University of Yalova (Turkish: Yalova Üniversitesi) is a university in Turkey's Yalova Province.University of Yalova (UYA) has been founded under the Law no 5765 on May 22, 2008 and Prof. Dr. M. Niyazi Eruslu has been appointed as the first rector.The Faculty of Economics and Administrative Sciences and Vocational School of Higher Education of Yalova that had already been established and functioning as a constituent of Uludag University became a part of the recently founded university following the enactment of the law.The Faculty of Economics and Administrative Sciences of the University provides education at the undergraduate and provides postgraduate levels for students from foreign countries and from Turkey. The faculty offers academic programs that strive to teach administrative, entrepreneurial, and economical skills. It encourages providing, compiling, and publishing new sources of information in the field of economics and administrative sciences. Also the faculty contributes the business world through programs that aim at mastering efficiency and productivity. Newer departments include business administration (English), international relations (English), social service and labor economics - industrial relations have already been established.The Faculty of Law has also been founded and attempts to open new departments have been initiated.The faculty of engineering aims not only to impart contemporary engineering and scientific knowledge in the disciplines concerned, but also to inculcate creativity, research techniques, and self development.The undergraduate curriculum provides a firm background in the basic sciences through courses in computer science, chemistry, energy systems and polymer fields.In addition to faculties, Yalova University has a vocational school of higher education.The school of foreign languages aims to improve the students' language skills before they start their English-medium instruction. The school of foreign languages has a modular system in accordance with Common European Framework of Reference.Despite being a new built university, national and international conferences, symposiums were Ottoman Empire’s Establishment Date Symposium organized by Yalova - Bilkent Universities and International VII..
This study presents a validated computational fluid dynamics (CFD) model of the Slagging Fixed-Bedgasifier, developed in OpenFOAM to simulate pure lignite, pure biomass, and coal-biomass co-gasification under pressurised oxygen-steam conditions. The main problem addressed in this study is the lack of a validated CFD-based engineering framework for assessing the fuel-flexible operation of slagging fixed-bed gasifiers using high-ash Turkish lignites and biomass blends under pressurised oxygen-steam conditions. The study therefore aims not only to predict syngas composition but also to identify operating conditions that improve hydrogen yield, reduce CO2 emissions, and maintain cold gas efficiency. Two Turkish lignites (Tun & ccedil;bilek and Soma) and selected biomass feedstocks (spruce wood, corn cob, wheat straw) were characterised by proximate and ultimate analyses. The model employs detailed devolatilisation, char oxidation, and gas-phase reaction submodels coupled with the Partially Stirred Reactor (PaSR) turbulence-chemistry interaction framework. Numerical predictions for syngas composition, hydrogen-to-carbon monoxide ratio (H-2/CO), and cold gas efficiency (CGE) were compared with experimental reference bands from peer-reviewed Slagging Fixed-Bed gasification studies. Across all cases, CFD predictions fell within experimental ranges, with relative deviations of +/- 3-4% for H-2, +/- 2-3% for CO, +/- 5% for CO2, and +/- 2% for CGE; CH4 showed slightly higher deviations (up to +/- 10%) due to its low concentration. Pure spruce wood achieved the highest hydrogen content (45.1 vol%), while Tun & ccedil;bilek lignite produced 34.2 vol% H-2 with CGE of 78%. Biomass co-feeding (20-40 wt%) increased hydrogen yield by 15-25% and reduced CO2 emissions by up to 21.8% without compromising CGE (>80%). The validated model demonstrates robust predictive capability for both single-feed and co-gasification scenarios, providing a reliable basis for optimisation studies. Parametric analysis confirmed that optimal O-2/C ratios were similar to 0.8 for coal and similar to 0.6 for biomass, with S/C above 1.0 increasing H-2 but slightly reducing CGE. These findings support the use of fuel-flexible Slagging Fixed-Bed units for hydrogen-oriented syngas production and partial decarbonisation in coal-based energy infrastructure.
