Mühendislik Fakültesi

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Citation - WoS: 11
Citation - Scopus: 11
Biogas Intake Pressure and Port Air Swirl Optimization to Enhance the Diesel RCCI Engine Characteristics for Low Environmental Emissions
(Elsevier, 2024) Dalha, Ibrahim B.; Koca, Kemal; Said, Mior A.; Rafindadi, Aminu D.
Exhaust emission and combustion control in RCCI (reactivity-controlled compression ignition) focused mainly on the direct-injected fuel parameters, urging to investigate the advantages of port-fuel intake parameters. The engine was modified for port injection of Biogas at the valve and RCCI mode. The influence of port swirl ratio (PSR, 0 - 80%) and biogas injection pressure (BIP, 1 - 4 bar) on the diesel RCCI combustion and emissions was tested and optimized at varied loads and 1600 rpm in a port injection at the valve (PIVE) approach. Established kinetic mechanisms were combined with multi-objective optimization to further investigate, predict, and analyze emissions occurrence and trade-offs for reduced environmental impacts. The results show that the radiation absorption triggered by increased CO2 lowers combustion temperature, resulting in prolonged ignition. Setting the airflow to swirl lowers the in-cylinder pressure at elevated BIP while raising the heat generated across the BIPs. Increasing the PSR slows the combustion while BIP speeds up the process. BIP and PSR show great trade-off reduction ability among all emission parameters. The optimum unburned hydrocarbon, nitrogen oxide, particulate, and carbon monoxide emissions for the injection at the valve were found to be 109.58, 0.577, and 2.336 ppm, and 0.103%, respectively, at low-load, low-BIP, and high-PSR. The emissions were lowered by 6.58, 91.26, 80.65, and 13.45% compared to the premixed RCCI mode, respectively. Therefore, introducing lowpressure biogas amid high swirling air at the valve elevates the in-cylinder condition while lowering the emissions, mitigating their environmental implications.
Citation - WoS: 18
Citation - Scopus: 20
A Simulation-Based Approximate Dynamic Programming Approach to Dynamic and Stochastic Resource-Constrained Multi-Project Scheduling Problem
(Elsevier, 2024) Satic, U.; Jacko, P.; Kirkbride, C.
We consider the dynamic and stochastic resource -constrained multi -project scheduling problem which allows for the random arrival of projects and stochastic task durations. Completing projects generates rewards, which are reduced by a tardiness cost in the case of late completion. Multiple types of resource are available, and projects consume different amounts of these resources when under processing. The problem is modelled as an infinite -horizon discrete -time Markov decision process and seeks to maximise the expected discounted long -run profit. We use an approximate dynamic programming algorithm (ADP) with a linear approximation model which can be used for online decision making. Our approximation model uses project elements that are easily accessible by a decision -maker, with the model coefficients obtained offline via a combination of Monte Carlo simulation and least squares estimation. Our numerical study shows that ADP often statistically significantly outperforms the optimal reactive baseline algorithm (ORBA). In experiments on smaller problems however, both typically perform suboptimally compared to the optimal scheduler obtained by stochastic dynamic programming. ADP has an advantage over ORBA and dynamic programming in that ADP can be applied to larger problems. We also show that ADP generally produces statistically significantly higher profits than common algorithms used in practice, such as a rule -based algorithm and a reactive genetic algorithm.
Citation - WoS: 44
Citation - Scopus: 52
CBI4.0: A Cross-Layer Approach for Big Data Gathering for Active Monitoring and Maintenance in the Manufacturing Industry 4.0
(Elsevier, 2021) Faheem, Muhammad; Butt, Rizwan Aslam; Ali, Rashid; Raza, Basit; Ngadi, Md Asri; Gungor, Vehbi Cagri
Industry 4.0 (I4.0) defines a new paradigm to produce high-quality products at the low cost by reacting quickly and effectively to changing demands in the highly volatile global markets. In Industry 4.0, the adoption of Internet of Things (IoT)-enabled Wireless Sensors (WSs) in the manufacturing processes, such as equipment, machining, assembly, material handling, inspection, etc., generates a huge volume of data known as Industrial Big Data (IBD). However, the reliable and efficient gathering and transmission of this big data from the source sensors to the floor inspection system for the real-time monitoring of unexpected changes in the production and quality control processes is the biggest challenge for Industrial Wireless Sensor Networks (IWSNs). This is because of the harsh nature of the indoor industrial environment that causes high noise, signal fading, multipath effects, heat and electromagnetic interference, which reduces the transmission quality and trigger errors in the IWSNs. Therefore, this paper proposes a novel cross-layer data gathering approach called CBI4.0 for active monitoring and control of manufacturing processes in the Industry 4.0. The key aim of the proposed CBI4.0 scheme is to exploit the multi-channel and multi-radio architecture of the sensor network to guarantee quality of service (QoS) requirements, such as higher data rates, throughput, and low packet loss, corrupted packets, and latency by dynamically switching between different frequency bands in the Multichannel Wireless Sensor Networks (MWSNs). By performing several simulation experiments through EstiNet 9.0 simulator, the performance of the proposed CBI4.0 scheme is compared against existing studies in the automobile Industry 4.0. The experimental outcomes show that the proposed scheme outperforms existing schemes and is suitable for effective control and monitoring of various events in the automobile Industry 4.0.
Citation - WoS: 49
Citation - Scopus: 53
Comprehensive Experimental Analysis of the Effects of Elevated Temperatures in Geopolymer Concretes With Variable Alkali Activator Ratios
(Elsevier, 2023) Ozbayrak, Ahmet; Kucukgoncu, Hurmet; Aslanbay, Huseyin Hilmi; Aslanbay, Yuksel Gul; Atas, Oguzhan
By growing population and rapid urbanization, demand for concrete increases exponentially. Researches on use of fly ash material in waste product class for concrete production are important to produce concrete more environmentally friendly. However, there is a need for more research to use geopolymer concrete (GPC) in every field where ordinary Portland cement concrete (OPC) is used. Therefore, it is crucial to experimentally investigate thermal properties as well as me-chanical properties of geopolymer concrete. As investigated thermal properties, the main factor affecting strength development of GPC is alkali activator ratios. In this study, GPC prism samples with nine different compositions, produced by various alkali ratios. After flexural strength tests, they were cut into cubes and exposed to 400 degrees C, 600 degrees C and 800 degrees C, then they were subjected to compressive strength tests. Results obtained from different AA/FA and SS/SH ratios were eval-uated as mechanical properties at ambient temperature and physical, mechanical and micro-structural properties at elevated temperature. An empirical formula, which considers the effect of activator ratios, was proposed to calculate flexural strength depending on compressive strength of samples at ambient temperature. As an increase of SS/SH and AA/FA ratios, compressive strength increased, while flexural strength decreased. The increase in AA/FA ratio decreased compressive strength of samples exposed to high temperatures, while increase in SS/SH ratio did not deter-mine at elevated temperatures. There is an inverse change with AA/FA ratio and parallel change with SS/SH ratio between compressive strengths of samples at ambient temperature and exposed to high temperature.

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