ep, and a 38-95% increase in Q compared to a static baseline. Rf remained within acceptable bounds, while dP varied between 0 and 136%, depending on separation requirements. This framework provides an efficient approach for ensuring stable separation under fluctuating feed conditions and offers a practical solution for controlling hydrocyclone performance.

Hydrocyclones are widely used in industrial separation processes due to their compact design and high processing capacity. The structure of the overflow pipe plays a crucial role in determining separation performance. Although numerous studies have explored the effects of overflow pipe wall thickness on hydrocyclone performance, results remain inconsistent. This study combines numerical simulations and physical experiments to investigate the impact of the ratio of the overflow pipe's outer diameter to the cylinder section diameter (RODCD) on the performance and flow characteristics of hydrocyclones (FX75, FX25, and FX75r). The FX75 is a commonly used medium-scale model, while the FX25 is designed for finer particle separation. The FX75r, geometrically similar to the FX75 but scaled to the size of the FX25, is introduced to examine the effects of RODCD across different geometric designs. The results indicate that for the FX75, optimal separation occurs within an RODCD range of 0.48-0.64, while for the FX25, an RODCD of 0.72 improves performance by reducing pressure drop and enhancing separation efficiency. Increasing the RODCD to 0.72 stabilizes the air core, reduces turbulence, and optimizes the vortex structure, leading to improved separation efficiency and reduced energy consumption. However, at excessive RODCD values (e.g., 0.88), flow field destabilization occurs, impairing effective separation. Geometric similarity validation confirms that hydrodynamic stability at higher RODCD values is more strongly influenced by internal geometric proportions than by cylinder diameter. These findings offer valuable insights into optimizing hydrocyclone design and RODCD to enhance separation efficiency in industrial applications.

The separation space of hydrocyclone, including its cylindrical and conical sections, governs internal fluid dynamics and significantly affects classification performance. While the individual effects of these two sections are well-studied, the effects of cylinder-to-cone ratio (CCR) remain insufficiently explored. This study utilizes numerical simulations to assess the effects of different CCRs on hydrocyclone performance metrics, including classification performance, flow field characteristics, and volume fraction distributions across seven CCR configurations. The results show that as CCR increases from 1:9 to 9:1, the cut size increases from 16.4 mu m to 30.4 mu m, Ecart probable increases from 6.1 mu m to 9.5 mu m, the pressure drop decreases by 11 kPa, and the water split drops from 5.8% to 3.7%. Additionally, a smaller CCR enhances tangential velocity and pressure gradient, improves particle classification, stabilizes the air core, and reduces particle misplacement. These findings offer valuable insights into optimizing hydrocyclone design and classification performance to meet diverse application needs.

Novel hydrocyclone inlet designs can improve the specific performance objectives, however, the overall performance improvement, comprehensively considering multiple key objectives, is not clearly investigated yet. This study proposes an integrated optimization approach that combines response surface methodology (RSM) and multi-objective optimization (MOO) to optimize four key performance objectives for a traditional inlet (TI) and two novel designs: the tangent circle inlet (TCI) and the tapered spiral inlet (TSI). The well validated RSM establishes the relationship between inlet variables and objectives. The MOO method generates the Paretooptimal set to capture the trade-offs among these objectives and identifies the optimal solution for overall separation performance. Results indicate that each inlet type is optimally suitable for different separation scenarios: TSI prioritizes separation performance over energy consumption, TCI maintains moderate separation performance and energy consumption, and TI minimizes energy consumption with compromises in separation performance. This study offers valuable insights into hydrocyclones optimization.

Previous hydrocyclone optimizations often overlooked crucial objectives interactions, thereby weakening the overall system performance. This study presents a framework that integrates meta-heuristic algorithms with preference-informed decision-making to simultaneously optimize key performance objectives. Meta-heuristics identify comprehensive Pareto-optimal sets, while preference-informed decision-making evaluates each solution's overall separation performance according to specific separation preferences. Supported by computational fluid dynamics, the study quantifies the trade-off between optimal overall separation performance and pressure drop, enabling the attainment of optimal overall separation performance at any pressure drop within the Paretooptimal set. Among the evaluated algorithms, the strength Pareto evolutionary algorithm 2 (SPEA2) stands out for its exceptional diversity and convergence. With this framework, the study circumvents excessive compromises on neglected but crucial objectives, especially highlighting the significant adverse effects of overlooking

Previous hydrocyclone optimization often neglected interactions among key performance objectives, which limits hydrocyclone wide applications to meet increasingly diverse industry demands. This study proposes an optimization framework to identify the most suitable hydrocyclone design and operating conditions with conflicting key performance objectives. An advanced multi-objective evolutionary algorithm (PICEA-g) is employed to generate Pareto-optimal solutions that capture trade-offs among multiple conflicting objectives. A novel datadriven predictive algorithm, INFO-ELM, is introduced to establish nonlinear relationships between key variables and performance objectives, thereby accelerating the search for Pareto-optimal solutions by PICEA-g. Furthermore, a multi-criteria decision-making method (TOPSIS) is utilized to determine the optimal solution based on decision-makers' preferences, ensuring consistency between preference information and decision outcomes. The framework's effectiveness is validated across various separation scenarios using two decision-making strategies. This study offers a comprehensive approach to address trade-offs in hydrocyclone optimization, widening its application in diverse separation scenarios.

