KTU Open Journal Systems (Kaunas University of technology)
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Utilization of Cowpea Seeds (Vigna Unguiculata L.) Compare to Poly Aluminium Chloride (PAC) in the Coagulation-Floculation Process of Peat Water
No full textPeat water has special characteristics, namely brownish color, high organic matter and high ferric content. It requires a treatment before it can be used for daily needs. The water treatment used in this research was coagulation-flocculation with Cowpea seeds and PAC as coagulants. The coagulation method aims to destabilize particles so that they can be combined with other particles to form larger aggregates and able to settle down. The purpose of this study was to determine the efficiency of using Cowpea seeds for removing organic matter and color in peat water, to evaluate the sludge volume index (SVI) and sludge mass and then to compare the results of using cowpea seeds with PAC as coagulants. In this study, Cowpea seeds can remove 91% of organic substance, 94.85% of color, has 38.29mL/g of SVI value and 98.26% of sludge mass. Meanwhile, PAC can remove 98.67% of organic, 98.26% of color, has 129.55mL/g of SVI value and 118.14% of sludge mass. The results of this study shows that Cowpea seeds have almost the same ability as PAC to be a coagulant but the dose is high. Cowpea seeds dose is more than 14 times of PAC dose.
Assessing a Pilot Door-to-Door Municipal Collection Program in Greece: Implementation Insights and Evaluation Outcomes
No full textDoor-to-door collection is the most effective method to meet the objectives of the EU’s new circular action plan. In line with the Directives (EU) 2018/851 and 2018/852, several municipalities in Greece are transitioning from the conventional kerbside system to door-to-door collection. In the municipality of Rafina-Pikermi, a pilot scheme for the collection of Municipal Solid Waste (MSW) was implemented and operated for a nine-month period to separate waste into recyclables and biowaste. This study aimed to assess the effectiveness of door-to-door waste collection in terms of purity, contamination levels, and resident satisfaction, involving 154 households and 528 residents. The analysis is based on data collected during the pilot program and includes results from waste sampling and transport to two facilities: a Materials Recovery Facility and a Mechanical Biological Treatment plant. Collection occurred five days a week, excluding weekends, and waste was sorted using barcodes into two categories based on the color of the bag (brown for biowaste and blue for recyclables). Each bag was weighed at the transport vehicle and analyzed at the recovery and biological treatment facilities to determine the percentage of impurities in the total weight and to separate it into various waste fractions. The results show that the purity levels for recycling waste from source separation of biowaste and recyclables are approximately 98% and 93%, respectively. Finally, an analysis of questionnaire data collected from participating households indicated that they rated the overall experience as satisfactory and provided feedback on general operational issues that could be improved. In conclusion, the current study proves that the development of source separation schemes is essential to achieving the goals set by the EU
Optimizing Coagulant-FlocculantComposition to ReduceDissolved Zinc inElectroplating Effluent
No full textElectroplating liquid waste creates toxic metal-organic complexes, posing a considerable environmental hazard. The primary problem is determining the most efficient and ecologically beneficial approach for processing this waste. One promising approach is the coagulant-flocculant procedure. This study focused on establishing the optimum combination of coagulant and flocculant to decrease Zn heavy metal liquid waste. The research involved experimenting with various compositions of pH, PAC (Poly Aluminium Chloride), and anionic polymers. The jartest equipment was applied to analyze changes in Total Dissolved Solid (TDS), Zn levels, and turbidity. The findings revealed that the optimal conditions were obtained at pH 8 with 40 ppm coagulant and 0.1 ppm flocculant. TDS increased by 11.05% (from 1262 ppm to 1401 ppm), whereas turbidity and Zn levels fell by 98.17% (from 54 to 1 NTU) and 98.38% (from 9.8 ppm to 0.16 ppm), respectively. These results underline the suitability of this composition for effectively treating increasing volumes of electroplating waste
Energy Demand and CO2 Emission Forecast Model for Turkey with Deep Learning and Machine Learning Algorithms
No full textThis study has conducted a forecast analysis of the energy demand and carbon dioxide (CO2) emissions of Turkey, a developing country. Considering Turkey’s rapidly increasing energy demand, various economic and social parameters have been used for the years 1990-2024. Both machine learning and deep learning methods have been applied, and artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and linear regression (LR) algorithms have been used for two models. The performance of these models has been assessed using various error metrics. The ANN has demonstrated the highest accuracy in modelling energy demand, achieving a coefficient of determination of 98.89 %, while the RNN has shown the best performance in modelling CO2 emissions, with a coefficient of determination of 96.80 %. The findings have shown that the growth rates in energy demand and CO2 emissions are high in the early years but slowed in the following years. However, it has been determined that the general trend continued to increase. The study emphasises the need for Turkey to diversify its energy sources and increase the use of renewable energy to meet its increasing energy demand. It also has concluded that accelerating efforts to achieve net zero emission targets are critical to long-term energy security and environmental sustainability
An Exact Analysis of Fine Resolution Frequency Estimation Method from Three DFT Samples: Windowed Data Case
No full textFrequency estimation for a single complex sinusoid in noise is a fundamental problem in signal processing. A suboptimal but simple frequency estimator, known as Jacobsen estimator, which is based on three discrete Fourier transform (DFT) samples, gives good bias performance without the need to increase the DFT size. Candan has modified the Jacobsen estimator by adding a so-called bias correction factor to further reduce the bias of the estimator. In addition to bias considerations, a number of asymptotic variance expressions of the estimators were performed in the literature. However, these expressions are valid only for signal frequencies located very near a DFT bin index. In this paper, with the use of a simple variance analysis technique, an accurate general variance expression for arbitrary frequency locations is derived for the case of windowed data. A general method for calculating the bias correction factor is also proposed. The variance expression is examined for the cosine-sum window family. An approximate variance formula for sufficiently large data record lengths is also given for windows from this family. Computer simulations are included to validate the theoretical results
