This paper presents a novel hybrid maximum power point tracking strategy for photovoltaic systems operating under stochastic weather conditions and load variations with partial measurements. The proposed approach integrates three complementary computational methods: Newton-Raphson-based nonlinear optimization for dynamic adaptive reference generation, model predictive control for computing the optimal duty cycle, and an extended Kalman filter to estimate all system states, including photovoltaic voltage, inductor current, and output voltage. Uncertainties in weather conditions and load are modeled as stochastic variations in solar irradiance, temperature, and equivalent load resistance, including abrupt changes due to load switching and cloud transients. These effects are represented using an improved Ornstein-Uhlenbeck jump-diffusion process, capturing both continuous fluctuations and sudden disturbances. Simulation results demonstrate high power extraction, with a tracking efficiency of 99.98%. A root mean square error of 0.0463 V is obtained for tracking the maximum power point voltage by comparing the reference voltage with the actual photovoltaic voltage, and 0.1198 V for voltage estimation by comparing the true and estimated values. Comparative results further show that the proposed approach outperforms classical methods in terms of tracking accuracy, convergence speed, and reduction of steady-state oscillations. Furthermore, statistical evaluation via Monte Carlo sampling confirms the robustness of the proposed strategy under varying operating conditions.
Approximate nearest-neighbor (ANN) indexes accelerate candidate retrieval, but comparisons at unrelated retrieval fidelity can produce misleading system choices. We evaluate exact Flat, HNSW, IVF-Flat, IVF-PQ, and Flat-PQ on four public interaction datasets and separate user-to-item from item-to-item retrieval. The CPU-only protocol calibrates tunable indexes against exact Flat at agreement-recall targets of 0.90, 0.95, and 0.98. A held-out-item rank proxy is reported separately from exact-neighbor fidelity. Because the archived pipeline learns item representations from the complete filtered interaction matrix before constructing leave-one-out queries, this proxy is transductive and is not an estimate of out-of-sample recommender quality. The study combines 40 primary conditions with query-level discordance, corrected paired inference over three nonredundant outcomes, exposure proxies, SVD-weighting sensitivity, and storage-and-fidelity synthetic scaling. HNSW and IVF-Flat reached all targets on the calibration samples, whereas the tested IVF-PQ configuration reached 0.90 in two of eight scenarios and never reached 0.95. Expanded-sample evaluation at the selected 0.95 setting retained the target in seven scenarios for HNSW and six for IVF-Flat, revealing finite-sample transfer uncertainty. HNSW and IVF-Flat changed the held-out item’s top-10 rank for only 0.03% and 0.11% of queries on average, compared with 5.23% for IVF-PQ and 6.01% for Flat-PQ. The corrected 20,000-resample analysis rejected 18 of 96 Holm-adjusted comparisons, predominantly for Recall@100; every rejection favored exact Flat. The results support constraint-first index selection in which the agreement threshold is derived from tolerated degradation in an application-specific downstream or online service metric, not treated as a universal technical constant.
Peroxymonosulfate (PMS)-based advanced oxidation processes are often limited by unstable activation and reliance on continuous mechanical stirring to sustain high photocatalytic performance during pollutant degradation. Herein, a ternary Co3O4-graphitic carbon nitride (GCN)-Bi2O3 dual S-scheme (CoCNBi) was successfully immobilized within calcium alginate (Alg) hydrogel beads (CoCNBi@Alg) via a 3D cross-linking strategy. The biopolymer structure aids dispersion/recovery; despite low BET surface area (3.9 m(2)/g), enhanced electroactive area and charge-transfer supported efficient degradation. CoCNBi@Alg beads showed excellent photo-electrochemical performance, achieving substantial mineralization of Sulfamethoxazole (SMX) (up to 76.3% total organic carbon (TOC) removal) under simulated sunlight without external stirring, outperforming bare GCN, Bi2O3, Co3O4, and non-immobilized CoCNBi powder. Additionally, CoCNBi@Alg demonstrated a strong performance under real-water matrices (>98% SMX degradation) and maintained similar to 91% efficiency after 10 cycles, with minimal metal leaching (Co: 0.05 mg/L; Bi: 0.03 mg/L). Mechanistically, the CoCNBi dual S-scheme heterojunction, coupled with Co/Bi redox cycling, facilitates effective e(-)/h(+) separation and transfer, thereby enhancing PMS activation. In this framework, the Alg matrix acts as a 3D stabilizing scaffold, ensuring better interfacial contact and streamlining mass transfer, thereby triggering synergistic non-radical (O-1(2)) and radical (SO4 center dot-, (OH)-O-center dot, O-2(center dot-)) oxidative species. Liquid chromatography-mass spectrometry (LC-MS) data reveal a complex degradation process with multiple routes, including the cleavage of the sulfonamide bond, hydroxylation, and ring opening, while the reduced toxicity of intermediates was confirmed by ECOSAR predictions. This study demonstrates that the encapsulation of dual S-scheme structures within Alg provides a dependable pathway for complete mineralization.
