Least Amount Of Energy Research Articles

Fatigue Crack Growth Monitoring and Investigation on G20Mn5QT Cast Steel and Welds via Acoustic Emission

The fatigue crack growth properties of G20Mn5QT cast steel and corresponding butt welds, using compact tension specimens, were monitored and investigated via acoustic emission (AE) techniques. Fatigue crack growth is a combination of cyclic plastic deformations before the crack tip, tensile crack fractures, and shear crack fractures. The cyclic plastic deformations release the maximum amount of energy, which accounts for half of the total energy, and the second-largest number of AE signals, which are of the continuous-wave type. The tensile crack fractures release the second-largest amount of energy and the largest number of AE signals, which are of the burst-wave type. The shear crack fractures release the least amount of energy and the lowest number of AE signals, which are similar to the burst type, albeit with a relatively longer rise time and duration. Crack tip advancement can be regarded as a discontinuous process. The critical area before the crack tip brittlely ruptures when the fatigue damage caused by cyclic plastic deformations reaches critical status. The ruptures produce a large number of tensile crack fractures and rare shear crack fractures. Through fractography observation, the shear crack fractures occur probabilistically around defects caused by casting or welding, which lead to stress and strain in the local complex.

Multiple Seismic Slip‐Rate Pulses and Mechanical and Textural Evolution of Calcite‐Bearing Fault Gouges

AbstractNatural fault zones are complex, spatially heterogeneous systems. Rock deformation experimental studies simplify the complexity of natural fault zones either as a surface discontinuity between intact rocks (bare‐rock surfaces) or as a few mm‐thick gouge layer. However, depending on the simplified fault type and its slip history, the response to applied deformation can vary. In this work, we conduct laboratory experiments for investigating the evolution of mechanical parameters of simulated faults made of calcite gouge subjected to multiple (four) identical seismic slip‐rate pulses. We observed that, as the number of applied slip‐rate pulses increased, (a) initial friction and steady‐state friction remained approximatively constant, (b) peak friction and normalized strength excess increased and, (c) the slip distances to achieve peak and steady‐state friction, Da and Dc, decreased. The greatest changes occurred between the first and the second slip‐rate pulse. From this pulse onward, the dissipated energy of the calcite gouge fault was similar to those obtained in bare‐rock surfaces experiments. Microstructural analysis showed that, strain is localized in up to two (recrystallized) principal slip zones (PSZ) with sub‐micrometric grain size, surrounded by low porosity sintered and non‐sintered comminuted gouge domains. We conclude that previous seismic slip episodes impact on both the structure and the strain localization processes within a fault, contributing to its shear fabric evolution. We highlight that the strain localization process identifies the PSZ, dissipating the least amount of energy within the entire experimental fault zone.

Design of a Sugarcane Crusher: Towards Sustainable Processing

Abstract: In order to answer the expanding demand for increased sustainability and efficiency in the sugar sector, this research focuses on the design and development of an innovative sugar cane crusher. The proposed crusher design includes cutting-edge elements that maximize juice extraction while consuming the least amount of energy. The structural integrity and mechanical reliability of the design are guaranteed through thorough study and testing, including finite element simulations. The study also looks at automation and control systems to improve crushing and keep product quality constant. In comparison to traditional models, the new crusher design achieves better rates of juice extraction and uses less power, showing promising outcomes in operational efficiency. The applicability of the design to various processing scenarios and sugar cane kinds is also illustrated.By providing a response that meets industry objectives for increased efficiency and sustainability, this research helps to improve sugar cane processing technology.

Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor Networks

Wireless sensor networks (WSN) find applications in diverse fields such as environmental monitoring, healthcare, and industrial control systems. The pivotal components of these networks are the sensor nodes, which, unfortunately, consume a substantial amount of energy when transmitting information directly to the base station (BS). To mitigate energy consumption associated with direct transmission, this paper proposes a two-phase approach utilizing hybrid clustering and routing algorithms. The proposed approach incorporates fuzzy and seagull techniques for clustering and adopts optimal CH (cluster head) selection, CBRP (Cluster-Based Routing Protocol), and AES (Advanced Encryption Standard) for secure routing. The system employs rule-based fuzzy logic to correlate input values in both clustering and routing algorithms. Decision-making is based on factors such as the residual energy of sensor nodes, distance from the BS, and the number of nodes within the communication range. Input variables' crisp values are transformed into diverse fuzzy values, and the fuzzy output values are converted back to crisp values using the centroid defuzzification method. Selection of cluster heads and routers is determined by the output values, with sensor nodes being allocated to respective cluster heads based on their load-handling capacity. The routing path is then generated considering the capacity of routers. Simulations are conducted to evaluate energy consumption, active sensor nodes per round, and the sustainability period of the network. This proposed hybrid clustering and routing system aim to enhance the overall efficiency of wireless sensor networks by optimizing energy consumption and ensuring secure data transmission. The optimization model identifies the most suitable nodes in the routing cycle, starting with chosen cluster heads. The overarching goal is to enhance network indicators, including network lifespan, power consumption per node, and packet delivery percentage. The proposed solution achieved a network lifetime of 100 hours and a data delivery rate of 98%. additionally, it consumed the least amount of energy, measuring at 95,000 joules.

Analysis of factors influencing the energy efficiency in Chinese wastewater treatment plants through machine learning and SHapley Additive exPlanations

Wastewater treatment plants (WWTPs) contribute significantly to the control of pollution in water. However, they are significant energy consumers. Identifying the factors influencing energy consumption is crucial for enhancing the energy efficiency of WWTPs. To address this, the unit energy consumption (UEC) of WWTPs was predicted using machine learning models. In order to accurately evaluate WWTPs' energy utilization efficiency, a comprehensive energy evaluation indicator, UEC (kWh/kg TODremoved) was utilized in this study. Among the prediction models, the eXtreme Gradient Boosting (XGBoost) achieves the highest prediction accuracy. SHapley Additive exPlanations (SHAP) was adopted as the model explanation system, and the results revealed that UEC was negatively affected by TN concentration, which was the most influential factor. The stoichiometry-based model calculation result indicates that the nitrification consumes average 77 % of the overall oxygen demand. SHAP analysis illustrated that the UEC of main technologies decreases with increasing influential factors. Partial dependence plot (PDP) compared average UEC of these technologies and SBR consumed the least amount of energy. The research also indicated that low influent TN concentration is the main problem in China. Consequently, it is imperative to exert efforts in ensuring the influent TN concentration while simultaneously making appropriate adjustments to the treatment process. This study provides valuable implications and methods for retrofitting and upgrading WWTPs.

Fuzzy logic controller for UAV with gains optimized via genetic algorithm

A gains optimizer of a fuzzy controller system for an Unmanned Aerial Vehicle (UAV) based on a metaheuristic algorithm is developed in the present investigation. The contribution of the work is the adjustment by the Genetic Algorithm (GA) to tune the gains at the input of a fuzzy controller. First, a typical fuzzy controller was modeled, designed, and implemented in a mathematical model obtained by Newton-Euler methodology. Subsequently, the control gains were optimized using a metaheuristic algorithm. The control objective is that the UAV consumes the least amount of energy. With this basis, the Genetic Algorithm finds the necessary gains to meet the design parameters. The tests were performed using the Matlab-Simulink environment. The results indicate an improvement, reducing the error in tracking trajectories from 30% in some tasks and following trajectories that could not be completed without a tuned controller in other tasks.

Energy capable protocol for heterogeneous blue brain network

The Blue Brain has a wide range of applications, which raises a number of challenging issues. Electronics may continuously monitor their surroundings depending on the real data that their Blue Brain nodes are acquiring by employing situational intelligence based on the Blue Brain environment. The Blue Brain does more than only monitor user behavior when utilizing this technology. Blue Brain is linked to a critical prerequisite for energy-efficient communication methodologies. Through the Blue Brain network, it utilizes the heterogeneity and variety of the interconnected components. Blue Brain nodes that are outsourced and have limited energy resources must utilize less energy. IoT nodes with differing energy levels are frequently dispersed across different geographic regions. The main goal of this work is to provide an energy-efficient Blue Brain framework capable of managing cluster head (CH) selection and Blue Brain node clustering. The appropriate CHs are selected, and an energetic cutoff concept is developed to guarantee that energy is shared equally among the CHs and participating Blue Brain nodes. The proposed concept envisions three different kinds of Blue Brain nodes for a Blue Brain infrastructure: expert, intermediary, and normal Blue Brain nodes. Level 1 Blue Brain nodes are regarded as normal nodes; level 2 nodes are regarded as intermediate Blue Brain nodes; and level 3 nodes are regarded as expert Blue Brain nodes. Level 1 Blue Brain nodes use the least amount of energy. The outcomes of the simulation demonstrate that the recommended strategy outperforms other existing methods.

