Search Results (151)

Recently, microgrid (MG) structures include a mix of renewable energy sources (RES) and conventional sources. At high levels of RES penetration, reduced inertia and frequency stability have been confirmed in several studies. Properly designed and structured load frequency control (LFC) and virtual inertia control (VIC) are feasible solutions to these problems. In this paper, a new hybridized two-degree-of-freedom (2DOF) non-integer controller is proposed for multi-generation, multi-area MGs’ frequency regulation. The proposed new LFC is based on a 2DOF tilt-integral/tilt-derivative-double-derivative controller with a filter (TI-TD²F²). Meanwhile, the proposed design process considers coordinating capacitive energy storage (CES) to help regulate frequency deviation, as well as the high penetration of RESs (wind and PV). The incorporation of CES participation in frequency regulation helps provide fast VIC for the studied multi-MG system. Furthermore, an Enhanced Escape Algorithm (EESC) optimization algorithm is proposed to simultaneously optimize the control parameter set of the two-area MG system. The proposed EESC optimization algorithm identifies appropriate parameters for controller design, yielding better overall dynamic performance. An enhanced Escape Algorithm (EESC) is based on boosting the searching mechanism of the conventional Escape Algorithm by the integration of three modifications, including the Chaos map logistic mutation mechanism, the Fitness distance balance mechanism, and the Sorted Quasi-oppositional based learning (SQOBL). The proposed 2DOF TI-TD²F² controller demonstrates improved frequency stability and sustainable operation under load changes, variation in RESs, and other uncertainties of system parameters. The obtained results showed that the proposed EESC optimization algorithm adjusts the parameters of the TI-TD²F² controller, which significantly improves the dynamic performance in load frequency and tie-line power control. Compared to traditional TID and FOPID controllers, TI-TD²F² achieves up to a 70–80% reduction in tie-line power deviation and up to 60% faster settling time in many scenarios, demonstrating better robustness, faster response, and better overall system stability. Full article

To address three major industry pain points—the lack of quantified profit-sharing standards in the two-stage pricing under the separation model of military research and production, the absence of stable Nash equilibrium in single-layer synchronous optimization, and insufficient incentives for full-cycle process optimization in design units—this paper constructs a two-level Stackelberg leader–follower game model with the general contracting unit as the leader and design and general contracting units as followers. This aligns with current prototype incentives and phased pricing policies for production rewards and penalties. At the theoretical level, it improves the complete proof system for the two-stage concave profit two-level Stackelberg Nash equilibrium, distinguishes the mathematical differences in equilibrium existence between sequential decision-making and synchronous optimization, and extracts general rules for phased differentiated profit sharing: high-innovation segments should be allocated more profit weight; simply maximizing total alliance profit may cause imbalanced interests, while introducing a minimum net profit-weighted objective can achieve Pareto improvements without profit loss. This conclusion can be applied to multi-stage general contracting scenarios across industries, such as EPC and military–civil collaborative innovation, enriching the basic theory of profit sharing and hierarchical games. Theoretically, the existence of the lower-level Nash equilibrium is proven using Brouwer’s fixed-point theorem, and combining it with the strictly monotonically decreasing feature of the best response function, uniqueness of the equilibrium is derived. Multiple sets of differentiated initial values are simulated to rule out multi-equilibrium bifurcation risk. The model incorporates the military’s reward and penalty policies as rigid exogenous constraints, sets dual individual rationality constraints of ‘cooperative profit greater than baseline profit with no allocation, and both parties’ net profit non-negative,’ and introduces differentiated cost-reduction efficiency and quadratic increasing effort costs to characterize the heterogeneous input of the two types of development entities. For models with piecewise nonlinearity and multi-constraint nonconvex structures, this paper modifies the standard PSO into a Bi-PSO solving framework through hierarchical temporal adaptation. It does not innovate the underlying particle update mechanism and is only used to match the sequential decision order of the leader–follower game. By comparing five algorithms—IPM, GA, SA, DE, and adaptive PSO—through 20 repeated simulations: gradient-based interior point methods easily get stuck in locally invalid solutions that violate cooperation thresholds; differential evolution has the best numerical global search performance, but all general evolutionary algorithms optimize allocation and effort variables simultaneously, disrupting the Stackelberg hierarchical timing. Only Bi-PSO maintains consistent game logic. Using a pricing case for a certain type of equipment and jointly calibrating all parameters with policy documents, three simulation scenarios were set up: no allocation, equal 50/50 split, and single-layer profit maximization. Under the no-allocation mode, R&D investment from the design unit drops to zero and alliance benefits plummet; a blanket equal split ignores differences in technical contributions across two stages, leading to clear efficiency losses; single-layer optimization only pursues total profit maximization, causing a severe imbalance in profit distribution. The two-layer basic framework can achieve the upper limit of alliance benefits, and by adding a weighted optimization goal that considers both total profit and cooperation fairness, it can achieve equal net profits for both parties without reducing overall profit. Through single-parameter sweeps and two-factor heatmap simulations, the study further revealed the coupled effects of main party efficiency and mass production rewards and penalties on equilibrium input and optimal sharing ranges. A robust check was performed by replacing the logarithmic concave output function, producing a standardized allocation range resilient to parameter perturbations: optimal split for the prototype stage is 0.4–0.6 for the design unit, and for mass production stage 0.7–0.9. The findings suggest that high-contribution stages in multi-phase collaboration contracts should receive more benefits, and a weighted fairness objective can achieve Pareto improvements. These conclusions can extend to multi-stage collaboration scenarios such as EPC and military–civilian cooperation. Theoretically, this research further completes the equilibrium proof system for two-party concave payoff two-layer games, providing a new reference for the theory of phased differentiated benefit-sharing contracts in the military sector. Methodologically, it proposes a two-layer intelligent solving tool adapted to leader–follower sequential decisions, effectively mitigating issues where single-layer model equilibria fail or analytical algorithms struggle with multi-constraint nonconvex games. The results can provide quantitative support for the military, general contracting unit, and design unit in drafting equipment incentive pricing contracts and managing full-cycle cost collaboration. Full article

