Engineering and Applied Science Research

Synthetic flood damage function for direct damage estimation in Loei Town Municipality

2025-01-12T21:30:43+07:00

The reflection between flood damage phenomena and flood characteristics in the flood-affected areas of Loei Town Municipality where flood damage issue is not deeply examined and documented, is desirable for a more accurate damage estimation. Therefore, based on the available empirical dataset collected during 2021-2023 floods, the site-specific flood damage functions and their curves were developed for assessing direct monetary damage to buildings. The replacement cost for household damaged contents was gathered through survey interview of 637 households, in which 75% and 25% of the entire dataset were randomly split for constructing and validating synthetic functions, respectively. The polynomial function was the best fitting method, rather than the other five damage functions (i.e., exponential, Fourier, Gaussian, Rational, and the Sum of Sines), as characterized by the highest R² values of 0.73, while relatively low values of MAE (0.17), MBE (-0.17), and RMSE (0.19) clearly indicated the validity of the synthetic damage function. All relevant data were then entered into the HEC-FIA software for damage estimation based on a structure-by-structure basis. The results revealed that the 2002 flood caused 199,330 USD in damage to Loei Town Municipality, which showed a reasonable agreement with the governmental relief budget (accounted for 56% of the 2002 total flood-relief budget of Loei Province). It is noteworthy that the findings gained from this study may be of assistance when assessing flood damage to buildings for the areas with flood-related data scarcity, in which the depth-damage function/curve developed herein could help pave the way towards more accurate flood damage estimation for risk assessment at local scale. Finally, this study could be technically beneficial for government and local authorities of Loei Town Municipality in making decisions in reducing flood damage and for residents living in flood-prone areas to achieve resilience after flood events.

Enhancing sustainability and key success factors in digital food supply chain management through digital transformation: A fuzzy AHP approach

2025-02-28T10:26:13+07:00

This study investigated the key success factors in digital food supply chain management for Thailand's food retail industry through digital transformation, employing the Fuzzy Analytic Hierarchy Process (FAHP) for the integrated digital food supply chain model. Through an extensive literature review and expert consultations, the critical criteria were identified and ranked in the researched domains of digital strategy, digital operations management, digital customer experiences, and digital organization and culture management. Business alignment is the primary focus within digital strategy, followed by technology investments to enable this alignment and data-driven decision-making. Supply chain visibility is considered the most crucial aspect of digital operations management, and data analytics along with inventory management are integral aspects that lead to operational efficiency. In the realm of digital customer experiences, aspects of customer centricity, convenience and accessibility, and order tracking and communication are the top drivers of satisfaction and loyalty. Finally, leadership and vision are recognized as essential for digital organization and culture management, along with fostering digital skills, capabilities, agility, and adaptability. In addition, this study provides a practical framework for improving Thai food retail organizations through the alignment of engineering, organizational, and consumer expectations.

Effect of warm deformation parameters on hardness and microstructure of AISI 1020 low carbon steel for near-net shape forging

2025-01-21T14:36:56+07:00

This research aims to present a concept for altering a metal manufacturing process from cold to warm forging thereby reducing unnecessary steps and energy consumption. This will lower costs and increase production profits. The study explores the impact of warm forging process parameters on the hardness and microstructure of low-carbon steel for near-net-shape forging in more than two continuous stages. The material used in this experiment is annealed AISI 1020 carbon steel with chemical additions of 0.01% Ni, 0.03% Cr, and 0.044% Al. The study procedure involves: (i) heating slugs with a height-to-diameter ratio (ho/do) of 2.07 to a temperature range of 200–700 and soaking them for 1 hour. The grain size noticeably increases at temperatures above 500 °C. (ii) The materials were forged at six different temperatures from 200 to 700 with both hardness and microstructure examined at each stage. This was done to determine the recrystallization temperature. The experimental results showed that recrystallization begins at 500 in a warm forging process and becomes more pronounced at 600 to 700 °C. The lowest average hardness value in the transverse direction (TD) occurs at 500 to 700 °C. This suggests that the suitable warm working temperature range should be below 500 °C, as the primary microstructure in the forging process has not yet undergone recrystallization. Our research provides valuable insights for manufacturers aiming to transition from cold to warm forging, emphasizing the importance of precise control over deformation parameters to achieve desired material properties.

