Using machine learning approaches to model and optimize a combined solar/natural gas-based power and freshwater cogeneration system

This research aims to promote the sustainable utilization of locally available, environmentally friendly resources, making a substantial contribution to both mitigating global warming and fulfilling future energy needs. Hybrid renewable energy systems have demonstrated superior stability and reliability compared to single-source systems, all while operating at minimal costs. This paper provides an overview of various hybrid renewable energy systems employed for electricity generation. It underscores that in hot regions, despite high solar radiation, solar PV systems experience reduced electrical yield due to elevated solar cell temperatures beyond the recommended range, especially in summer. In such areas, leveraging solar thermal applications and abundant biomass energy from extensive agricultural activities is preferable. What sets this study apart is its innovative approach: replacing conventional hybrid systems, like PV, wind, diesel generators, and batteries, with a Stirling engine powered by dish solar concentrators, which offer high efficiency and cost-effectiveness. The proposed Hybrid Renewable Energy System (HRES), consisting of a parabolic dish solar concentrator and Biomass Digester (PDSC/BG), transforms challenges into opportunities. It provides the community with a sustainable source of electricity, heating, and cooking gas, simultaneously addressing the region's waste management issues. The system is evaluated at Brack City, Libya, and comprises a 36,560 m3 biomass digester that produces 27 Mm3 annually, a 1230 kW Stirling generator, and a 6006 m2 PDSC collector area. The system will add 5,670,534 kWh of energy to the grid yearly, with a peak load capacity of 1230 kWh and a coverage of 5,892,666 kWh. The system's technical and economical viability is evaluated using SAM software. The project has a 20.04-year payback period, an estimated capital cost of $12,365,896, and an LCOE of $0.075/kWh. Annual CO2 emissions will be reduced by 7.8 kton as a result of the project. 

A new design for a built-in hybrid energy system, parabolic dish solar concentrator and bioenergy (PDSC/BG): A case study – Libya

Progress in beam-down solar concentrating systems

Numerous numerical and experimental studies have been conducted regarding the Concentrated Photovoltaic Thermal (CPVT) system because of its significant potential for efficient conversion of solar energy. The overall efficiency of the CPVT system is strongly dependent on the device, which extracts excess heat from photovoltaic cells. The most efficient cooling technology involves active cooling, which means that heat is collected from the PV cell via the forced flow of heat transfer fluid. This research paper provides an extensive discussion on devices dedicated to active-cooling CPVT systems, taking into account the latest solutions. First, a short introduction regarding CPVT systems and their main components is presented. The second part of this study presents state-of-the-art solutions in the field of heat extraction devices for the active cooling of photovoltaic cells. The available solutions are classified into two main groups depending on the scale of internal channels: macro- and micro-. Each geometry of the heat receiver is juxtaposed with the corresponding concentrating element, photovoltaic cell, concentration ratio, heat transfer fluid, and operating parameters of the specified system. In addition, this paper discusses the advantages and disadvantages of various devices for heat extraction and provides a comparative study of these devices. Finally, a set of recommendations for CPVT cooling devices is provided.

/pdf/a-review-on-heat-extraction-devices-for-cpvt-systems-with-2vxdb3wt.pdf

A Review on Heat Extraction Devices for CPVT Systems with Active Liquid Cooling

A Review of Hydrogen Production from Onboard Ammonia Decomposition: Maritime Applications of Concentrated Solar Energy and Boil-Off Gas Recovery

A review on design parameters and specifications of parabolic solar dish Stirling systems and their applications

This paper introduces an image denoising method based on sparse representation. We assume that images are generated by a sparse land model which consists of a redundant dictionary, sparse coefficients and the representation error. The OMP (orthogonal matching pursuit) is used to get the sparse coefficients with respect to the redundant dictionary. The dictionary is trained by K-SVD algorithm which is an iterative method that alternates between sparse coding of the examples based on the current dictionary, and a process of updating the dictionary atoms to better fit the data. The K-SVD algorithm is computationally expensive and not suitable for large images, so a large image is always divided into many small patches each of which is applied with the algorithm. The denoised image is presented after averaging many small overlapped images. The results show that this denoising algorithm is quite effective.

http://www.aicit.org/IJACT/ppl/IJACTVOL4NO4MAIN_part5.pdf

An Image Denoising Method Based on OMP and K-SVD

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A review on design parameters and specifications of parabolic solar dish Stirling systems and their applications

An Image Denoising Method Based on OMP and K-SVD