This study investigates configuration resilience in emergency medical rescue networks under disaster-induced compound disruptions. Major disasters can simultaneously intensify casualty severity, disrupt road networks, restrict response access, and increase hospital surge pressure, turning emergency preparedness into a location–capacity–transfer planning problem under uncertainty. We formulate a two-stage scenario-based mixed-integer programming model that determines the locations and capacity levels of temporary rescue sites and emergency medical facilities and then optimizes scenario-adaptive casualty transfers among affected areas, rescue sites, emergency facilities, and hospitals. The model considers 81 compound disruption scenarios defined by casualty severity, road-network disruption, response-access constraints, and hospital surge pressure. To solve large-scale instances, we develop a small-sample active-learning-assisted variable neighborhood search algorithm (AL-VNS), which combines a committee random forest surrogate with a progressive active-verification strategy to prioritize limited exact CPLEX evaluations, while keeping all accepted and reported solutions exactly evaluated. Numerical experiments show that AL-VNS achieves near-optimal performance in small- and medium-scale instances. Based on three independent runs for each large-scale instance, AL-VNS reduces average runtime by 34.71–63.73% relative to baseline variable neighborhood search (BVNS), while maintaining average objective-value differences of only 0.01–0.12% and requiring approximately 217 exact evaluations per instance. In the Ya’an case and the tested sensitivity settings, temporary rescue sites provide a relatively stable spatial triage-and-transfer backbone, whereas emergency medical facilities offer flexible surge capacity for relieving hospital pressure. The findings support resilience-oriented location and capacity planning for emergency medical rescue networks under uncertain compound disruptions. Full article

Background/Objective: Status epilepticus (SE) is a life-threatening condition that requires immediate response to effectively control. Although benzodiazepines are the first-line treatment against SE, when treatment is delayed, benzodiazepine pharmacoresistance develops. In preclinical models of benzodiazepine refractory SE, the addition of antiseizure medications (ASMs) as adjunct to midazolam to reduce neuronal excitability and enhance inhibitory function is essential to protect against the neurodegeneration and epileptogenesis that follows prolonged seizure. Brivaracetam is a recently FDA-approved ASM to treat partial onset seizures in pediatric and adult patients as a monotherapy or adjunct therapy. We evaluated the potential of brivaracetam as monotherapy or in combination with midazolam and ketamine for efficacy against organophosphorus nerve agent (OPNA)-induced refractory SE in rats. Methods: Adult male rats were exposed to a seizure-inducing dose of soman and treated with atropine sulfate and the oxime asoxime chloride one minute after soman exposure and with brivaracetam alone or in combination with midazolam and ketamine 40 min after seizure onset. Multiple metrics of protection such as seizure severity, spontaneous recurrent seizure (SRS), neuronal loss, and neuroinflammation were evaluated. Results: Although brivaracetam monotherapy resulted in 100% survival, protection from the development of SRS and neurodegeneration only occurred when brivaracetam was administered as an adjunct to ketamine and midazolam. Initial seizure severity was also reduced by the combination of brivaracetam–midazolam–ketamine over monotherapy. Conclusions: Although further research is needed to determine optimal drug combinations, these preclinical findings provided further evidence that simultaneous polytherapy with ASMs improves OPNA-induced seizure outcomes. Full article