Hybrid machine learning models: A comprehensive, data-driven evaluation with diverse data partitioning strategies for net radiation estimation

2025-02-14T11:14:01+07:00

Surface net radiation (R_n) is crucial for climate modeling and agricultural management but is often not readily available, especially in regions like Thailand. Accurate prediction of R_n is essential for estimating evapotranspiration, which is vital for irrigation planning and agricultural productivity. This study develops a hybrid machine learning framework that incorporates K-Nearest Neighbors (KNN) for missing data imputation, Random Forest-Recursive Feature Elimination (RF-RFE) for feature selection, and machine learning models (Multi-layer Perceptron, K-Nearest Neighbors, and Random Forest) for prediction. The research evaluates various data partitioning methods, including hold-out split, K-fold cross-validation, and growing-window forward-validation (gwFV), alongside hyperparameter tuning using GridSearch to enhance model robustness and prevent overfitting. The primary objectives are to develop and evaluate the hybrid ML models for daily R_n estimation using basic meteorological inputs (temperature, relative humidity, and sunshine duration), assess the impact of different input combinations on prediction accuracy in Sawi, Chumphon, Thailand, and compare data partitioning techniques to determine the optimal model performance. Utilizing FAO56PM-calculated R_n as a reference, this study finds that the Random Forest model, with average temperature and sunshine duration (M2) as inputs evaluated under the gwFV method, achieves the highest stability and high accuracy (R² of 0.972, RMSE of 0.457 MJ m^-2 day^-1, and MAPE of 3.50%). The Random Forest demonstrates strong generalization capabilities, making it a reliable choice. Even models using only sunshine duration (M3) perform adequately, offering a solution when data availability is scarce. This study concludes that hybrid machine learning models, combined with careful data partitioning, significantly improve R_n estimation. These advancements provide valuable insights for climate modeling, agricultural management, and irrigation scheduling, particularly in data-scarce regions.

Effect of the waste bottom ash strengthened the problematic clay soil: The use of by-product material

2025-02-03T22:03:46+07:00

The extraction of industrial by-products such as bottom ash from power plants to the research areas can be lucrative for the economy and environment. In the civil engineering field, the soil condition which is filled with abundant soft clay soil has always been a problematic issue. Previous studies examined the available techniques of ground improvement for instance the installation of a granular pile is an efficient method to treat the soft clay soil, resolving the detrimental issues of lower value of soil’s bearing capacity, soil settlement occurrence, greater compressibility, and erodibility. Coherent to that, this research deployed the Vibro-replacement technique in fabricating the single bottom ash column beneath the clay soil to rectify the weak engineering properties of clay soil. The raw kaolin clay and bottom ash were examined through the geotechnical approaches, the distribution of particle size (PSD), Atterberg limit, relative density, pycnometer, hydrometer, falling head, constant head, and standard proctor. Via the Unconfined Compression Test (UCT), 20 specimens from unreinforced and reinforced categories were investigated. The average shear strength value from 5 identical specimens was utilized, considering the Column Penetrating Ratio (CPR), Column Height to Column Diameter Ratio (HDR), and Column Volume Replacement Ratio (CVR). The maximum shear strength improvement occurred in the S1680 sample, recorded at 58.66% at the CPR (0.8), HDR (5.00), and CVR (8.19%). Hence, the research findings verified that bottom ash as a sustainable material can raise the clay’s strength effectively.

Comparative analysis of steady and unsteady friction models in numerical simulation of water hammer phenomenon

2025-02-11T14:20:31+07:00

This study presents comparative numerical modeling using the method of characteristics to solve the water hammer phenomenon in a pipeline that consists of reservoir-pipe-valve systems resulting from an instantaneous valve closure. Study focused on the unsteady friction approach, the two-coefficient instantaneous acceleration-based unsteady friction model (2-k IAB), which describes the transient flow behavior in pipeline systems during rapid velocity changes that result in water hammer events. Also, the methodology includes developing a mathematical model based on the numerical simulation of the governing equations using the proposed models and validating the model results by utilizing six sets of experimental data from two different laboratory sources. The study compares the performance of steady and unsteady friction models and reveals the limitations and advantages of each in simulating pressure waves resulting from the water hammer. The study also discusses the basic criteria for selecting values when using the average wave speed for experimental data. Numerical results show that the best model and the most consistent with experimental data is the 2-k IAB model, followed by the one-coefficient instantaneous acceleration-based model (1-k IAB), and finally, the least accurate model is the steady friction model. This was validated by using the statistical method Normalized Root Mean Squared Error. The methodology presented in this paper can be a valuable tool for analyzing and designing pipeline systems subjected to the water hammer phenomenon. It provides insights into transient flow characteristics resulting from instantaneous valve closure and helps determine the appropriate mitigation measures to prevent pipeline system damage.

