Energy efficiency improvements in longan drying practice

This study evaluates the feasibility of integrating pulsed electric field (PEF) technology with heat recovery for fruit juice pasteurization, comparing it to conventional high-temperature short-time (HTST) pasteurization. Three preheating temperature conditions (35 °C, 45 °C, and 55 °C) and varying heat recovery efficiencies have been assessed to analyze energy consumption, economic feasibility, and environmental impact. The results indicate that, while PEF pasteurization requires a higher initial investment, it improves energy efficiency, leading to significant reductions in utility costs. Across the tested configurations, PEF technology achieved reductions in electricity consumption by up to 20%, fuel gas usage by over 60%, greenhouse gas emissions by approximately 30%, and water consumption by 25%, compared to HTST. The optimal configuration of the PEF process, featuring a 35% waste heat recovery efficiency and a pre-heating temperature of 55 °C, has been identified as the most energy-efficient and sustainable solution, effectively reducing both water consumption and CO2 emissions. A life cycle assessment has confirmed these environmental benefits, demonstrating reductions in global warming potential, fossil fuel consumption, and other impact categories. This study suggests that PEF technology can significantly contribute to more sustainable food processing by reducing environmental impacts, optimizing resource usage, and enhancing energy efficiency.

In this work, the industrial case study of effluent drying with a direct and hybrid heat recovery configuration is investigated for the purpose of improving heat recovery potential and understanding the influence of various design parameters on the performance indicators in the spray drying process. Initially, data collection and performance analysis of industrial spray dryer of effluent management is performed. Then, the effect of design variables such as recirculation ratios, mass flow rate of dry hot air, water vapor, temperature of the mixing chamber, and heat input on performance parameters (thermal efficiency, energy savings, and the sustainability index) are evaluated for several configurations of the spray drying unit used in effluent management. As an outcome, substantial improvement in energy efficiency (83%) and energy-saving (26%) was achieved for a spray drying system with a 70% recirculation ratio of exhaust air for a hybrid heat recovery configuration, which is approximately 2.5% higher than the direct heat recovery configuration and it is significantly higher than that without an energy recovery system. In addition, a significant reduction in the blower power and heat input is noted in the direct and hybrid heat recovery system. The sustainability index is higher for the hybrid heat recovery configuration, indicating it being the most environment friendly configuration. The absolute humidity in the exhaust air is kept constant during entire investigation for uniformity in analysis.

The Environmental Protection Agency classifies healthcare as one of the leading energy-consuming industries. Extensive energy is needed around the clock in healthcare institutions for lighting, ventilation, and operating medical equipment. However, there is a growing concern over the sustainability of energy utilization by healthcare institutions worldwide. This narrative review thus seeks to examine energy efficiency and utilization in healthcare institutions and energy management and conservation techniques and make recommendations for future optimal usage. The paper notes that healthcare institutions use different quantities of energy from diverse sources, including hydropower, biomass, solar energy, and wind power. However, energy consumption varies from one institution to another, with the number of beds and intensity of healthcare operations, with an average of 0.27 MWh m−2. Moreover, this review also identified various techniques and measures to enhance energy efficiency, such as the variant refrigerant flow technology and the combination of renewable energy sources with diesel generators to reduce the cost of electricity. Overall, healthcare institutions need energy management systems such as automated energy monitoring technologies, to check the systems' efficiency. The same techniques can also help Middle Eastern healthcare institutions with efficient energy utilization. Ultimately, the literature review aims to introduce an approach that focuses on reducing site-level consumption of energy while increasing the quality of the energy used and hence, helping reduce energy costs while conserving the environment.

Clean production of power and heat for waste water treatment plant by integrating sewage sludge anaerobic digester and solid oxide fuel cell

The shipping industry shows potential for improvements in energy efficiency. Nonetheless, shipping companies appear reluctant to adopt these seemingly cost-efficient technical and operational measures aiming at reducing energy costs. Such phenomenon is not specific to the shipping industry and is commonly referred to as the energy efficiency gap. Decades of research in other sectors have contributed to the development of taxonomy of economic, organizational and psychological barriers that determine energy efficiency gaps through the use of a variety of research frameworks. This article aims to apply this research in the shipping context through interviews and review of existing literature and applications from other industries, with the objective of providing useful insight for shipping managers. The article discusses examples of barriers that are typical to shipping and that are related to information asymmetries and power structures within organizations. Managers of shipping firms are encouraged to look through their organizations in search of principal agent problems and power structures among the possible causes for energy efficiency gaps in their companies’ operations and possibly strive towards organizational change.

Communication industry demands the frameworks, which allow significant improvement in energy efficiency with spectral efficiency, however, these two do not always coincide and may even conflict sometimes in cognitive radio networks. In addition to this, the distributed network structures are preferred over centralised structures due to ease of deployment as well as less information exchange between the cognitive users which reduce the network-overhead. Therefore, in this study, the authors have proposed a framework to enhance the spectral as well as energy efficiency by exploiting the novel frame structure in the distributed cognitive radio networks. A self-scheduled multichannel-medium access control (SMC-MAC) protocol has been proposed in which the contention and sharing intervals are exploited for the efficient spectrum and energy utilisation. Moreover, the closed-form expressions of the spectral efficiency, power consumption and energy efficiency for the proposed SMC-MAC protocols are derived. Furthermore, the relation between energy and spectral efficiency for the proposed framework is also derived. The numerically simulated results are presented to validate the proposed work and Monte-Carlo simulations are performed for the random events in order to support the numerically simulated results.