This study systematically investigated the hydrothermal liquefaction (HTL) of Chlorella vulgaris to understand how operating conditions influence bio-crude oil yield, nitrogen behavior, fuel properties, and compositional changes. A Box–Behnken design (BBD) was used, with temperature (200–300 °C), pressure (50–200 bar), and reaction time (15–45 min) as the process variables. Bio-crude oil yields ranged from 11.10 to 40.79 wt% (dry basis), and nitrogen content varied between 2.34 and 6.22 wt%. Temperature was the primary factor affecting both bio-crude oil yield and nitrogen content. Higher temperatures increasedbio-crude oil production but also led to more nitrogen incorporating into the oil phase, indicating a trade-off between yield and nitrogen retention. Van Krevelen analysis showed that HTL bio-crude had lower O/C ratios than the raw microalgal feedstock, suggesting progressive deoxygenation under more severe HTL conditions. GC–MS analysis revealed that hydrocarbons increased and fatty acids decreased with rising temperature. Nitrogen-containing compounds such as amides and N-heterocyclic compounds, were still detectable at high temperatures. Multi-response optimization identified 300 °C, 200 bar, and 15 min as the optimal conditions, balancing maximized bio-crude oil yield with minimized nitrogen content. These results suggest that HTL bio-crude oil is regarded as an intermediate feedstock requiring further upgrading. Therefore, optimizing the HTL process should simultaneously consider both fuel-related properties and nitrogen behavior. Full article

The persistent accumulation of tetracycline (TC) antibiotics in aquatic environments poses severe ecological and public health risks, necessitating the development of multifunctional platforms capable of simultaneous detection and degradation. Herein, we report magnetically recoverable plasmonic Fe₃O₄/Cu₂O-Ag nanocomposites (NCs) that integrate visible-light-driven photocatalysis with ultrasensitive surface-enhanced Raman scattering (SERS) detection. Hierarchical flower-like Fe₃O₄ nanocrystals were employed as magnetic supports, followed by in situ growth of Cu₂O nanocrystals and controlled deposition of Ag nanocrystals. The optimized composite (FCA-2) exhibited enhanced visible-light absorption (E_g = 1.86 eV), suppressed electron–hole recombination, and improved photocurrent response, which were attributed to Schottky barrier formation at the Cu₂O-Ag interface and localized surface plasmon resonance (LSPR) effects. Under simulated solar irradiation, FCA-2 NCs achieved 91.79% degradation of TC within 60 min, following pseudo-first-order kinetics (k = 20.37 × 10⁻³ min⁻¹). Finite-difference time-domain (FDTD) simulations revealed that optimal Ag loading maximized plasmonic “hot spot” density, thereby enhancing electromagnetic field intensity and SERS performance. The FCA-2 substrate enabled ultrasensitive TC detection with a limit of detection of as low as 10⁻¹⁰ M. Moreover, the superparamagnetic Fe₃O₄ core allowed for rapid magnetic separation and sustained performance over multiple SERS–photocatalysis cycles, with negligible signal attenuation after 30 days. This work presents a rational strategy for constructing plasmonic magnetic NCs that synergistically couple photocatalytic remediation, ultrasensitive sensing, and magnetic recyclability, offering significant potential for integrated environmental monitoring and sustainable water treatment applications. Full article

