Multi-objective Analysis Research Articles

Parametric Optimization of Friction Stir Welding of AA6061-T6 Samples Using the Copper Donor Stir-Assisted Material Method

This study presents an optimization of the process parameters for the effect of copper (Cu) donor material percentage on the friction stir welding (FSW) of AA6061-T6 alloy. Extensive factorial experiments were conducted to determine the significance of the rotational speed (ω), the transverse speed (v), the interface coefficient of friction (μ), and the Cu donor material percentage in the plunge, left, right, and downstream zones. Design Expert 13 software was used to identify the number of simulation experiments to be conducted using the Abaqus simulation software. From Design Expert 13, which is a thorough multi-objective optimization analysis software, we were able to identify ideal welding parameters such as a rotational speed of 1222 rpm, transverse speed of 1.1 mm/s, the coefficient of friction of 0.9, and a 19% donor material percentage for the plunge zone. Significant findings demonstrate that increasing the Cu donor material substantially reduced the temperature from 502 °C to 134 °C when the Cu content is increased from 0% to 50%. This integrated modeling and optimization approach provides a practical procedure to identify the best experimental parameters for the process and a new understanding to guide advances for high-quality FSW of aluminum alloys. This work offers a methodology for optimizing the FSW parameters aligned with multifaceted thermomechanical physics.

Energy absorption characteristics and multi-objective optimization of 3D bionic negative Poisson’s ratio honeycomb

This paper introduces a new bionic cell element with a negative Poisson’s ratio, which is derived from the traditional re-entrant hexagonal cell element. By incorporating rotation into the two-dimensional (2D) cell element, a three-dimensional (3D) cell element is formed, which exhibits enhanced mechanical properties. The 3D honeycomb is obtained by arraying the 3D cell elements and is enhanced by adding longitudinal ribs to the honeycomb. The present study employs the ABAQUS/EXPLICIT software to investigate the correlation between various compression velocities and the crashworthiness and energy absorption capacity of a given system. The effects of different compression velocities on the 3D honeycomb deformation pattern and energy absorption performance are analyzed. The simulation results show that the new bionic negative Poisson’s ratio honeycomb has better crashworthiness and energy absorption capacity than the traditional re-entrant hexagonal honeycomb. The nominal stress-strain curves of different honeycombs and the energy absorbed per unit mass of honeycomb are compared and analyzed, and the NSGA-II genetic algorithm is used to perform a multi-objective optimization analysis of the peak stress and energy absorbed per unit mass of honeycomb. The results show that the optimized honeycomb can increase the absorbed energy per unit mass by 7.5 % when the peak stress is the same as the initial honeycomb, while the peak stress can be reduced by 9.6 % when the absorbed energy per unit mass is the same.

Research on tribological performance of textured thrust bearing using gray relational degree and improved multi-objective water circulation algorithm

To enhance the loading capacity of the oil film, reduce the lubricating oil temperature, and minimize the deformation of the friction pairs, three types of concave textures were proposed. The tribological properties of the thrust bearing underwent multi-objective optimization analysis using the two different methods. By comparing the two methods, it was found that the improved algorithm (MOWCA) demonstrated better optimization performance. Under this algorithm, when the texture shape was triangular, the number of textures was 18n, the size was approximately 0.135 mm, and the spacing between textures was approximately 4.6 mm, the loading capacity of the oil film increased by approximately 26.8%, and the temperature decreased by approximately 0.46%.

Multi-objective RIME algorithm-based techno economic analysis for security constraints load dispatch and power flow including uncertainties model of hybrid power systems

In recent times, the landscape of power systems has undergone significant evolution, particularly with the integration of diverse renewable energy sources (RESs). This advancement presents an invaluable opportunity to enhance energy efficiency in the modern power grid, primarily by bolstering the role of stochastic RESs. The challenge lies in the optimal power flow (OPF), a multifaceted and non-linear optimization challenge that grows more complex with the inclusion of stochastic RESs that aims to optimize the allocation of power system resources to minimize the operational cost while maintaining the stability and security of the system. Addressing this, the current study introduces an innovative optimization approach, the Multi-Objective RIME (MORIME) algorithm. Drawing inspiration from the physical phenomenon of rime-ice, called the RIME, the MORIME seeks to effectively tackle OPF issues. This algorithm enhances solution accuracy by smartly dividing with non-dominated sorting and crowding distance mechanism. The proposed OPF model incorporates three types of RESs: solar photovoltaic, wind and small-scale hydropower units. While uncertainties in wind speed and solar irradiation are managed through Monte Carlo simulations, the small hydro unit is considered a constant power source. The efficacy of the MORIME algorithm is tested on IEEE 30 bus systems and results indicate that the MORIME method identifies the optimal solution for the multi-objective OPF problem while satisfying the power system constraints, thereby proving its effectiveness and superiority over MOWOA, MOGWO, MOALO, MOMRFO and MOAGDE in terms of Hyper Volume (HV) and reciprocal of Pareto Sets Proximity (1/PSP) metrices. The MORIME source code is available at: https://github.com/kanak02/MORIME