The energy is substantial input for the economic growth of any country. In lieu of developing countries, increase in energy demand requires huge investments. Consequently, energy cost reduction, efficiency improvement and energy conservation utilization, management and audit are needed. In Sultanate of Oman, energy demand has been rising rapidly due to contemporary growing economic and population. The non-renewable natural gas produces energy. This prompts the move toward renewable energy sources and the implementation of Energy Efficiency and Conservation (EE&C) schemes such as energy audit and conservation. It is an analysis of the energy flow within a building that attempts to balance the energy input with its use not affecting the energy output. It is used to classify the entire energy stream. Energy management is used to attain and maintain an optimum energy flow to reduce energy costs and prevent the misuse of energy. Comprehensive EE&C measures are needed to conserve energy and for load leveling. In this paper, energy audit, conservation and management studies are conducted for Salalah College of Technology. The saving in the energy is achieved by implementing EE&C schemes through load observation, identifying the unnecessary usage of power and weak links in energy usage.

This paper examines the application of artificial intelligence (AI) in desalination. The study explored AI techniques, including machine learning, neural networks, and genetic algorithms, to enhance system efficiency and reduce energy costs. Case studies assessed the impact of AI on desalination systems, including those powered by renewable energy sources. Key findings revealed that AI-driven systems improved water quality, reduced energy consumption by up to 50%, and enabled predictive maintenance, minimizing downtime. Challenges in integrating AI with renewable energy-powered water treatment and desalination systems were addressed by analyzing hybrid setups combining solar, wind, and battery storage with reverse osmosis (RO) and multi-stage flash (MSF) technologies. These systems demonstrated critical improvements in energy efficiency, making desalination more viable for arid and remote areas. Fault detection algorithms and predictive maintenance emerged as pivotal AI applications, significantly reducing maintenance costs and enhancing reliability. This study was concluded by identifying challenges such as the intermittent nature of renewable energy and the complexities of designing scalable hybrid AI systems. Future research should further refine AI techniques, advancing sustainable and energy-efficient water treatment solutions.

Analysis and feasibility study of residential integrated heat and energy recovery ventilator with built-in economizer using an excel spreadsheet program

This chapter explores the innovative integration of methanol reforming and heat recovery technologies for sustainable hydrogen production, building on the techno-economic and multi-objective optimization framework presented in the author’s prior research. The focus is on converting methanol-to-hydrogen efficiently while maximizing energy utilization through cascading heat recovery systems. The chapter highlights the role of proton-exchange membrane fuel cells in enhancing system performance and reducing carbon emissions. By leveraging advanced optimization techniques, the proposed system demonstrates significant improvements in energy efficiency and cost-effectiveness, offering a viable pathway for decarbonizing energy systems. The discussion also addresses the challenges and opportunities in scaling up methanol-to-hydrogen technologies, emphasizing their potential to contribute to a sustainable energy future. This work aligns with global efforts to innovate methanol utilization and transition toward cleaner energy solutions.

Sustainable construction materials for low-cost housing: Thermal insulation potential of expanded SBR composites with leather waste

Improving the detection of thermal insulation in buildings –which includes the development of models for heating and ventilation processes and fabric gain - could significantly increase building energy efficiency and substantially contribute to reductions in energy consumption and in the carbon footprints of domestic heating systems. Thermal insulation standards are now contractual obligations in new buildings, although poor energy efficiency is often a defining characteristic of buildings built before the introduction of those standards. Lighting, occupancy, set point temperature profiles, air conditioning and ventilation services all increase the complexity of measuring insulation efficiency. The identification of thermal insulation failure can help to reduce energy consumption in heating systems. Conventional methods can be greatly improved through the application of hybridized machine learning techniques to detect thermal insulation failures when a building is in operation. A three-step procedure is proposed in this paper that begins by considering the local building and heating system regulations as well as the specific features of the climate zone. Firstly, the dynamic thermal performance of different variables is specifically modelled, for each building type and climate zone. Secondly, Cooperative Maximum-Likelihood Hebbian Learning is used to extract the relevant features. Finally, neural projections and identification techniques are applied, in order to detect fluctuations in room temperatures and, in consequence, thermal insulation failures. The reliability of the proposed method is validated in three winter zone C cities in Spain. Although a great deal of further research remains to be done in this field, the proposed system is expected to outperform conventional methods described in Spanish building codes that are used to calculate energetic profiles in domestic and residential buildings.KeywordsFeature SelectionHeating SystemMachine IntelligenceImprove Energy EfficiencyIndoor TemperatureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

10 - Improving energy efficiency in next-generation IoT networks with beam adjustment technique

Thermal and acoustic performance evaluation of 3D-Printable PLA materials

Economic growth pressure and energy efficiency improvement: Empirical evidence from Chinese cities