ТЕХНИКО-ТЕХНОЛОГИЧЕСКИЕ АСПЕКТЫ ЛАЗЕРНОЙ ПРОПОЛКИ РАСТЕНИЙ

The creation of technologies and autonomous robots for laser weeding of agricultural crops is a promising and relevant direction in the fight against weeds, which will allow for high-precision and targeted processing of crops without the use of herbicides. (Research Purpose) The research purpose is analyzing technologies and technical means for laser weeding of plants. (Materials and methods) Using the aspect approach, the analysis of domestic and foreign scientific publications on the use of laser technologies in agricultural practice was carried out. (Results and discussion) It has been established that laser weeding technology includes several key components: mobile robotic devices, computer vision systems for plant recognition, navigation systems for precise positioning of robotic platforms in field conditions and laser systems for high-precision weed eradication. Artificial intelligence and deep learning methods are preferred for plant recognition. CO2 lasers, fiber and diode lasers are mainly used as laser energy sources. The most effective solution for laser weeding is a ground-based mobile robotic platform with a block-modular design, which is equipped with integrated laser systems for processing plants, weed recognition systems, as well as various navigation systems. (Conclusions) It is determined that carbon dioxide lasers with wavelengths of 9.3, 10.2 and 10.6 micrometers and power up to 100 watts are used for laser weeding. Diode lasers operate in the wavelength range of 450-900 nanometers with pulses lasting up to tenths of a second and a power of 5-25 watts, forming a contact spot measuring 0.5-2.5 millimeters. It has been established that fiber lasers with high peak power of short pulses and compact dimensions with a wavelength in the visible and near infrared ranges of 0.75-1.4 micrometers are promising for laser weeding. To treat weeds, carbon dioxide lasers require the least amount of energy - up to 125 Joules per weed at a wavelength of 10,600 nanometers. When using wavelengths of 1908, 940 and 532 nanometers (fiber, diode and solid-state lasers), 230, 237 and 1400 Joules are spent per weed. It is also determined that the height of treatment of weeds with a laser beam is 0.3-0.6 meters.

A novel data-driven model-free synchronization protocol for discrete-time multi-agent systems via TD3 based algorithm

System modeling is a complex and time-consuming task in engineering, and traditional model-based or observer-based methods are unable to deal situations when the system models are unknown or affected by elements such as temperature. As a result, it is critical to build model-free methods capable of iterative learning and real-time updating. In this paper, a data-driven, model-free TD3-based algorithm is proposed. The neural network is used to address the issue of dimensional explosion in the state and action space. Besides, each agent can set a variable number of neighbors via virtual neighbor technology, giving the connection topology more flexibility. What is more, the proposed controller is only constructed based on the consensus error, which can quickly realize synchronization while consuming the least amount of energy. Finally, proofs and some simulation examples are given to verify the efficiency of our algorithm.

Indoor experiments of a horizontal multiple effects diffusion solar still: Influence of heat input and the number of stages

<abstract> <p>Clean water is important for human life, and obtaining it with the least amount of energy is significant. This research aims to desalinate water using a horizontal multiple effects diffusion solar still (DSS). A small distillation device with an area of 10 × 10 cm<sup>2</sup> was designed and 3D printed. An electric heater was used to simulate solar radiation at 400, 700, and 1000 W/m<sup>2</sup>. The amount of water produced when using 1, 3, and 5 effects was recorded. The most notable results were: maximum water yield reached 1.93 kg/m<sup>2</sup> h at 1000 W/m<sup>2</sup>, at which the solar to vapor conversion efficiency was 107%. The daily water yields throughout the year were estimated, and the maximum production was 10.16 kg/m<sup>2</sup> during a day in June, when the global horizontal irradiance was 7.01 kWh/m<sup>2</sup>. The results were also compared with other distillation systems.</p> </abstract>