Enhanced reaction extraction distillation via multi-objective optimization and 4E analysis for sustainable recovery of organic compounds from wastewater

Resource utilization and sustainable treatment of wastewater, which can achieve efficient energy utilization and environmental protection, have received hot social attention. Tetrahydrofuran and methanol are the two most common substances in pharmaceutical wastewater. It is very necessary to explore the wastewater treatment process containing these two substances. In this study, extractive distillation and reactive distillation were proposed to effectively separate the tetrahydrofuran-methanol-water component of pharmaceutical wastewater. To further reduce energy consumption, two strengthening processes, double-column reactive-extractive distillation and reactive-extractive dividing wall column distillation were designed. Taking the minimum operating and equipment cost as the objective function, the processes were optimized by using the multi-objective genetic optimization algorithm to obtain the best process parameters. Four processes were evaluated in terms of total annual cost, energy consumption, gas emissions and thermodynamic efficiency. Compared to the traditional three-column extractive distillation process, the above-mentioned four aspects of the reactive-extractive dividing wall column process was reduced by 40.63%, 43.13%, 43.14% and 17.71%, respectively. The reactive-extractive dividing wall column distillation process is of great significance for the efficient purification of valuable organic matter in wastewater and environmental protection.

Waste-to-energy power plants: Multi-objective analysis and optimization of landfill heat and methane gas by recirculation of leachate

Elevated thermal energy trapped in landfill voids is a potential threat to humans since, fires might get ignited, eventually releasing poisonous gases, contaminated fluids (leachate), and abnormal quantity of heat. This study explores the practical potential of landfills to produce heat and methane gas through a leachate recirculation process, which involves returning nutrient-rich fluids to landfills to maximize electricity generation. This paper presents an experimental investigation of the physio-chemical properties of landfills by recirculating leachate to achieve sustainable performance characteristics of landfill models. Five input parameters i.e. waste particle size (WPS in mm), waste addition (WA in Ktoe), inorganic matter content (IO in %), leachate recirculation rate (LRR in liters/day), and age of landfill (LA in years) each at five levels is considered. The investigation is accomplished on the central composite rotating design (CCRD) matrix with a 5-level factor. The results reveal that WPS at D10, WA at 52.32 Ktoe, IO at 2%, LRR at 300 l/day, and LA at 4.07 year is the optimal setting of the input parameters that produce optimum values of the output responses considered simultaneously. An innovative method is studied for simultaneously lowering the internal temperature of landfills to ideal levels while also enhancing the rate of methane production. Energy transfer to the ORC system is 2.3 GJ in winters while 1.5 GJ in summers. Landfills equipped with leachate recirculation facilities provide 2.2 times more energy with stabilization occurring in 4th year with an energy content of 950 MJ/m3 of waste.

Interpretable GBDT model-based multi-objective optimization analysis for the lateral inlet/outlet design in pumped-storage power stations

ABSTRACT The uneven velocity distribution formed at the lateral inlet/outlet poses a significant risk of damaging the trash racks. Reasonable design of the inlet/outlet structures requires the consideration of two major aspects: the average velocity (Vm) and the coefficient of unevenness (Uc). This paper developed an optimization framework that combines an interpretable Gradient Boosting Decision Tree (SOBOL-GBDT) with a Non-dominated Sorting Genetic Algorithm (NSGA-II). 125 conditions are simulated by performing CFD simulations to generate the dataset, followed by GBDT implemented to establish a nonlinear mapping between the input parameters including vertical (α) and horizontal (β) diffusion angles, diffusion segment length (LD), channel area (CA), and the objectives Uc and Vm. The SOBOL analysis reveals that in Uc prediction, CA and α play more significant roles in the model development compared to β and LD. Besides, GBDT is observed to better capture interactive effects of the input parameters compared with other machine learning models. Subsequently, a multi-objective optimization framework using GBDT-NSGA-II is developed. The framework calculates the optimal Pareto front and determines the best solution using a pseudo-weight method. The results demonstrate that this framework leads to significant improvements in flow separation reduction in the diffusion segment and the normalized velocity distribution. The SOBOL-GBDT-NSGA-II framework facilitates a rational and effective design of the inlet/outlet.