The most commonly used pairwise comparison method in the literature is the Analytic Hierarchy Process (AHP), with the Best Worst Method (BWM) proposed as an alternative. Both methods can be effectively used in studies with a small number of decision-makers. However, in cases where the number of participants increases, the consistency and applicability of the surveys can sharply decrease. In some instances, it has been observed that the consistency in AHP is considered acceptable up to 10
Aim of study: Forestry residues and wood processing wastes are promising resources for biorefineries due to their abundance and easy accessibility. However, lignin recovery remains challenging in lignocellulosic biorefineries. This study investigates the delignification of spruce sawdust using ethylene glycol organosolv pretreatment, focusing on how catalyst selection affects cellulosic pulp and lignin properties. Area of study: Spruce sawdust samples were sourced from a wood processing plant in Bursa. Material and method: Spruce sawdust was delignified using ethylene glycol with phosphoric acid, acetic acid, or sodium hydroxide catalysts at 130 degrees C under atmospheric pressure. Characterization was done through elemental analysis, FTIR, TGA, Py-GC/MS, and SEC. Main results: The ethylene glycol-phosphoric acid (EGPA) system showed the highest delignification (41.55%) and lignin recovery (42.87%). The sodium hydroxide system (EGNa) produced lignin with stronger lignin-specific FTIR bands, indicating higher purity. Py-GC/MS analysis showed esterification in both fractions, with EGPAL producing mainly esters, acids and phenols, and EGNaL producing phenols, esters and aldehydes. SEC indicated EGPAL had a lower molecular weight (Mw=2814 g/mol, Mn=828 g/mol) than EGNaL (Mw=4725 g/mol, Mn=1258 g/mol). Research highlights: Ethylene glycol-based organosolv pretreatment shows promise for biomass valorization, highlighting catalyst effects on delignification, lignin recovery, and characteristics.
Reliable and cost-effective fault detection is essential to ensure the safety, efficiency, and long-term stability of photovoltaic (PV) systems. However, most existing diagnostic techniques remain limited to simulation studies or rely on computationally intensive algorithms unsuitable for low-power, real-time embedded environments. This article presents an experimentally validated, IEC 61724-compliant, long-range (LoRa)-enabled fuzzy–Internet of Things (IoT) framework for real-time PV fault detection and diagnosis. The proposed system integrates a custom multi-sensor hardware platform with redundant measurement channels for voltage, current, irradiance, and temperature; an Arduino Mega-based fuzzy inference engine for edge-level fault classification; and LoRa–Firebase connectivity for long-range data transmission and cloud-based visualization. Unlike many existing fuzzy-logic or IoT-based PV monitoring systems that rely primarily on simulation-based validation or cloud-dependent processing, the proposed framework integrates hardware-injected multi-subsystem fault testing, embedded edge-level intelligence, and IEC 61724-1-compliant monitoring within a deployable low-power architecture. To ensure comprehensive validation, the framework was assessed under both Python-based I–V / P–V simulations and hardware fault injection tests, including shading (25%–75%), sensor disconnection, and boost converter faults (metal oxide semiconductor field effect transistor, diode, and inductor degradation). The fuzzy logic diagnostic engine employs 25 optimized rules using trapezoidal and triangular membership functions, achieving robust resilience to noise, irradiance variation, and sensor drift. Experimental results demonstrated a mean diagnostic accuracy of 98.7% ± 1.2% (95% CI) and an average detection delay below 0.5 s. Compared to traditional threshold- and model-based schemes, the proposed method reduced false positives by 12%, while maintaining real-time inference (8 ms) and minimal memory usage (2 KB). The complete 50 W prototype—comprising a PV module, pulse-width modulation controller, lead-acid battery, and custom DC–DC boost converter—was implemented at a total hardware cost of ∼$185 (≈ $3.6/W). The system's hybrid fuzzy–IoT intelligence, low-power LoRa communication, and cloud-based analytics collectively establish a scalable, cost-efficient, and empirically verified architecture for intelligent PV monitoring, bridging the gap between simulation-driven research and practical field-deployable photovoltaic diagnostic systems.
This study develops and characterizes polylactic acid (PLA) based biocomposites reinforced with black cumin cake (BC) for biodegradable cutlery applications. BC was incorporated at 20-40 wt% alongside 3 wt% talc and compatibilized with maleated PLA. Neat PLA exhibited a tensile strength of 46.34 MPa and modulus of 1043 MPa, while 40% of BC added sample showed reduced strength (12.20 MPa), which improved to 15.30 MPa with compatibilization. The addition of BC significantly enhanced surface hardness from 77.83 Shore D (PLA) to 84.9 Shore D (BC40). DSC results showed a substantial rise in crystallinity from 5.18% (PLA) to 27.60% at 30 wt% BC and the complete suppression of cold crystallization. Thermal stability decreased with BC loading, with T5 falling from 290.4 degrees C to 214.2 degrees C, while char yield increased from 0.5% to 14.5%. Soil burial tests demonstrated enhanced biodegradation, with mass loss increasing from 0.22% (PLA) to 42.4% in 40 wt% BC after 46 weeks. Finite element simulations revealed a reduction in load-bearing capacity from 5.275 N (PLA) to 1.75 N (BC40). The results demonstrate that BC represent a promising filler to produce sustainable PLA biocomposites with acceptable mechanical, thermal, and biodegradation properties for eco-cutlery applications.
