Türkiye is one of the biggest developing countries and the second biggest cement exporter in the world. In 2021, the country exported around $1billion of cement, which is responsible for over 8% of emissions globally. In order to fulfill the EU norms, energy, emissions, and cost reduction investments continue in the country. The aim of this paper is to perform a detailed exergoeconomic assessment of a rotary burner to increase the energy and exergy performance and decrease energy consumption, exergy costs and environmental impacts of a real scale cement factory in Türkiye. During the 2-year period, detailed data has been obtained from the factory by real time detection of clinker manufacturing process. By applying the specific exergy costing (SPECO) method, energy and exergy destructions, and exergetic cost distributions for the rotary burner are calculated in detail. The 1st and 2nd law efficiencies of the overall factory, specific energy (SEC) and exergy (SExC) consumption, and SPECO for clinker production are calculated to be 59.84%, 39.04%, 4786.75 MJ/ton, 5230.38 MJ/ton, and 10.11 $/MJ, respectively. The use of magnesia-spinel composite refractory bricks and the anzast layer formation decreased the SPECO by 2.71% corresponding to a saving of $2,280,000 preventing 13.74 MtCO emissions yearly.
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http://dx.doi.org/10.1007/s11356-022-24882-w | DOI Listing |
Sci Rep
January 2025
School of Mechanical & Electronical Engineering, Lanzhou University of Technology, Lanzhou, 730050, China.
Heliyon
December 2024
Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China.
This study explores the optimization and performance of a hybrid energy system combining a geothermal heat pump (GHP) with a wind turbine in Izmir, Turkey. Utilizing a 4E (Energy, Exergy, Economic, and Exergoenvironmental) analysis approach, the system aims to enhance winter heating efficiency. Geothermal heat pumps leverage the Earth's consistent temperatures for heating and cooling, offering a sustainable alternative to traditional energy sources.
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January 2025
Department of Mechanical Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran.
This article introduces an innovative multipurpose system that integrates a solar power plant with a coastal wind farm to generate refrigeration for refinery processes and industrial air conditioning. The system comprises multiple wind turbines, solar power plants, the Kalina cycle to provide partial energy for the absorption refrigeration cycle used in industrial air conditioning, and a compression refrigeration cycle for propane gas liquefaction. An extensive energy and exergy analysis was conducted on the proposed system, considering various thermodynamic parameters such as the solar power plant's energy output, the absorption chiller's cooling load, the electricity generated by the turbines, the wind turbines' power output, and the energy efficiency and exergy of each cycle within the system.
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January 2025
Department of Mechanical Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, P.O. Box 179, Ardabil, Iran.
In this article, the effect of parameters in the solid oxide fuel cell cycle has investigated using the response surface method. The thermodynamic modeling of this cycle has been done by EES software, which by considering three variables (current density, molar flow rate and fuel cell temperature) as input parameters, to examine the mutual effects of parameters on the objective functions (net output power and exergy efficiency) using the experimental design method. According to the results of thermodynamic analysis, the net power output and exergy efficiency of solid oxide fuel cell are 2424 kW, 52.
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January 2025
Computational Data Science Program, College of Computational and Natural Science, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia.
This study presents an in-depth analysis and evaluation of the performance of a standard 200 W solar cell, focusing on the energy and exergy aspects. A significant research gap exists in the comprehensive integration of numerical models with advanced machine-learning approaches, specifically emotional artificial neural networks (EANN), to simulate and optimize the electrical characteristics and efficiency of solar panels. To address this gap, a numerical model alongside a novel EANN was employed to simulate the system's electrical characteristics, including open-circuit voltage, short-circuit current, system resistances, maximum power point characteristics, and characteristic curves.
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