Multi-objective optimization of twisted tri-lobed tube geometry for enhanced heat transfer and reduced resistance in turbulent flow

This study presents a detailed multi-objective optimization analysis of turbulent flow and heat transfer within a twisted tri-lobed tube, employing an integrated approach that combines Central Composite Design (CCD), Response Surface Methodology (RSM), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto frontier analysis, Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The investigation focuses on optimizing key design parameters: Reynolds number (Re), twist pitch (P), and minor circle radius (r), targeting Nusselt number (Nu), Poiseuille number (fRe), and Performance Evaluation Criterion (PEC) as optimization objectives. Our major findings reveal that the developed second-order regression model precisely predicts the influences of these parameters on Nu, fRe, and PEC, with sensitivity analysis showing Re's strong positive impact on Nu and fRe, P's negative effect on fRe, and r's minimal impact. The study notably highlights the significant Nu enhancement with Re increase. Utilizing multi-objective optimization, we identified a Pareto optimal set, and through LINMAP and TOPSIS methods, derived compromise solutions that optimally balance heat transfer efficiency, flow resistance, and overall performance. These outcomes underscore the critical importance of parameter selection to meet specific engineering needs, offering a strategic framework for enhancing twisted tri-lobed tube designs.

A multicriteria model for assessing item importance and risk using operational data from military supply chains

PurposeThis paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.Design/methodology/approachThe approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.FindingsRisk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.Originality/valueTo better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.

Enhancing the capabilities of Mg–4Zn-1RE-0.7Zr alloy through synergistic integration of ceramic (ZrO2) & solid lubricant (BN/MoS2) reinforcements: A Taguchi-GRA optimization approach

In this present investigation, novel hybrid magnesium composites were synthesized by the inclusion of self-lubricating (BN/MoS2) and ceramic (ZrO2) particulates with the help of the powder metallurgy technique. ZE41 magnesium (Mg–4Zn-1RE-0.7Zr) was applied for a broad range of components, including the gearbox casing, the framework of the door, and transmission in aircraft like the Sikorsky UH-60 and the Boeing 727. Apart from its structural application, its usage was limited because of its deficient wear and mechanical properties. This research focuses on enhancing the potential applications of the ZE41 magnesium alloy by improving its mechanical and tribological characteristics. The design of the experiment scheme was applied to explore the effect of individual reinforcements and their synergistic effects on various properties of prepared composites. Microstructural inspection was employed to inspect the morphological characteristics of prepared specimens through scanning electron microscope (SEM) and Energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction analysis (XRD). By analysing SEM, EDS, and X-ray diffraction patterns, the microstructural evaluation reveals uniform phase dispersion and the existence of reinforcements with some minor defects. As a result of the inclusion of reinforcements in composites, the hardness of the reinforced specimen improved to 39.65%, while the wear rate of the reinforced specimen decreased to 40.90% compared with monolithic alloy. A multi-objective grey relational analysis (GRA) optimization method was used to perform a detailed tribological analysis on Specimen G (ZE41 mg + 3% ZrO2 + 3% MoS2). According to the results of GRA, the optimized rate of wear and frictional coefficient were achieved on sliding velocity (4 ms−1), sliding distance (2000 m), and load (10 N). The worn surface micrograph reveals that the reinforced composite underwent abrasion and delamination wear, while the unreinforced alloy experienced plastic deformation.