With the widespread integration of renewable energy, microgrid environmental economic dispatch (EED) faces challenges such as uncertainties in wind and solar power outputs and multi-objective conflicts. This paper proposes a stochastic expected dispatch framework based on an improved multi-objective dung beetle optimization algorithm (MO-CLDBO). First, considering both wind–solar uncertainties and demand response, a Gaussian Copula function is employed to characterize the 24-h temporal correlations among wind speed, solar irradiance, and load, and typical scenarios are generated via Monte Carlo sampling and simultaneous backward reduction; a time-of-use demand response model is also introduced. Second, taking expected operational cost and environmental emission as dual objectives, three improvements are proposed to address the issues of uneven initial population, easy local convergence, and Pareto front collapse in the standard dung beetle algorithm: a Folded Two-Dimensional Modified Coupled Logistic-Sine Map (Folded 2D-MCLSM) is used to initialize a high-quality population, a non-dominated sorting mechanism is introduced, and a dynamic lens imaging backward learning strategy is designed. Finally, the proposed algorithm is compared with several classical algorithms in the mathematical model of microgrid optimal dispatch through 50 independent runs. Experimental results show that the improved dung beetle optimization algorithm achieves not only the lowest average operating cost, but also the best hypervolume (HV) indicator, demonstrating excellent comprehensive performance in multi-objective search convergence and solution set diversity. Full article

The accelerating integration of renewable energy sources into modern power grids has created unprecedented operational challenges, with significant system cost volatility under extreme uncertainty events. To address this challenge, this paper presents a risk-aware stochastic approximate dynamic programming (SADP) algorithm based on machine learning and parallel computing architectures. The algorithm learns optimal coordination strategies for source-grid-load-storage resources while explicitly quantifying and mitigating tail risk events that conventional approaches overlook. First, a risk-averse stochastic optimization model is constructed, which captures the complex interdependencies between renewable generation uncertainty, demand variability, and flexible resource coordination through second-order cone programming formulations. This model integrates the GlueVaR (Glued Value-at-Risk) metric, enabling simultaneous optimization across multiple risk horizons with adjustable conservatism parameters. Second, to solve the established model efficiently, an SADP algorithm based on risk-averse approximate value functions (RAVFs) is proposed, in which the training process of the RAVFs employs machine learning principles to directly encode risk preferences into operational decisions. By integrating GlueVaR into offline training across 5000 probabilistically weighted scenarios, the algorithm discovers emergent coordination patterns between distributed resources, which are rarely identified by human operators. Third, a large-scale parallel computing architecture is implemented for the SADP algorithm. This architecture decomposes the multi-period optimization problem into single-period coordinated sub-problems. During offline training, parallel computing of a series of single-period sub-problems can be performed across all probabilistic scenarios, significantly reducing training time. Extensive validation on both the modified IEEE 33-bus and 69-bus systems with integrated wind turbines, photovoltaic plants, energy storage systems, and demand response capabilities demonstrates remarkable performance improvements. Convergence analysis reveals that the AVFs stabilize within 30 training iterations, achieving sub-160 s solution times in online application even for complex networks with heterogeneous resources. By enabling real-time risk-aware decision-making under severe uncertainty, the proposed method provides grid operators with actionable strategies that balance economic efficiency and operational resilience. Full article

Cadmium (Cd) contamination in agricultural soils poses a severe threat to food safety and human health through the food chain. This study investigated the efficacy of tobacco straw-derived biochar, applied at varying rates (0%, 1%, 2%, and 5% w/w), in mitigating Cd accumulation and modulating the growth and nutritional quality of two amaranth (Amaranthus tricolor L.) cultivars (red and green) grown in Cd-contaminated soil (initial total Cd of 2.18 mg/kg). The pot experiment revealed that biochar significantly reduced Cd uptake in both cultivars. Mechanistically, biochar elevated soil pH and drove the in-situ transformation of highly bioavailable exchangeable Cd into the more stable Fe-Mn oxide-bound fraction. Consequently, shoot Cd concentrations were notably suppressed, with the red cultivar exhibiting a superior response; the 2% biochar treatment optimally reduced its shoot Cd concentration by 37.6% compared to the control. Crucially, the amendments induced highly cultivar-specific growth responses. While biochar application simultaneously mitigated Cd toxicity and promoted biomass accumulation in red amaranth (yielding a 58.6% increase in shoot dry weight at the 2% rate), it exerted antagonistic, inhibitory effects on the growth of green amaranth. In conclusion, the incorporation of 2% tobacco straw biochar serves as a highly effective, dual-purpose strategy for significantly reduced health risks and enhancing the yield of red amaranth in Cd-contaminated fields. However, in green amaranth, biochar application induced a physiological trade-off, inhibiting growth despite successful Cd reduction. Furthermore, while Cd concentrations were significantly reduced on a dry-weight basis, future evaluations based on fresh-weight regulatory limits are required to fully confirm food safety. Full article