The widespread discharge of synthetic dyes into aquatic environments poses a significant threat to both ecological balance and human well-being, underscoring the urgent need for effective and sustainable treatment approaches. A composite material combining CoAl2O4 and CoS was prepared to remove Brilliant Green (BG). The new CoAl2O4@CoS photocatalyst was obtained through a hydrothermal synthesis. The structural and physicochemical properties of these materials were examined by XRD, FTIR, XPS, SEM-EDS, and UV-Vis DRS, which confirmed their crystalline phases, surface composition, oxidation states, and optical characteristics. Photocatalytic tests under simulated sunlight demonstrated that the 1 g/L of CoAl2O4@CoS composite could completely degrade a 20 mg/L BG solution within 70 min. Its apparent rate constant (k = 0.0434 min(-1)) was significantly higher than those of CoAl2O4 (k = 0.0059 min(-1)) and CoS (k = 0.0091 min(-1)). Radical scavenging experiments indicated that hydroxyl radicals (center dot OH) and superoxide anions (O-2(center dot-)) played dominant roles in the degradation reaction. The composite retained its efficiency after five consecutive cycles, confirming its stability and reusability. Density functional theory (DFT) calculations supported the experimental results, showing favorable band positions and orbital hybridization responsible for the enhanced sunlight response. Overall, these findings demonstrate that CoAl2O4@CoS is a promising photocatalyst for solar-assisted wastewater purification.
Accurate extraction of photovoltaic model parameters is essential for optimal system design and performance prediction. This study introduces a hybrid methodology combining analytical techniques with the Blood-Sucking Leech Optimizer to estimate the five parameters of the single-diode model and the seven parameters of the double-diode model. The analytical stage provides reliable initial estimates for the photo-generated current, reverse saturation current, and shunt resistance using three characteristic points on the I–V curve, while the Blood-Sucking Leech Optimizer optimizes the remaining nonlinear parameters including the ideality factor and series resistance. By decoupling the analytical and optimization phases, the hybrid approach reduces the dimensionality of the search space, enhancing convergence speed and numerical stability. Experimental validation was performed under a wide range of irradiance and temperature conditions using the RTC France solar cell, Photowatt PWP201, PVM-752 GaAs, and STP6-120/36 PV module, demonstrating robust performance, low root mean square error values, and high fidelity in reproducing experimental I–V characteristics. The proposed Analytical Blood-Sucking Leech Optimizer achieved superior accuracy with minimum RMSE values of 7.81 × 10−4, 2.15 × 10−3, 3.15 × 10−4, and 1.53 × 10−2 respectively, outperforming conventional Blood-Sucking Leech Optimizer, Genetic Algorithm based on non-uniform mutation, Golf Optimization Algorithm, and Ant Lion Optimizer. Statistical analysis confirmed improved robustness, faster convergence, and lower computational cost. These results demonstrate that Analytical Blood-Sucking Leech Optimizer provides an accurate, stable, and practical solution for photovoltaic parameter extraction and photovoltaic system modeling.
