A high-efficient and salt-rejecting 2D film for photothermal evaporation

Solar desalination system for fresh water production performance estimation in net-zero energy consumption building: A comparative study on various machine learning models

This study employs diverse machine learning models, including classic artificial neural network (ANN), hybrid ANN models, and the imperialist competitive algorithm and emotional artificial neural network (EANN), to predict crucial parameters such as fresh water production and vapor temperatures. Evaluation metrics reveal the integrated ANN-ICA model outperforms the classic ANN, achieving a remarkable 20% reduction in mean squared error (MSE). The emotional artificial neural network (EANN) demonstrates superior accuracy, attaining an impressive 99% coefficient of determination (R2) in predicting freshwater production and vapor temperatures. The comprehensive comparative analysis extends to environmental assessments, displaying the solar desalination system's compatibility with renewable energy sources. Results highlight the potential for the proposed system to conserve water resources and reduce environmental impact, with a substantial decrease in total dissolved solids (TDS) from over 6,000 ppm to below 50 ppm. The findings underscore the efficacy of machine learning models in optimizing solar-driven desalination systems, providing valuable insights into their capabilities for addressing water scarcity challenges and contributing to the global shift toward sustainable and environmentally friendly water production methods.

Rational design and easy fabrication of transparent photothermal/hygroscopic composite coatings with long-lasting antifogging performance under sunlight activation

Hygroscopic polymers are good candidates for antifogging coatings, but their long-term effectiveness is limited by the equilibrium between water absorption and expansion. As an efficient and environmentally friendly solution, photothermal materials are being introduced into the field of antifogging. However, there is a need for enhancement in the spectral characteristics of most photothermal materials within the visible light region. In addition, photothermal antifogging coatings often exhibit a delay in heating response, which hinders their ability to promptly evaporate condensed water droplets in the absence of illumination or during initial illumination. Here, a bilayer structure design of photothermal nanomaterials/hygroscopic polymers is proposed to achieve long-term antifogging under sunlight activation. Ensuring the rapid absorption of condensed water droplets on the coating surface, while simultaneously achieving efficient photothermal conversion for a swift temperature increase over the entire coating, is key to this approach, which will not only suppress early fogging but also lead to an exponential decrease of the nucleation rate of droplets. During this process, a dynamic equilibrium is gradually established between the condensation and evaporation of fog droplets, leading to long-term antifogging properties. The light transmittance of the composite coatings reaches as high as ca. 75% in the visible light region, making them well suited for a diverse range of transparent substrate and device applications. A clear field of view can be maintained for at least 6 h under 1 sun illumination above 65 °C hot steam. The antifogging/defogging performance is effectively demonstrated even under challenging non-ideal natural conditions, such as low solar irradiation during dusk or when placed indoors behind windows.

Optimization and intelligent power management control for an autonomous hybrid wind turbine photovoltaic diesel generator with batteries

In this paper, a critical issue related to power management control in autonomous hybrid systems is presented. Specifically, challenges in optimizing the performance of energy sources and backup systems are proposed, especially under conditions of heavy loads or low renewable energy output. The problem lies in the need for an efficient control mechanism that can enhance power availability while protecting and extending the lifespan of the various power sources in the system. Furthermore, it is necessary to adapt the system's operations to variations in climatic conditions for sustained effectiveness. To address the identified problem. It is proposed the use of an intelligent power management control (IPMC) system employing fuzzy logic control (FLC). The IPMC is designed to optimize the performance of energy sources and backup systems. It aims to predict and adjust the system's operating processes based on variations in climatic conditions, providing a dynamic and adaptive control strategy. The integration of FLC is specifically emphasized for its effectiveness in balancing multiple power sources and ensuring a steady and secure operation of the system. The proposed IPMC with FLC offers several advantages over existing strategies. Firstly, it showcases enhanced power availability, particularly under challenging conditions such as heavy loads or low renewable energy output. Secondly, the system protects and extends the lifespan of the power sources, contributing to long-term sustainability. The dynamic adaptation to climatic variations adds a layer of resilience to the system, making it well-suited for diverse geographical and climatic conditions. The use of realistic data and simulations in MATLAB/Simulink, along with real-time findings from the RT-LAB simulator, indicates the reliability and practical applicability of the proposed IPMC strategy. Efficient load supply and preserved batteries further underscore the benefits of the fuzzy logic-based control strategy in achieving a well-balanced and secure system operation.