Mature thin oil reservoirs remain pivotal to maintaining reserves, sustaining production, and enhancing profitability due to their substantial annual output and untapped recovery potential. However, prolonged development leads to compromised fracturing efficacy, manifesting as severe formation-energy depletion, rapid production decline, and short effective periods of stimulation measures. Energy-storage fracturing technology addresses these challenges through fluid-injection energization and imbibition displacement, thereby replenishing formation energy and mobilizing residual oil. Leveraging a geo-engineering integrated platform, this study establishes an inverted seven-spot well-pattern energization model to systematically investigate pore pressure–stress field evolution and dynamic responses under varying energization parameters, including energy-storage injection rate, energy-storage volume, and energy-storage sequence. Key findings include: (1) increasing the energy-storage injection rate from 1.5 m³/min to 3.5 m³/min elevates average pore pressure by 7.8 MPa, with minimum and maximum horizontal principal stresses increasing by 1.4 MPa and 1.7 MPa, respectively; (2) raising the energy-storage volume from 2800 m³ to 4200 m³ enhances pore pressure by 5.5 MPa, accompanied by 2.5 MPa and 2.6 MPa increments in minimum and maximum horizontal principal stresses; (3) simultaneous energizing of all injection wells (1–6) is identified as the optimal injection sequence, yielding the highest average pore pressure of 40.3 MPa at equivalent monitoring positions within the well group, with corresponding average minimum and maximum horizontal principal stresses of 55.3 MPa and 60.3 MPa, respectively. The results provide theoretical and technical support for optimizing energy-storage fracturing strategies in mature thin oil reservoirs. Full article

Uridine diphosphate glucuronosyltransferase 2B (UGT2B) enzymes constitute a critical subgroup of phase II metabolizing enzymes that modulate the clearance of steroid hormones, carcinogens, and numerous anticancer agents, thereby influencing cancer susceptibility, progression, and therapeutic outcomes. This review provides a comprehensive synthesis of the genetic, regulatory, and functional roles of UGT2B family members, particularly UGT2B4, UGT2B7, UGT2B10, UGT2B15, UGT2B17, and UGT2B28, in oncogenesis and cancer treatment. We summarize evidence from molecular, epidemiological, pharmacogenetic, and clinical studies demonstrating how UGT2B expression patterns, polymorphisms, copy number variations, epigenetic regulation, and microRNA-mediated control shape intratumoral hormone homeostasis, carcinogen detoxification, and drug resistance across multiple malignancies, including prostate, breast, lung, colorectal, hematological, and hormone-dependent cancers. UGT2B enzymes metabolize several widely used anticancer drugs and active metabolites, thereby affecting pharmacokinetics, efficacy, and toxicity. Understanding the context-specific roles of UGT2B family members offers a compelling opportunity for therapeutic exploitation. In particular, rational combination strategies incorporating UGT2B inhibitors or modulators alongside standard anticancer agents may enhance drug effectiveness without increasing dosage, while simultaneously enabling the dose reduction of the partner agent to mitigate dose-dependent toxicities. Such approaches are especially relevant for therapies with narrow therapeutic indices. Overall, this review highlights UGT2B enzymes as multifunctional determinants of cancer risk and treatment response and underscores their promise as biomarkers and actionable targets for precision oncology and optimized combination regimens. Full article

Photoacoustic spectroscopy (PAS) is a highly sensitive and non-destructive technique widely used for trace gas detection; however, the simultaneous quantification of methane (CH₄), ethane (C₂H₆), and ethylene (C₂H₄) remains challenging due to severe spectral cross-interference and non-linear responses across broad concentration ranges. In this work, we propose a high-precision, end-to-end detection framework based on a Deep Kernel Extreme Learning Machine (DKELM) optimized using a Mutation–Chaotic Particle Swarm Optimization (MCPSO) algorithm. To enhance diagnostic information in the photoacoustic signals, a multi-scale wavelet transform based on a db4 wavelet basis with 5-layer decomposition and a Heursure soft threshold strategy is first employed for denoising and enhancing absorption features. To address the hyperparameter sensitivity and local-optimum trapping inherent in deep models, the MCPSO algorithm integrates hybrid chaotic initialization, adaptive mutation probability control, Cauchy-based perturbation, temperature-controlled mutation amplitude, and elite-guided population updating. The proposed MCPSO-DKELM model is evaluated on an expanded dataset of 470 mixed-gas spectra and benchmarked against other frameworks, including the previously reported SVM-CPSO-KELM architecture. The experimental results demonstrate that MCPSO-DKELM achieves stable, segmentation-free quantification across the full dynamic range, with an average detection error below 3.5% and the maximum relative error constrained to under 15%, which represents a substantial improvement over existing approaches. Thus, the combination of deep kernel feature extraction and mutation–chaotic global optimization provides a robust and reliable solution for simultaneous multi-component hydrocarbon gas analysis in complex industrial environments. Full article

