Papers by Keyword: Optimization

Abstract: Biogas produced via anaerobic digestion where microorganisms break down organic matter in the absence of oxygen, is seen as a promising solution to global energy and environmental challenges. Co-digestion of two or more wastes enhances biogas yield. However, study on optimization of biogas yield using substrate combination is seldom reported. This study was conducted to determine optimum substrate combination to maximize biogas yield. Simple lattice mixture design (SLMD) of Design Expert 13 was employed for experimental design and model development. SLMD was used to systematically vary ratios of different biodegradable wastes. Cassava vinasse (CV), kitchen waste (KW), cow dung (CD) and poultry dropping (PD) were taken as independent variables, and biogas yield as response. Fifteen biodigesters were set-up for the laboratory experiment. Four of the biodigesters were single-waste setups, while the rest digesters were used for co-digestion. A Scheffé quadratic model was developed to predict biogas yield and numerical optimization technique was used for optimization. The model developed gave adequate prediction with coefficient of determination (R²) of 0.7504 and adequate precision of 7.72. The optimum substrate combination of cassava vinasse (8.6%), kitchen waste (7.1%), cow dung (41.6%) and poultry droppings (42.7%) were obtained for co-digestion process. The findings from this study made invaluable contributions to the field of waste co-digestion to enhance biogas production, offering a sustainable approach to managing organic waste and producing renewable energy.

Abstract: This study investigates plantain peduncle extract as a green corrosion inhibitor for AA6063 aluminium alloy in 1M NaCl using electrochemical techniques (OCP, LSV, Tafel analysis). Extract concentrations were tested at 30–50°C. Results showed concentration-dependent inhibition, with (0.3 ml) achieving maximum efficiency: 85.48% (30°C), 88.00% (40°C), and 89.92% (50°C). Tafel data confirmed reduced corrosion rates (0.18–0.13 mm/yr vs. control: 1.24–1.29 mm/yr) and increased polarization resistance (1.71–2.34 kΩ·cm² vs. control: 0.247 kΩ·cm²). OCP/LSV curves demonstrated cathodic potential shifts and suppressed current densities, indicating mixed-type inhibition. Langmuir isotherm analysis (R² > 0.994) confirmed monolayer adsorption, with ΔG_ads values (−64.32 kJ/mol at 30°C) suggesting chemisorption dominance. Optical micrographs revealed reduced corrosion with inhibitor concentration, though isolated pitting persisted. Empirical optimization (ANOVA) identified 0.144 ml at 30.1°C as optimal for minimal corrosion rate (0.21 mm/yr).

Abstract: The automotive industry has been challenged by the rising need for lighter, environmentally friendly, low-emission, and low-energy consumption vehicles. Aluminium is regarded as a viable alternative to the heavier materials presently used in manufacturing automobiles due to its desirable characteristics. A review of the application of hybrid aluminium matrix composites (HAMCs) and aluminium matrix composites (AMCs) in the automotive sector is discussed in this paper. An overview of the properties and applications of fiber-reinforced, discontinuous, and particle-reinforced AMCs and HAMCs is given. Due to their superior mechanical, tribological, and physical properties, aluminium composite materials have emerged as the material of choice for most engineering applications. A discussion of the importance of proper selection of materials is also presented. The potential applications of AMCs and HAMCs in the automotive industry, i.e., brake discs and drums, cylinder blocks and liners, pistons, crankshafts, connecting rods, brake calipers, turbo heat exchangers, and others, are also addressed in this review. Recent trends and trends forming in aluminium use in automotive applications are also determined through the assessment.

Abstract: In this study, Taguchi-L18 design is applied to cut AISI 304 stainless steels based on surface roughness under the effects of main control factors through un-coated carbide (K10 grade) and TiAlN coated carbide. The orthogonal array and analysis of variance are utilized to examine the performance characteristic when turning steel bars. A linear regression analysis is carried out to find out the relationship between input parameters and output. In addition, the chips are collected by both cutting inserts to see the morphology. The experimental results indicated that optimal levels were determined at 190 m/min speed, 0.076 m/rev. feed rate, 1.4 mm depth of cut when used TiAlN coating insert for surface roughness. Pareto chart and analysis of variance results revealed that feed rate was dominant, followed by coated tool and cutting speed in analyzing the surface roughness, but the coating was more effective than that of the speed. Further, it was concluded that correlation coefficients were around 93.8% for output. Confirmation tests were provided by Taguchi method and regression analysis. Moreover, the chips collected by TiAlN carbide inserts showed long narrow chips, leading to lower surface roughness because of obtaining the lowest feed rate/moderate speed and insert hardness in addition to providing the larger chip radius and chip length.