Environmental and economic multi-objective real options analysis: Electrification choices for field development investment planning

Hydrocarbons combustion is one of the primary sources of greenhouse gas emissions, causing climate change. As large volumes of gas are burned to power the energy-consuming oil and gas production on offshore platforms, the Norwegian petroleum industry contributes significantly to the country’s emissions, making it a target for environmental regulations. Hence, Norwegian petroleum companies are facing the challenge of finding solutions to comply with increasing environmental requirements while being economically feasible. Offshore platforms electrification helps to reduce carbon emissions, but relevant investment decisions are complicated by uncertainty related to market fluctuations and regulations. In this study, we analyze how considering environmental and economic objectives simultaneously affects optimal investment strategies under uncertainty. We develop a novel methodology for determining the project value and the best investment policy for field development, considering several objectives and uncertain factors. By introducing multi-objective optimization into a real options investment valuation model, economic and environmental objectives are combined using the weighted sum method. The optimization model allows to determine trade-offs between the objectives of the project along with a flexible investment policy. Such optimal solutions result in a significant decrease in carbon emissions at only a marginal reduction in economic value.

Knowledge-driven networking and ambidextrous innovation equilibrium in power systems transition

PurposeThe ongoing paradigm shift in the energy sector holds paramount implications for the realization of the sustainable development goals, encompassing critical domains such as resource optimization, environmental stewardship and workforce opportunities. Concurrently, this transformative trajectory within the power sector possesses a dual-edged nature; it may ameliorate certain challenges while accentuating others. In light of the burgeoning research stream on open innovation, this study aims to examine the intricate dynamics of knowledge-based industry-university-research networking, with an overarching objective to elucidate and calibrate the equilibrium of ambidextrous innovation within power systems.Design/methodology/approachThe authors scrutinize the role of different innovation organizations in three innovation models: ambidextrous, exploitative and exploratory, and use a multiobjective decision analysis method-entropy weight TOPSIS. The research was conducted within the sphere of the power industry, and the authors mined data from the widely used PatSnap database.FindingsResults show that the breadth of knowledge search and the strength of an organization’s direct relationships are crucial for ambidextrous innovation, with research institutions having the highest impact. In contrast, for exploitative innovation, depth of knowledge search, the number of R&D patents and the number of innovative products are paramount, with universities playing the most significant role. For exploratory innovation, the depth of knowledge search and the quality of two-mode network relations are vital, with research institutions yielding the best effect. Regional analysis reveals Beijing as the primary hub for ambidextrous and exploratory innovation organizations, while Jiangsu leads for exploitative innovation.Practical implicationsThe study offers valuable implications to cope with the dynamic state of ambidextrous innovation performance of the entire power system. In light of the findings, the dynamic state of ambidextrous innovation performance within the power system can be adeptly managed. By emphasizing a balance between exploratory and exploitative strategies, stakeholders are better positioned to respond to evolving challenges and opportunities. Thus, the study offers pivotal guidance to ensure sustained adaptability and growth in the power sector’s innovation landscape.Originality/valueThe primary originality is to extend and refine the theoretical understanding of ambidextrous innovation within power systems. By integrating several theoretical frameworks, including social network theory, knowledge-based theory and resource-based theory, the authors enrich the theoretical landscape of power system ambidextrous innovation. Also, this inclusive examination of two-mode network structures, including the interplay between knowledge and cooperation networks, unveils the intricate interdependencies between these networks and the ambidextrous innovation of power systems. This approach significantly widens the theoretical parameters of innovation network research.

Design and Optimization of 3D-Printed Variable Cross-Section I-Beams Reinforced with Continuous and Short Fibers.

By integrating fiber-reinforced composites (FRCs) with Three-dimensional (3D) printing, the flexibility of lightweight structures was promoted while eliminating the mold's limitations. The design of the I-beam configuration was performed according to the equal-strength philosophy. Then, a multi-objective optimization analysis was conducted based on the NSGA-II algorithm. 3D printing was utilized to fabricate I-beams in three kinds of configurations and seven distinct materials. The flexural properties of the primitive (P-type), the designed (D-type), and the optimized (O-type) configurations were verified via three-point bending testing at a speed of 2 mm/min. Further, by combining different reinforcements, including continuous carbon fibers (CCFs), short carbon fibers (SCFs), and short glass fibers (SGFs) and distinct matrices, including polyamides (PAs), and polylactides (PLAs), the 3D-printed I-beams were studied experimentally. The results indicate that designed and optimized I-beams exhibit a 14.46% and 30.05% increase in the stiffness-to-mass ratio and a 7.83% and 40.59% increment in the load-to-mass ratio, respectively. The CCFs and SCFs result in an outstanding accretion in the flexural properties of 3D-printed I-beams, while the accretion is 2926% and 1070% in the stiffness-to-mass ratio and 656.7% and 344.4% in the load-to-mass ratio, respectively. For the matrix, PAs are a superior choice compared to PLAs for enhancing the positive impact of reinforcements.