The Constrained Application Protocol (CoAP) is widely adopted to ensure end-to-end reliability in resource-constrained Artificial Intelligence of Things (AIoT) and Wireless Sensor Networks (WSNs). However, CoAP’s default retransmission timeout (RTO) mechanism lacks algorithmic responsiveness under volatile channel conditions, and state-of-the-art benchmarks like CoCoA+ and FASOR often suffer from over-conservative backoff states or destabilizing retransmission storms. To overcome these operational bottlenecks, this paper proposes a novel dual-adaptive Dynamic RTO algorithm specifically engineered for heterogeneous IoT deployment scales. The proposed framework dynamically adjusts its parameter inspection cycle (N) based on instantaneous round-trip time (RTT) variance while simultaneously scaling its tuning coefficient (α) in response to real-time packet loss indicators. To rigorously validate the algorithmic resilience, performance evaluations were conducted within a highly volatile network environment governed by the Gilbert–Elliott dynamic loss model across multi-hop linear (1 × 6) and grid (3 × 6, 5 × 6) topologies. Experimental results demonstrate that the proposed Dynamic RTO consistently optimizes the throughput–latency trade-off, achieving a total communication time of 25.92 s in complex grids—outperforming CoCoA+ and FASOR by 14.28% and 8.89%, respectively. Furthermore, the proposed mechanism significantly curtails transmission overhead, restricting the cumulative retransmission footprint to just 59 counts under severe localized impairments, thereby establishing a scalable, resource-efficient, and empirically robust transport-layer solution for next-generation edge-computing infrastructures. Full article

The continuous rise in turbine inlet temperatures to maximize engine efficiency makes highly integrated composite cooling schemes essential, but their intricate thermal interactions pose formidable challenges for parameter optimization. In this study, an impingement–pin-fin–film configuration is extracted as a representative composite cooling unit from a double-wall blade and subjected to 3D steady-state RANS simulations under realistic engine conditions. The numerical results are then used to construct quadratic polynomial response surface surrogate models for multi-objective optimization. It is revealed that the blowing ratio dictates overall thermal performance primarily through internal cooling, and excessively high ratios weaken the film coverage. Geometrically, insufficient control over the spanwise ratio disrupts film coverage and breaks the continuity of internal cooling, thereby degrading both cooling effectiveness and structural thermal compatibility. Additionally, a critical region is located upstream of the film hole exit; the combination of an extremely thin solid wall and high heat transfer coefficients creates a localized over-cooled zone, severely constraining temperature uniformity. Ultimately, the optimization framework clarifies the coupled flow and heat transfer behaviors of the double-wall unit. It simultaneously maximizes area-averaged overall cooling effectiveness and temperature uniformity while minimizing coolant mass flow, revealing the key mechanism behind induced thermal stress concentrations. Full article

This study aims to optimize the performance of cyclone separators by simultaneously maximizing separation efficiency and minimizing pressure drop through a CFD-based multi-objective optimization framework. The research objectives are explicitly focused on (i) developing accurate predictive surrogate models for cyclone behavior and (ii) identifying optimal geometric configurations that balance both performance criteria. The methodology integrates Design of Experiments (DOE), Response Surface Methodology (RSM), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II), using a full factorial design of 25 simulations to construct fourth-order surrogate models. This formulation was strictly necessary to capture the severe non-linearities observed in preliminary CFD runs. These models exhibited high predictive capability, with coefficients of determination (R²) above 0.94. The NSGA-II optimization generated a Pareto-optimal front that clearly describes the trade-off between separation efficiency and pressure drop, enabling systematic decision-making. The selected optimal configuration achieved a separation efficiency of 94.5% and a pressure drop of 194.78 Pa. CFD validation confirmed the robustness of the surrogate models, with relative errors below 1% for efficiency and below 5% for pressure drop. Overall, the results demonstrate that integrating CFD, surrogate modeling, and evolutionary optimization provides a reliable and computationally efficient strategy for cyclone separator design optimization. Full article