Supportive particle swarm optimization with time-conscious scheduling (SPSO-TCS) algorithm in cloud computing for optimized load balancing

Task scheduling for virtual machines (VMs) has shown to be essential for the effective development of cloud computing at the lowest cost and fastest turnaround time. A number of research gaps about job schedule optimization are included in the current paper. A thorough analysis of the data generated by this activity is essential to resolving the resource allocation mechanism of the cloud architecture. To fully utilize virtual machines with a similar weight distribution, a strategy-oriented mixed support and load balancing structure has been developed in this work. To minimize make-span time and accomplish initial load balancing, the SPSO-TCS technique combines Time-Conscious Scheduling with Supportive Particle Swarm Optimization. Finding the optimal make span time minimization for each virtual environment is the aim of this stage. Its main objective is to discover the sequence of activities with the least computation time and to reduce the time required to finish each operation. Utilizing the hybrid idea leads to a decrease in makespan and the use of the least amount of energy.

Study of the Impact of Data Compression on the Energy Consumption Required for Data Transmission in a Microcontroller-Based System.

As the number of Internet of Things (IoT) devices continues to rise dramatically each day, the data generated and transmitted by them follow similar trends. Given that a significant portion of these embedded devices operate on battery power, energy conservation becomes a crucial factor in their design. This paper aims to investigate the impact of data compression on the energy consumption required for data transmission. To achieve this goal, we conduct a comprehensive study using various transmission modules in a severely resource-limited microcontroller-based system designed for battery power. Our study evaluates the performance of several compression algorithms, conducting a detailed analysis of computational and memory complexity, along with performance metrics. The primary finding of our study is that by carefully selecting an algorithm for compressing different types of data before transmission, a significant amount of energy can be saved. Moreover, our investigation demonstrates that for a battery-powered embedded device transmitting sensor data based on the STM32F411CE microcontroller, the recommended transmission module is the nRF24L01+ board, as it requires the least amount of energy to transmit one byte of data. This module is most effective when combined with the LZ78 algorithm for optimal energy and time efficiency. In the case of image data, our findings indicate that the use of the JPEG algorithm for compression yields the best results. Overall, our research underscores the importance of selecting appropriate compression algorithms tailored to specific data types, contributing to enhanced energy efficiency in IoT devices.

Influence of various fractions of ultrafine humato-saproel suspension on the growth, development and quality of basil (Ocimum basilicum L.) products in comparison with chemical fertilizers

The paper presents the results of a study of the effect of various fractions of humic acids (humic and fulvic acids), extracted from ultrafine humic-sapropel suspension, on the growth and development of various varieties of basil used for medicinal purposes. The plants were grown in a closed grow box for 50 days, while maintaining a stable internal microclimate and an optimal level of insolation for the culture inside the working area. Four varieties of commercial use were chosen as objects for the study: Green Large (mid-ripening), Emily (early-ripening), Lemona (early-ripening) and Marian (early-ripening). A soil mixture consisting of 50 % vermiculite and 50 % peaty-gley soil was used as a solid substrate for sowing seeds: the total organic content was 30.4 %; Ntotal - 1.4 %; pHKCl - 5.57; mobile forms P2O5- 8.8 and K2O - 8.2 mg/kg. Both fractions of humic acids were added as dilute solutions at a concentration of 0.01 %. The treatment of plants was carried out by alternating two methods: a single application of each fraction separately according to the options under the root, and a foliar method, when spraying the leaves once a week. In the control variant, 1.0 N water was used for irrigation during the entire growing season. Knop nutrient solution without humic acid additives. The results of morphometric and chemical analyzes showed that the Lemon variety proved to be the best candidate for further scientific research. The treated plants did not differ in height and biomass from the controls (average 29.75 mg in treatment versus 29.00 mg in control). The best organization in the diet, based on the calculation of element structural coherence indices, also resulted in a slight decrease in the efficiency of photosynthesis in this variety, which is indirectly indicated by the values of the SPAD chlorophyll optic counter (31.98 units on average during processing, against 35.54 units on the control). It is concluded that due to the introduction of organic additives, the variety spends the least amount of energy for its growth, even in a substrate depleted in elements.