Comparative study on tribological behavior of foil journal bearings with micro pocket

Abstract This study explores the tribological performance of bump foil journal bearing (FJB) equipped with micro-pocket. The influence of micro-pocket on the load capacity (LC) and friction factor (FF) of FJBs is evaluated for various foil materials, including Inconel X750 (IN), Spring steel EN42J (SP), SS 316, Beryllium copper (BC), and Phosphor bronze (PB). The compressible Reynolds equation is solved numerically to assess the tribological behavior of bump FJB under various operating conditions. Initially, the impact of micro pocket on the tribological characteristics such as load capacity and friction factor of bump FJB is examined by comparing their performance with plain FJB. Subsequently, the effect variable parameters such as pocket depth (PD), bearing number (Λ), and eccentricity ratio (ε), on the tribological behavior is analysed by using response surface methodology (RSM). The multi-objective grey relational analysis (GRA) technique is employed to determine the optimal values of these variable parameters for each foils material. The findings reveal that micro-pocket enhance the dimensionless LC of bump FJB beyond a bearing number of 2.061. Moreover, micro-pocket reduce the friction factor of bump FJBs. Within the presence of micro pocket, SS 316 exhibits the most significant foil material for the better tribological characteristics of bump FJB, while phosphor bronze is the least signifcant. For SS 316, the optimal values of pocket depth (PD), bearing number (Λ), and eccentricity ratio (ε) are 3 μm, 2.061, and 0.5 respectively. Under these optimal conditions, the load capacity (LC) and friction factor (FF) of bump FJBs are 0.7740 and 13.645, respectively. Micro-pocket reduces the friction factor (FF) by 2 to 15%. The impact of pocket depth on the LC and FF of bump FJB is significant for the phosphor bronze. Additionally, increasing the pocket depth enhances the dimensionless LC of bump FJBs while decreasing the friction factor.

The Chilean Laja Lake: multi-objective analysis of conflicting water demands and the added value of optimization strategies

Abstract Water scarcity leads to conflicts over water allocation. Laja Lake in Chile is a natural lake, which was formed by a volcanic barrier. Outflow from the lake is created by seepage through the barrier and via a controllable artificial outlet, which adds reservoir characteristics to the lake. Hydroelectric power stations have been built at both outlets. Downstream, water is diverted into irrigation canals, and the Laja River forms the Laja Falls, a popular tourist attraction. The previous operating policy preferred the most upstream water user and was found to be inadequate because the lake level decreased over long term. The current reservoir operation policy was established through stakeholder negotiations. This study investigated whether optimization (using Non-Dominated Sorting Genetic Algorithm II) can further improve the operation of Laja Lake while maintaining a fair balance between stakeholder groups. The results were compared with the stakeholder agreement and the previous policy. The main difference is in the spring, when Laja Lake fills up before the irrigation season starts. The optimization strategy prioritizes hydropower generation during this period, resulting in reduced storage. Ultimately, optimization proves to be a valuable tool for identifying trade-offs and exploring different scenarios in water management.

Innovative use of air gap membrane distillation to harvest waste heat from alkaline fuel cell for efficient freshwater production: A comprehensive 4E study

Poor power density of alkaline fuel cell (AFC) resulting from inherent waste heat generation significantly hinders its widespread application. Herein, an innovative hybrid system that combines AFC and air gap membrane distillation is proposed, aiming to enhance the power density via utilizing the waste heat for freshwater generation. Based on the laws of thermodynamics and electrochemical theory, the hybrid system model is formulated to derive performance metrics by quantifying various irreversible losses. The thermodynamic performance, competitiveness, economic viability, and environmental impact of the system are comprehensively discussed through energetic, exergetic, economic, environmental (4E) analyses. Calculations predict that the maximum equivalent output power density permits 63.28% higher than that of the standalone AFC at 353 K. The influencing mechanisms of the AFC operation conditions, air gap thickness, and properties of the porous hydrophobic membrane are clarified to discern potential avenues for further performance improvement. Furthermore, a multi-objective local sensitivity analysis indicates that membrane porosity and coolant solution temperature are the most sensitive factors. The levelized costs study reveals that water and electricity costs over the total lifecycle are 56.89 $m−3 and 0.1012 $kWh−1, respectively. The multi-objective optimization further provides a high-performance and cost-effective design solution for the hybrid system.