Optimizing hydrocyclone inlet design is regarded as an effective strategy to mitigate the adverse effect of particle misplacement and improves separation efficiency. This work proposes innovative hydrocyclone designs based on spiral inlet with a specific spiral angle and tangential inlet with a specific curvature radius. The new designs are evaluated and compared with a standard design using a validated two–fluid model. The separation performance, flow characteristics and volume fraction distribution are considered in the evaluation. An optimum spiral inlet design is identified, with an inlet spiral angle of 90° under the current conditions. This inlet design evidently improves the tangential velocity, strengthens the stability of the air core and reduces short-circuit flows. Additionally, the new inlets help improve the volume fraction of coarse particles in the region near the spigot, mitigating the misplacement of coarse and fine particles. This study offers a new perspective for improving hydrocyclone flows and performance.

Accelerating the prediction time of separation performance and flow field characteristics in industrial hydrocyclones holds paramount importance for real-time control. Machine learning methods exhibit significant advantages in this particular aspect. This study presents a network model that integrates a long short-term memory (LSTM) layer with a fully connected layer to accurately forecast the flow field and separation performance under various operational conditions, leveraging CFD data. The study evaluates the effect of factors such as the number of LSTM layers and neurons per layer on the model's performance, aiming to identify the optimal network structure. The results demonstrate that the predicted flow field and performance of the hydrocyclones using LSTM closely align with the outcomes from CFD simulations. The average absolute percentage error is constrained to within 6%, which significantly enhances the prediction efficiency. The findings contribute to a more efficient and automated separation process in industrial environments.

Balancing the requirements for thermal comfort and indoor air quality while minimizing energy consumption is a challenging trade-off problem for an indoor ventilation system. Multi-criteria decision-making (MCDM) technology is commonly employed to address this issue in response to changing outdoor weather conditions. However, conflicting ventilation performances, including thermal comfort and energy consumption, introduce complexities in identifying optimal ventilation operation parameters. This study therefore proposes an integrated multi-objective optimization and preference-based decision-making model to surmount these challenges. The former replaces the original alternatives, comprising all ventilation parameter combinations in the decision matrix, with Pareto-optimal sets, achieving improved trade-offs among all objectives without compromising individual performance. It mitigates undue influences on decision outcomes from inappropriate alternatives of ventilation parameter combinations when solely relying on MCDM techniques. Furthermore, this method maintains performance metrics with established standards in recommended thresholds, only compromising on the objectives without predefined criteria. This not only reduces the algorithm's complexity but also contributes to energy conservation. To expedite iterative processes, the proposed method incorporates the response surface methodology for predicting ventilation performances based on experimentally validated computational fluid dynamics simulations. Compared to the typical MCDM method, the proposed method has reduced energy consumption by up to 16.99% on average while still meeting thermal comfort and indoor air quality requirements. Additionally, this study identifies the optimal return vent height and operation modes that align with diverse decision preferences amidst changing weather conditions. The findings provide insights into accurately designing and controlling ventilation parameters in impinging jet ventilation, contributing to the practical applications across various ventilation scenarios.

Floor-standing air conditioners (FSAC) have been widely used in civil and office buildings due to the advantages of high cooling/heating capacity and easy installation. The major challenge of FSAC is to lower energy consumption while meeting the requirements of thermal comfort (TC) and indoor air quality (IAQ). Using the validated computational fluid dynamics (CFD) method, this study extensively examines the effects of some key operation parameters on the FSAC performance. The operation parameters include controllable supply vane angle, supply air velocity, supply air temperature, and uncontrollable outdoor weather condition. The main ventilation performance is assessed by indices such as predicted mean vote (PMV), predicted percent dissatisfied (PPD), draught rate (DR), local mean age of air (LMAA), and energy consumption (Psystem). The relative importance of the operation parameters for each ventilation performance index is identified by evaluating the individual effects of the operation parameters on such performance. Comparison of the individual and combined effects of the operation parameters on the ventilation performance reveals that the corresponding most important operation parameters account for 90.6%, 97.4%, 77.1%, 95.1%, and 79.2% of the variations in PMV, PPD, DR, LMAA and Psystem caused by the combined effects, respectively. Additionally, the outdoor weather conditions do not significantly affect DR, PPD and LMAA, but can shift PMV to uncomfortable levels. Finally, the constant-air-volume (CAV) system is recommended for FSAC during the steady stage and a simplified operation strategy is proposed.