A learning automata based edge resource allocation approach for IoT-enabled smart cities

The development of the Internet of Things (IoT) technology is leading to a new era of smart applications such as smart transportation, buildings, and smart homes. Moreover, these applications act as the building blocks of IoT-enabled smart cities. The high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for processing. However, there is a high computation latency due to the presence of a remote cloud server. Edge computing, which brings the computation close to the data source is introduced to overcome this problem. In an IoT-enabled smart city environment, one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay constraint. An efficient resource allocation at the edge is helpful to address this issue. In this paper, an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation problem. First, we presented a three-layer network architecture for IoT-enabled smart cities. Then, we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization problem. Learning Automata (LA) is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource mapping. An extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

Vibrational spectra, molecular level solvent interaction, stabilization, donor- acceptor energies, thermodynamic, non-covalent interaction and electronic behaviors of 6-Methoxyisoquinoline- anti tubercular agent

The present research is a theoretical examination of 6-methoxyisoquinoline implementing Density Functional Theory quantum mechanical approach. Structural optimization was performed to achieve a stable molecular structure with the least amount of energy. Several protic and aprotic solvent interactions were performed using the IEF polarization continuum model to examine the reactivity and stability of the molecules. Molecular interactions with solvents were investigated using UV–Visible, HOMO-LUMO, MEP, NBO, and NLO properties, which shows some changes in the compound activity at solvent phases against gas phase. The molecular interaction of π*C1–N2 to π*C5 - C10 shows the higher stabilizing energy. DMSO solvent exhibits a very low band gap energy, favors the molecule's high stability. Molecular reactivity, interactions, and optical behavior were all readily apparent. The chemical reactivity of the compound was found good in water. The percentage of PED contributions was evaluated for each mode of vibrations using vibrational spectral investigation. Topological surface analysis was used for better understanding of molecular electron density, reactive center, and steric effect. ELF, LOL, and RDG was generated for compounds with different solvents. The distance of transition and the overlapping of electron and hole were calculated using electron hole analysis. Pharmacological assessments include drug likeness, ADME, toxicity, and a pre-ADMET online tool, which supports the biological capability of the compound. Finally, molecular docking with anti-tubercular protein was performed, and a Ramachandran plot of chosen protein was shown to explore stability.

Performance enhancement of stepped solar still coupled with evacuated tube collector

The provision of fresh water is the most important problem in developing countries. With the rising need for fresh water, it is vital to look for other sources. Solar energy is still one of the most essential and technically feasible applications of the sun. There are numerous varieties of solar stills; the basin type is the most basic and well-proven. The biggest disadvantage of a tra-ditional basin solar still is that it produces very little distilled water per unit area. Solar distilla-tion is one of the most basic method to remove pollutants including heavy metals, dust, salts, and microorganisms from water. When compared to rainwater, it produces more clean water. Using solar distillation technology, sea water can be converted to fresh water. In this study, a solar still with a single basin is compared against a concentrator with evacuated tubes and a stepped basin solar still to see which one produces the most output with the least amount of energy. The four cases are analyzed, and it is discovered that the productivity of case 1: a solar still with a single slope with constant flow rate is 1.05kg/m2 and the maximum temperature ob-tained during this case is 49.0°C at 3:00 PM. The productivity for case 2: single slope solar with secondary stepped basin is 1.32kg/m2 while the maximum temperature is about 61.8°C at 3:00 PM. The productivity of case 3: a solar still with a single slope linked to a compound parabolic concentrator is 1.47kg/m2 with a maximum temperature of 62.4°C at around 3:00 PM. The output of Case 4: a solar still with a single slope with secondary stepped basin and compound parabolic concentrator is 1.72kg/m2 with a maximum temperature of 70.2°C obtained at 3:00 PM. The efficiency of a solar still with a single slope and a secondary stepped basin with a compound parabolic concentrator is 63.8 % higher than the reference case.

