Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Comprehensive 4E Analysis, Multi-Objective Optimization, and Feasibility Study of Five Natural Gas Liquefaction Processes With a Case Study for Iran(Elsevier Sci Ltd, 2026) Basmenj, Farhad Rahmdel; Tabriz, Zahra Hajimohammadi; Aghdasinia, Hassan; Mohammadpourfard, MousaNatural gas (NG) is increasingly vital as a cleaner energy source due to its lower carbon emissions compared to other fossil fuels. Liquefaction facilitates efficient long-distance transportation. While numerous studies address NG liquefaction's technical aspects, holistic research remains limited. This study presents a comprehensive evaluation of five conventional natural gas (NG) liquefaction processes (including SMR-Linde, SMR-APCI, C3MRLinde, DMR-APCI, and MFC-Linde) through a 4E framework: energy, exergy, exergoeconomic, and exergoenvironmental analyses. Addressing limitations in prior research, it incorporates environmental considerations and introduces production volume-independent metrics to ensure equitable comparisons. Multi-objective optimization, based on exergoeconomic and exergoenvironmental criteria, is employed to identify Pareto-optimal operating conditions. To accelerate this complex process, neural networks are utilized. The study concludes with a feasibility assessment of large-scale LNG production in Iran, offering practical insights for optimizing process selection and enhancing the economic and environmental viability of LNG technologies. Simulations show that the MFC-Linde cycle as the most efficient regarding specific energy consumption (0.2563 kWh/kgLNG), coefficient of performance (3.184), and exergy efficiency (52.32 %). It also demonstrates the lowest unit exergy cost (3.67$/GJ) and exergy unit environmental impact (5271.86mPts/GJ). Multi-objective optimization, considering both exergetic-economic and exergetic-environmental criteria, using neural networks and genetic algorithms in MATLAB identifies Pareto-optimal conditions for all processes. For the MFCLinde, as the most efficient process, optimal operating conditions in the exergetic-economic trade off scenario are: Exergy efficiency of process = 51.45% and Exergy cost rate of LNG = 82, 162.15$/h; at Pressure of NG feed = 5, 925.32kPa, Pressure drop in valve = 5, 831.99kPa, and NG side temperature in heat exchanger = -168.34 degrees C. Finally, a feasibility study for large-scale LNG (Liquefied Natural Gas) production in Iran shows promising results, with a return on investment of 32.24 %/year and a payback period of 2.34 years, indicating the project's potential success in regions with abundant NG reserves.Article Citation - WoS: 2Citation - Scopus: 2Image Processing and Artificial Neural Network Based Determination of Surface Mean Texture Depth on Lab-Controlled Chip Seal Pavement Samples(Nature Portfolio, 2024) Gokalp, Islam; Uz, Volkan Emre; Barstugan, Mucahid; Balci, Mehmet CanBecause surface texture is nearly the sole indicator of pavement functional properties and highly correlated with critical operational characteristics of roadways like traffic noise and safety, the change in pavement surface texture because of traffic loadings and environment has to be evaluated routinely. There are numerous direct or indirect evaluation techniques in the market. However, most of these methods have some limitations like requiring lane closure or being expensive. In this study, a 2D image processing method was established to estimate the surface mean texture depth (MTD) of chip sealed pavements. We produced chip sealed pavement samples in the laboratory with different aggregate type, shape, and size ranging between 2 and 19 mm to cover wide range of live conditions. Two well-known conventional test methods, Sand Patch (SP) and Hydrotimer (HT), were used to determine MTDs of chip seal samples. Subsequently numerous photos were taken on surface of the samples with a camera for 2-D image processing that was done based on surface void ratio (SVR) approach. With the image processing, SVR of all samples were determined. At the point of whether there is a relationship or not, correlation analysis was made between the MTDs obtained with SP and HT and the data obtained by SVR approach with the artificial neural network method. The results show that the proposed SVR approach construed on 2D image processing method can be a reliable alternative to evaluate the surface texture of pavements.