Various studies to improve the thermal performance of flat plate solar collector (FPSC) solar water heater have been conducted, and more are currently in progress. This study aims to review existing methods on thermal performance enhancement for FPSC and discuss on heat-transfer enhancement using vibration and its potential application for FPSC. Ten methods for improving thermal performance are identified, which include applications of nanofluids, absorber coatings, phase change materials (PCM), thermal performance enhancers, FPSC design modifications, polymer materials, heat loss reduction, mini and micro channel and heat-transfer enhancement using vibration. An examination of heat-transfer enhancement using vibration in low frequency ranges for an evacuated-tube solar collector (ETSC) solar water heater system showed that it can potentially achieve heat-transfer enhancement of up to 78%. Nevertheless, there is still a lack of research on the applications of heat-transfer enhancement using vibration on FPSC to date.

A system’s operational life cycle now includes an integrated health management and diagnostic strategy due to improvements in the current technology. It is evident that the life cycle may be used to identify abnormalities, analyze failures, and forecast future conditions based on current data. Data models can be trained using machine learning and statistical ideas, employing condition data and on-site feedback. Once data models are trained, the data-processing logic can be integrated into onboard controllers, allowing for real-time health evaluation and analysis. Interestingly, the oil and gas industries may encounter numerous obstacles and hurdles as a result of the integration, highlighting the need for creative solutions to the perplexing problem. The potential benefits in terms of challenges involving feature extraction and data classification, machine learning has received significant research attention recently. The application and utility in pump system health management should be investigated to explore the extend it can be used to increase overall system resilience or identify potential financial advantages for maintenance, repair, and overhaul activities. This is seen as an evolving research area, with a variety of application domains. This article present a critical analysis of machine learning’s most current advances in the field of artificial intelligence-based system health management, specifically in terms of pump applications in the oil and gas industries. To further understand its potential, various algorithms and related theories are examined. Based on the examined studies, machine learning shows potential for prognostics and defect diagnosis. There are, few drawbacks that is seen to be preventing its widespread adoption which prompt for further improvement. The article discussed possible solutions to the identified drawbacks and future opportunities presented. This study further elaborates on the commonly available commercial machine learning (ML) tools used for pump fault prognostics and diagnostics with an emphasis on the type of data utilized. Findings from the literature review shows that the neural network (NN) is the most prevalent algorithm employed in studies, followed by the Bayesian network (BN), support vector machine (SVM), and hybrid models. While the need for selecting appropriate training algorithms is seen to be significant. Interestingly, no specific method or algorithm exists for a given problem instead the solution relies on the type of data and the algorithm’s or method’s aptitude for resolving the provided errors. Among the various research studies on pump fault diagnosis and prognosis, the most frequently discussed problem is a bearing fault, with a percentage of 46%, followed by cavitation. The studies rank seal damage as the third most prevalent flaw. Leakage and obstruction are the least studied defects in research. The main data types used in machine learning techniques for diagnosing pump faults are vibration and flow, which might not be sufficient to identify the condition of pumps and their characteristics. The various datasets have been derived from expert opinion, real-world observations, laboratory tests, and computer simulations. Field data have frequently been used to create experimental datasets and simulated data. In comparison to the algorithmic approach, the data approach has not received significant research attention.

This paper examines the economic and environmental impacts of district cooling systems (DCS) that are integrated with renewable energy sources and thermal energy storage (TES). Typically, a DCS offers a highly efficient and environmentally friendly alternative to traditional air conditioning systems, providing cool air to buildings and communities through a centralized system that uses chilled water. However, the integration of renewable energy and thermal energy storage into these systems can further increase their sustainability and efficiency, reducing their dependence on fossil fuels and improving their ability to handle fluctuations in demand. The goal of this paper is to provide a comprehensive review of the current state of the art of renewable energy-driven DCS with TES integrated and to highlight the benefits and challenges associated with these systems. Finally, the findings of this paper offer valuable insights into the potential for renewable energy-powered district cooling systems to contribute to a more sustainable and efficient built environment.

In order to maintain competitiveness, it is imperative for the oil and gas sectors to consistently enhance the effectiveness and efficiency of their produc In order to maintain competitiveness, it is imperative for the oil and gas sectors to consistently enhance the effectiveness and efficiency of their produc