Rutin extraction from female Carica papaya Linn. using ultrasound and microwave-assisted extractive methods: Optimization and extraction efficiencies

Green extractive methods accompanied by resource conservation through process optimization are important in working towards sustainable processes. In the present paper, rutin was extracted from the leaf of female Carica papaya Linn using microwave-assisted extraction (MAE), ultrasound-assisted extraction (UAE), sequential microwave ultrasound-assisted extraction (MUAE), and sequential ultrasound microwave-assisted extraction (UMAE) methods. Subsequently, the effect of extraction parameters on rutin yield were analyzed and compared. In addition, the extraction efficiency and energy consumption of the extraction processes were measured and discussed. In the present study, solid-liquid (S/L) ratio was determined to be the most significant extraction variable. Under optimized conditions, MUAE and UMAE were determined to yield the highest amount of rutin extracted at 18.46 ± 0.64 mg/g and 18.43 ± 0.81 mg/g, respectively. However, MUAE was determined to be the least resource efficient method as it consumed the highest amount of energy due to its relatively long extraction time. UAE was determined to be the most efficient in resource utilization as it required the least amount of energy for every mg/g of yield extracted, while the yield obtained was, nonetheless, comparatively high. The optimal condition obtained for UAE was 20 min of ultrasonic extraction time (TU), 20 % of ethanol mixture concentration (C), 710 μm of particle size (S), and 1:650 wt/wt of solid-liquid (S/L) ratio (R).

A multi‐objective optimization approach to designing window and shading systems considering building energy consumption and occupant comfort

AbstractThe energy performance of a building has a significant impact on the environment as well as the comfort of its occupants. Given that window systems and control elements such as shading devices play a critical role in a building's energy consumption and comfort, this article presents an investigation into the optimization of windows in four different orientations, along the four cardinal directions, for a typical office room in Tehran. The objective is to reduce cooling, heating, and lighting energy consumption while improving occupant comfort simultaneously, using the NSGA‐II algorithm as a multi‐objective optimization method. To this end, first, an investigation into the effects of various parameters on total building energy consumption and occupant comfort is conducted. Subsequently, each parameter is evaluated in detail concerning its impact on each façade. As the final step, for each façade, the optimal solutions and the most undesirable scenarios are determined. The results, in which the possibility of glare is also considered, can be used by architects and designers for making informed design decisions. The results show that, in all orientations, the number of slats and their distance from the wall are the most effective parameters of shading configurations. Although the eastern and western windows are larger in this study, visual discomfort can be controlled using suitable shadings to achieve acceptable levels of visual comfort. Furthermore, north façades provide the least amount of thermal comfort and consume the least amount of energy while experiencing glare for longer periods.

Parallel pump and chiller system optimization method for minimizing energy consumption based on a novel multi-objective gorilla troops optimizer

Minimizing the energy usage of the parallel pump and chiller system is a crucial method for lowering buildings’ energy consumption. The variations in the cooling load of buildings should be taken into account when optimizing the operation of the parallel pump and chiller system. Although several strategies have been put forth, it is challenging to operate at a general optimal level. The parallel pump and chiller system with different chillers and pumps is frequently used in practical applications but is rarely taken into account in literature. Hence, we propose a two-step energy consumption optimization method for a parallel pump and chiller system with different chillers and pumps based on a novel multi-objective gorilla troops optimizer (MOGTO). The gorilla troops optimizer (GTO), which makes use of an exclusive non-dominated sorting (NDS) and a diversity-preserving crowding distance (CD) mechanism, is the inspiration for the proposed MOGTO method. By resolving the trade-off between chillers and pumps, a holistic optimization based on the MOGTO algorithm was carried out to determine the least amount of energy used overall. By optimizing practical parameters, including binary and continuous variables, the overall energy consumption as well as the difference between the flow rates of pumps and chillers were simultaneously reduced. A simulation was used to examine the suggested strategy. The outcomes imply that the optimization was made logically.

Analysis and Experimental Investigation of Sound Absorbing Materials for Industrial Noise Control

Abstract These days, noise pollution is a major issue that affects the environment. The high amount of noise present in the industries has adverse impact on the workers, operators and people associated in those fields. The consequences could be as hazardous as heart attack or even lifelong hearing loss. Sound absorption through barriers, mufflers are some popular fields of study. Some natural extracted materials show impressive SAC at lower and mid frequencies. Moreover, natural materials usage in this field is being popular as they emit almost zero amount of greenhouse gases and their manufacturing process requires a little amount of energy to process for ready to usage. A galvanized steel pipe as the impedance tube was chosen because it is cheaper than the impedance tube available in the market. We have tried to measure sound absorption coefficient of some combinations of different materials as an acoustic barrier material.