Variations in acoustic emission characteristics across different deformation stages of various materials

2025-01-27T15:53:03+07:00

Integration of acoustic emission (AE) monitoring with traditional mechanical testing presents an attractive methodology for the prediction and assessment of material failure processes. While AE monitoring during mechanical testing has been extensively applied to study deformation and fracture in individual materials, conducting a comparative analysis across various monolithic materials using a single AE system provides valuable insights. This approach helps identify material-specific signal characteristics and behaviors that are essential for predicting failure in complex, multi-material structures. In this study, uniaxial testing with AE monitoring was conducted for evaluating failure behaviors of four different materials, namely low-carbon steel, aluminum alloy, acrylic and white Portland cement (WPC). The AE patterns obtained from tensile testing of ductile metallic materials, low‑carbon steel and aluminum alloy clearly indicated the regions of elastic, plastic and fracture by difference of peak amplitude intensity of AE signals. AE signals obtained from brittle materials, PMMA acrylic and WPC specimens showed fluctuated AE peak amplitude intensities throughout the linear deformation region until specimen fracture. The AE signals and their corresponding Fast Fourier Transform spectra provide data indicating failure of different materials, as well as failure behaviors, i.e., elastic, plastic and fracture, of ductile metallic materials.

Stress-strain relationships of spirally confined instant concrete under axial compression

2025-02-06T16:31:20+07:00

Project activities require the strength of concrete to reach 100% at an age less than 28 days. Many manufacturers have created concrete with a strength that reaches 100% at an age less than 28 days; this product is called instant concrete. This instant concrete can be designed to attain 100% concrete strength at the ages of 7, 14, and 21 days. However, concrete is not only required to be strong; it must also be ductile. Ductility is usually measured after the concrete reaches its peak load phase, namely in inelastic conditions. Square or spiral stirrups can be used for concrete confinement to improve ductility. Spirals are a better confining system than square stirrups. The evidence for this can be seen from the relationship between concrete stress and strain. The concrete stress-strain relationship is necessary as a basis for reinforced concrete design assumptions. However, for instant concrete with a certain age, it is necessary to develop its specific stress-strain relationship model. Thus, this study aims to develop a stress-strain relationship model that can be used as a basis for assumptions in analysing confined concrete structural members. This study measured the concrete strength f'_c at the ages of 7, 14, 21, and 28 days, and on average it reached the target f'_c value; the stress-strain relationship tends to fit the Kent-Park model. Therefore, the design of reinforced concrete structural members for the ages of 7, 14, and 21 days can use the proposed stress-strain relationship model.

Enhanced real-time paddy moisture content assessment in pneumatic drying using correction factor

2024-12-09T20:07:47+07:00

Accurate and real-time monitoring of paddy moisture content during the drying process is critical to preserving rice quality and ensuring efficient post-harvest management. This research article presents the application of weather sensor (DHT22, AM2302, Aosong Electronics Co.,Ltd) that measures air temperature and relative humidity for evaluating paddy moisture content in drying process of a pneumatic dryer. Khao Dawk Mali 105 paddy with initial moisture content of 26%w.b. was dried by a constant drying air temperature of 70 ^ºC and the drying air flow rate of 0.0631 m³/s with paddy feed rate of 8.45 kg/min throughout the test. Two methods namely DHT-Prediction and DHT-Factor correction methods for evaluating moisture content of paddy were developed by mass balance equation and the result were compared with the reference paddy moisture content analyzed by hot air oven method. The results showed that the DHT-Prediction method gave high moisture measurement error compared to the reference paddy moisture content (R² between 0.274 - 0.359 and RMSE between 5.077 - 7.465). The DHT-Factor correction method gave the lowest discrepancy of paddy moisture content during the first 150 minutes of the drying with R² of 0.987 and RMSE of 0.515. This method also showed low discrepancy results over the entire drying period which indicated that it has high potential for evaluating the real-time paddy moisture content during drying process.

