Synthetic dyes play a crucial role in our daily lives, especially in clothing, leather accessories, and furniture manufacturing. Unfortunately, these potentially carcinogenic substances are significantly impacting our water systems due to their widespread use. Dyes from various sources pose a serious environmental threat owing to their persistence and toxicity. Regulations underscore the urgency in addressing this problem. In response to this challenge, metal oxide nanoparticles such as titanium dioxide (TiO2), zinc oxide (ZnO), and iron oxide (Fe3O4) have emerged as intriguing options for dye degradation due to their unique characteristics and production methods. This paper aims to explore the types of nanoparticles suitable for dye degradation, various synthesis methods, and the properties of nanoparticles. The study elaborates on the photocatalytic and adsorption-desorption activities of metal oxide nanoparticles, elucidating their role in dye degradation and their application potential. Factors influencing degradation, including nanoparticle properties and environmental conditions, are discussed. Furthermore, the paper provides relevant case studies, practical applications in water treatment, and effluent treatment specifically in the textile sector. Challenges such as agglomeration, toxicity concerns, and cost-effectiveness are acknowledged. Future advancements in nanomaterial synthesis, their integration with other materials, and their impact on environmental regulations are potential areas for development. In conclusion, metal oxide nanoparticles possess immense potential in reducing dye pollution, and further research and development are essential to define their role in long-term environmental management.
With the widespread deployment of photovoltaic (PV) power stations, timely identification and rectification of module defects are crucial for extending service life and preserving efficiency. PV arrays, subjected to severe outside circumstances, are prone to defects exacerbated by dust accumulation, potentially leading to complex compound faults. The resemblance between individual and compound faults sometimes leads to misclassification. To address this challenge, this paper presents a novel hybrid deep learning model, ResGRU, which integrates a residual network (ResNet) with bidirectional gated recurrent units (BiGRU) to improve fault diagnostic accuracy. Additionally, a Squeeze-and-Excitation (SE) module is incorporated to enhance relevant features while suppressing irrelevant ones, hence improving performance. To further optimize inter-class separability and intra-class compactness, a center loss function is employed as an auxiliary loss to enhance the model's discriminative capacity. This proposed method facilitates the automated extraction of fault features from I-V curves and accurate diagnosis of individual faults, partial shading scenarios, and compound faults under varying levels of dust accumulation, hence aiding in the formulation of efficient cleaning schedules. Experimental findings indicate that the suggested model achieves 99.94% accuracy on pristine data and 98.21% accuracy on noisy data, markedly surpassing established techniques such as artificial neural networks (ANN), ResNet, random forests (RF), multi-scale SE-ResNet, and other ResNet-based approaches. Thus, the model offers a reliable solution for accurate PV array fault diagnosis.