Aerodynamic Shape Optimization of Simplified Ground Vehicle (Ahmed Body) using Passive Control Devices
No full textThe present study aims to control the flow separation on the simplified ground vehicle (Ahmed Body) utilizing cylindrical roughness elements and vortex generators (VG) as passive control devices. To simulate the flow over the solid body, the Computational Fluid Dynamics (CFD) method was used and combined with aerodynamic shape optimization applied concurrently to obtain the optimal dimensions and position of the passive control devices. Firstly, aerodynamic analysis methodology was validated by conducting also mesh independence study with experimental results from the literature. Later on, by changing the slant surface angle of the model, the analyses were performed to indicate different scenarios and the results were presented visually for each case. Aerodynamic shape optimization is performed using a Genetic Algorithm (GA) on DesignXplorer in ANSYS to find the optimum size and location of the passive control devices that minimize drag force, as an objective. Consequently, the flow separation of the rear end of the body was found to be delayed or suppressed and vortex height is to be reduced thanks to the applied passive control devices. The total drag reduction was achieved by about 10.72%, and 13.37% for the optimal shape and location of the cylindrical roughness elements and VG devices, respectively, comparing to the baseline model
Shape Optimization of the Aerostatic Bearing Considering Dynamic Performances
No full textThe shape optimization is conducted to improve the dynamic performances of the aerostatic bearing with an air pocket. Firstly, the basic configuration of the air pocket used in the optimization is created, and four typical flow patterns are discussed. And then, the displacement impedance of the air film-floating facility system is studied, and the effects of bearing parameters on displacement impedance are discussed in detail. The approximate model of the displacement impedance is established; the optimization model is built based on the flow analysis, and both the Reynolds number and the maximum Mach number in the bearing clearance are considered in optimization to suppress the micro-vibration. The shape optimization is conducted and the optimum pocket configuration is achieved. Through optimization, the self-excited vibration is reduced, and the capability of the bearing to resist the external dynamic load is enhanced, which means that the operational stability of the aerostatic bearing is improved. The optimization process offers a reference for the bearing optimization in the engineering application
Real-Time Swing-up of a Linear Inverted Pendulum Using Reinforcement Learning
No full textThis study focused on applying and enhancing the Deep Deterministic Policy Gradient (DDPG) algorithm to effectively control a Single Inverted Pendulum (SIP) system. The primary objective was to improve the algorithm\u27s performance by addressing common challenges such as overestimation of Q-values and convergence to local optima. The system\u27s behaviour was analyzed through simulation and real-world experiments, showcasing the algorithm\u27s ability to offer faster responses, enhanced stability, and reduced pendulum displacement. The research introduced key modifications to the experience replay mechanism and the Critic network, which played a significant role in improving the efficiency of the learning process and the robustness of the control strategy. By combining Reinforcement Learning with traditional control methods, this approach successfully managed the nonlinear dynamics of the SIP system. Nevertheless, certain challenges persist, particularly in terms of the efficiency of deep reinforcement learning algorithms and their stability in real-world environments. These findings suggest that future research should focus on further refining DRL algorithms to increase their practical application in physical control systems. In conclusion, the research highlights the potential of combining DRL techniques with conventional control strategies for tackling complex control problems. The success achieved in controlling the SIP system indicates a promising direction for further exploration and development in this field
Application of Worm Optimization Algorithm to Design Challenges: A Focus on Pressure Springs
No full textMeta-heuristic algorithms are approximate algorithms that are used in situations where traditional optimization techniques cannot achieve acceptable results. Specifically, they are widely used to achieve optimum design of machine elements. Designing springs according to minimum weight or volume is one of the most basic problems in this field. In this study, a bio-inspired meta-heuristic Worm Optimization Algorithm (WOA) inspired by the contribution of worms to nature was used to solve the problem. The results obtained were compared with those obtained by other algorithms and it was seen that similar data was obtained recently. As a result, it was determined that this algorithm can be used for optimum design of mechanical structures
Design and Parametric Study of Lightweight Aluminium Alloy Bumper Beams for Enhanced Crashworthiness Using the Finite Element Method
No full textThe study focuses on improving the performance and efficiency of a vehicle bumper beam by addressing limitations in the baseline design, which uses Steel 350MC material with a thickness of 3 mm. The baseline design, though robust, is bulky, absorbs limited crushing energy during high-speed impacts, and contributes to reduced fuel efficiency due to its heavy weight. To address these challenges, a three-point bending test was employed to evaluate stiffness and energy absorption characteristics at the component level. Based on numerical simulations, three novel bumper beam designs were developed using aluminum alloy materials to achieve weight reduction while maintaining performance. Design of Experiments (DOE) was utilized to validate these designs through iterative three-point bending tests, identifying the optimal configuration using AA6056 material. The optimized bumper beam demonstrated a 61.29% reduction in mass, an 8.8% improvement in energy absorption, and no compromise in stiffness compared to the baseline. Furthermore, the optimized design was validated in a slow-speed RCAR (Research Council for Automobile Repairs) numerical simulation to ensure compliance with crashworthiness standards. Results confirmed the optimized bumper beam meets RCAR requirements, highlighting its suitability for real-world application. This research provides automakers with a lightweight, high-performance bumper beam design that enhances crashworthiness, reduces vehicle mass, and improves fuel efficiency, contributing to sustainable automotive innovation