Exergetic performance optimization and thermoeconomic analysis of a variable compression ratio diesel engine fueled with distilled plastic oil and diesel doped with nanographene

Due to the fast depletion of fossil fuels, enormous concerns about environmental pollution, and advocacy for waste-to-energy drives from the global perspective, compression ignition engines need a sustainable alternative fuel source. Enormous plastic wastes were generated in health sectors, particularly during post-pandemic. In this context, the study intends to introduce a reasonable solution for such waste plastics recycling by converting them into liquid oil by pyrolysis followed by the distillation process. Distilled waste plastic oil (DPO) extracted from medical plastic waste is a potential alternative diesel source. The performance of the engine significantly increases when nanographene is added with DPO/diesel blends, which act as a combustion improviser. The energy efficiency (η1), exergy efficiency (η2), and brake-specific fuel consumption (BSFC), which are regarded as key performance indicators, exhibited promising results when operated with 20% DPO +100 ppm nanographene (20DPO100G) emulsified fuel mixture as compared to normal diesel. When compared to diesel and other fuel combinations, the energy efficiency (η1) and exergy efficiency (η2) for 20DPO100G fuel mixture were found enhanced by 5.78% and 10.9%, respectively, and lowest by 14.7% for BSFC in comparison to diesel. The optimum energy efficiency, exergy efficiency, and minimum BSFC were obtained for the test engine from response surface methodology multi-objective optimization analysis as 31.44%, 22.12%, and 0.32 kg/kW-hr, respectively, for the composite desirability, D of 0.974. The 100 ppm nanographene emulsified distilled waste plastic pyrolysis oil and diesel blend has the lowest relative cost variation of −14.583.

Performance optimization and multi-objective analysis of an innovative solar-driven combined power and cooling system

Solar-driven combined cooling and power systems are promising for hot climates, but lower performance is the main concern. Therefore, an improved new configuration is proposed to enhance its performance, which consists of a suitably located secondary ejector along with an internal heat exchanger and parabolic trough collector. The proposed system is analyzed with three different refrigerants from thermodynamic, economic, and environmental perspectives. System performance is optimized by employing a genetic algorithm. At the mean operating scenario, the best exergy efficiency and performance index are 6.92 % and 0.149, respectively. The total cost with isobutene is the lowest, nearly 16500 $/yr. The performance is at its peak in January but lowest in April at Varanasi in India. The proposed system is also compared with the individual power and cooling as well as the analogous systems. The exergy efficiency and performance index of the studied system are approximately 18% and 9% higher than those of the individual cooling and power system and nearly 98% and 68% higher than the analogous system, respectively, with isobutane. Annual total cost is 8% lower than that of the individual system and 16% lower than that of analogous system. Hence, the proposed system shows superiority for building applications.

Multi-objective optimization and analysis of chemical kinetics properties: Exploring the impact of different hydrogen blending ratios on LPG and methane-air mixtures

The use of hydrogen with hydrocarbon fuels like methane and LPG is crucial for carbon neutrality and environmental goals. United Nations Environment Programme (UNEP) and the International Energy Agency (IEA) emphasise reducing LPG and methane emissions to prevent temperature increases. Utilising H2-LPG and H2-CH4 blends enhances free radical formation, mitigates emissions, and combats global warming. Using the CHEMKIN tool, this research compares the combustion characteristics of H2-CH4-air and H2-LPG-air mixtures under atmospheric conditions and different operating conditions. Blending 50 % hydrogen with LPG and methane yields notable variations, including a 20.97 % higher flame speed in H2-CH4, 42.89 % more H free radical formation in H2-LPG, 50.24 % reduction in O free radical formation in H2-LPG, and a 24.01 % increase in OH free radical formation in H2-LPG. The study investigates the energy and environmental impacts of incorporating hydrogen into CH4-air and LPG-air combustion, focusing on 50 % H2 blending with LPG. The benefits of hydrogen blending encompass heightened flame speed and augmented free radical formation, ultimately contributing to emission reduction in hydrogen-blended fuels. These findings highlight H2-LPG and H2-CH4 blends as environmentally friendly alternatives with improved combustion.

Multi-Objective Analysis of Housing Transformation in Istanbul

Multi-Objective Analysis of Housing Transformation in Istanbul