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This paper proposes the Hybrid desalination and power generation through parabolic trough collectors. This study aims to examine the feasibility, performance optimization, and economic viability of power-generation plant and hybrid-desalination using multi-stage flash (MSF) and reverse osmosis (RO) technologies assessed by using the parabolic trough collectors (PTC). The objective is to discover the posibilities of this innovative approach in addressing water scarcity and clean energy demands. The studies usually focus on several key aspects. Firstly, it resolves the technical feasibility of integrating MSF and RO technologies with parabolic-trough-collectors to optimize energy efficiency and freshwater production. This involves evaluating the system design, heat transfer mechanisms, and operational parameters.Secondly, performance optimization is a key area of investigation. By optimizing these parameters the aim is to maximize freshwater production while minimizing energy consumption. Thirdly, the economic viability of the hybrid plant will be assessed. This involves considering capital investment, conducting a comprehensive cost analysis, maintenance costs, and operational expenses. The study also evaluates the potential for revenue generation through the sale of excess electricity produced by the system. The potential outcomes of this research include demonstrating the technical-feasibility as well as economic-viability of the hybrid desalination and power generation plant. The findings may indicate that this approach can effectively address water scarcity challenges while contributing to clean energy production. The study may also highlight the environmental benefits of integrating renewable energy sources (RESs) and decreasing greenhouse gas emissions compared to traditional desalination methods.The proposed method is executed in MATLAB platform and compared to various existing approaches. [ABSTRACT FROM AUTHOR]

In today's smart communities, small-scale energy systems are essential for sustainable development and efficient resource management. However, ensuring the confidentiality, safety, and accurate prediction of energy consumption patterns in energy trading is a major challenge. To address these issues, an innovative solution that synergistically combines two cutting-edge technologies: blockchain and machine learning is proposed. This paper unveils a novel approach that harmoniously merges blockchain with the Recalling-Enhanced Recurrent Neural Network (RERNN) to revolutionize energy trading systems called 'Blockchain-Enhanced Energy Trading with Recalling-Enhanced Recurrent Neural Network (BET-RERNN).' Data from IoT-enabled smart devices is securely stored in blockchain blocks, ensuring data integrity and immutability. Blockchain's decentralized nature creates a trust-less environment for energy trading, protecting the privacy and anonymity of participants while maintaining transparency. At the heart of our system lies the advanced machine-learning capabilities of the RERNN model. By processing the data stored on the blockchain, RERNN accurately predicts optimal power generation for small-scale energy systems, enabling smart communities to make informed decisions and optimize their energy consumption. The BET-RERNN scheme provides a plethora of strengths. First, participants can securely engage in energy trading without compromising sensitive information, fostering a more resilient and efficient market. Second, blockchain technology ensures that all energy-related data is protected from tampering and unauthorized access, ensuring system reliability and trust. An in-depth comparison of RERNN's performance to traditional General Regression Neural Network (GRNN) and Gradient Boost Decision Tree (GBDT) methods is conducted. To verify the strategy's effectiveness, MATLAB simulations are employed, demonstrating its real-world applicability and scalability. By combining blockchain and machine learning, a secure and privacy-preserving smart community is established, promoting sustainable energy practices for a greener future. [ABSTRACT FROM AUTHOR]