Abstract: The paper presents a study of the behavior of particles of different sizes in a medium, focusing on their settling rate, hardness and elastic modulus. The settling rate was calculated using Stokes’ law, which shows a quadratic dependence on the particle radius. The results demonstrate that particles with a diameter of 100 μm settle significantly faster compared to smaller particles (1 μm and 10 μm), while the latter remain suspended for a long time due to the significant influence of viscosity. Mechanical properties of particles, such as hardness and elastic modulus, exhibit size dependence: hardness decreases with decreasing particle size, making smaller particles more vulnerable to mechanical stress. The elastic modulus shows a weak decrease for small particles, which may affect their resistance to deformation during collisions. The results obtained are important for the practical use of particles in various technological processes, such as liquid purification, development of nanomaterials, transport of solid particles in liquid or gas flows. The study emphasizes the need to consider the relationships between the physical, mechanical and dynamic characteristics of particles for optimizing technological processes and developing new materials.

Abstract: Friction stir welding (FSW), which is termed a green manufacturing process, is a very efficient method for joining magnesium alloys. In this research work, dissimilar Mg-Al-Zn magnesium-alloys have been welded at different operating conditions using FSW method with the aim of optimizing the tensile-strength (TS). The maximum value of TS was 234.86 MPa which was obtained at 15 mm of shoulder-diameter ( SD_Tool), 40 mm/min of welding-speed (WS) and 1000 rpm of tool-rotational-speed (TRS). Further, mathematical model for TS was developed to optimize the TS using desirability approach. The desirability approach predicts the optimized value 248.83 MPa at 15 mm of SD_Tool, 30 mm/min of WS and 1000 rpm of TRS.

Abstract: In this study, CFD simulations were conducted to investigate the optimal design parameters for the diffuser vane slit, which is one of the means to suppress diffuser rotating stall (DRS), in terms of slit width, height, and position, to maximize the DRS suppression effect while maintaining diffuser performance. The investigated pump is a centrifugal pump with a specific speed of Ns = 138 m³/min, m, rpm and the diffuser is an axial-flow type. The simple prediction method of DRS by CFD simulation used in the previous study was applied and evaluated in terms of the coefficient of variation C.V., static pressure recovery coefficient C_P, and total pressure loss coefficient C_T in the diffuser flow channel. As a result, it was found that the slit position was the best at 18% of the vane axial chord length regardless of the slit width, and that the slit height from the hub to the tip provided the best DRS suppression. A wider slit width increases the flow rate through the slit and enhances the DRS suppression effect, but it causes lower diffuser performance and results in a trade-off relationship. Therefore, the slit width should be set to an appropriate value depending on the required operating range.

Abstract: Today's computational capacity enables the use of advanced statistical algorithms to identify relationships between features in high-dimensional data. Additive manufacturing methods are typically complex processes with many variables in both printing parameters and material properties. Consequently, machine learning offers opportunities for process optimization, quality assurance, and innovation in both Material Extrusion and Powder Bed Fusion technologies. The paper reviews the recent findings in machine learning applications for these additive manufacturing techniques, focusing on areas like defect detection, process control, and material property prediction. Key trends reveal that, while machine learning offers promising enhancements for additive manufacturing, challenges remain in data scarcity, model generalization, real-time adaptability. Our findings underscore the potential of machine learning to improve the overall quality of additive manufacturing processes by predicting optimal manufacturing parameters.

Abstract: An important challenge of swarm robotics in practical applications lies with optimizing swarm navigation especially in dynamic or time-changing environments, which may affect the swarm’s overall performance. One technique to guide swarm behavior is by the use of the gradient climbing algorithm. This is an optimization technique where agents move towards increasing values of a scalar field, such as heat intensity or gas concentration, based on local gradient information and enables agents to navigate towards areas of interest by iteratively adjusting their positions to maximize the gradient. In complex and dynamic environments, achieving optimality may be difficult if appropriate swarm leadership strategies are absent. Leader selection entails identifying certain agents that may possess superior sensing capabilities, computational power, or strategic positioning within the swarm to guide the swarm behavior and decision-making. Therefore, this study develops an algorithm for dynamic leadership selection in swarm robotics for operations in changing environments such as in forest fires. Using the gradient information, leadership roles are assigned within the swarm to robots with the highest gradient value, which allows the algorithm to adapt to changing environmental conditions and improves the overall navigation towards the desired gradient maxima. The convergence of the swarm to the global maxima is evaluated through simulations, and shows that swarms with dynamic leader selection have convergence times that are less than half of the convergence times obtained in swarms with fixed leaders and swarms with no leaders selected. Also, the algorithm results in a reduced exploration area corresponding to improved energy efficiency when compared to the swarm with fixed leaders. The results demonstrate the effectiveness of dynamic leader selection in optimizing swarm behaviour in changing environments and its potential for real-world applications.

Abstract: Gravity dust-catcher is one of wildly used dedusting mechanical equipment in industry field, which separates dust from gas flow through gravitational precipitation. The gravity dust-catcher for blast furnace gas in one steel plant is researched with computer numerical simulation in this work. Based on the flow pattern analysis and particles separation efficiency computation, an optimization scheme is proposed for the blast furnace overhaul. The dust collection efficiency is improved evidently, which has been validated in the new campaign life of the blast furnace.