Attention-X: Enhancing the classification of natural attraction scenes with advanced attention mechanisms

2025-03-01T23:57:51+07:00

This paper proposes the Attention-X method, which is an attention-based framework designed to address the challenges of interclass similarity and intraclass variance in natural scene classification tasks. The proposed method enhances pretrained convolutional neural networks (CNNs) by integrating an attention mechanism that selectively emphasizes salient and discriminative features, which enables the model to more effectively differentiate between visually similar scenes and manage variations within the same class. The proposed Attention-X method generates attention maps aligned with extracted features, integrating spatial representations with channel-wise relevance to overcome the limitations of the original deep features. This fusion enables the model to selectively amplify meaningful feature activations while suppressing irrelevant or redundant information. This improves the model’s ability to distinguish between visually similar scenes and to handle variations within the same class. The proposed method was evaluated on the widely used SUN397, ADE20K, and Places365 benchmark datasets. The experimental results demonstrate that the proposed Attention-X method improves classification accuracy while maintaining competitive model complexity, outperforming several state-of-the-art methods. These findings highlight the effectiveness of the proposed method in real-world scenarios where subtle interclass differences and intraclass variability pose significant challenges.

Optimization of aluminum matrix composite production in a friction stir process with MgO nanoparticle reinforcement using ANN-GRA modeling

2025-02-13T21:05:15+07:00

This research focuses on enhancing the performance of magnesium oxide (MgO) particle-reinforced aluminum matrix composites (AMCs) using friction stir processing (FSP). The study addresses the limitations of traditional methods, such as response surface methodology (RSM), which often cannot accurately capture the nonlinear relationships between critical parameters. A hybrid model that integrates artificial neural network (ANN) and grey relational analysis (GRA) approaches is developed to improve prediction accuracy and optimize mechanical properties, particularly tensile strength and hardness. MgO particles offer significant advantages by refining the grain structure, enhancing hardness, and improving tensile strength due to their large surface area and thermal stability, thereby increasing the durability and wear resistance of the composites. The ANN model effectively analyzes complex nonlinear relationships, while the GRA technique identifies optimal production parameters. The results demonstrate that the ANN-GRA model outperforms RSM, showing lower mean squared error values and more accurate predictions closely aligned with experimental outcomes. The novelty of this research lies in integration of ANN and GRA to simultaneously optimize multiple FSP parameters, addressing a research gap in composite material improvement. This approach significantly enhances the mechanical properties of the composites while minimizing material and energy use during the production process. The findings of this study hold substantial implications for the aerospace and automotive industries, which require lightweight materials with superior properties. Additionally, this research serves as a foundation for future applications of hybrid machine learning techniques to efficiently and sustainably optimize composite material production.

Green extraction of anthocyanins from roselle: A comparative evaluation of extraction techniques and solvents

2025-01-05T21:10:47+07:00

Due to the abundance of roselle in nature, the potential of the roselle to be a source of natural colorant with high anthocyanin content was explored. Moreover, in conjunction with the 12 principles and practices of Green Chemistry, one of the promising solvents that can offer greenness is deep eutectics solvents (DESs) with broad tunability and high selectivity as an alternative to volatile organic solvents. DESs are solvents comprising a combination of hydrogen bond acceptors (HBA) and hydrogen bond donors (HBD) with lower melting points than their parents’ salts. Therefore, this study explored the potential of DESs as an additive to water as solvents for the extraction of total anthocyanins content (TAC) from roselle extracts. By optimizing extraction parameters, including the best DESs, solid-to-solvent ratio, particle size, and extraction method, this study identified the most effective conditions. Based on the results obtained, the best solvents were choline chloride and triethylene glycol at a ratio of (1:1). Meanwhile, the best solid-to-solvent ratio is 1:15 (g/g). Furthermore, this study obtained the best extraction condition at 750 µm of average particle size using ultrasonic-assisted extraction (UAE) with a yield of 119.02 mg/L cyanidin-3-glucoside equivalence (GCE). As a result, UAE is a promising way to get anthocyanins out of roselle, using DESs as an extra solvent and following the set experimental rules. This study highlights the potential of DESs and the UAE to recover valuable compounds from natural sources and promote sustainable and environmentally friendly practices.

Properties evaluation of natural bitumen-filler mastic mixture

2024-12-26T15:07:24+07:00

Natural bitumen (NB) is a highly precious material and has drawn increasing attention due to its unique properties, especially since it is available in large quantities and has been used in limited fields. In this research, the exploitation of NB from sulfur springs as an alternative energy resource in the production of asphalt pavement is evaluated. It can be concluded from the experimental results that the chemical composition and surface morphology of NB samples are different from those of base asphalt. Besides, the rheological properties of virgin NB are not sufficient for paving work. To overcome this obstacle, NB from five different springs is modified with limestone filler (LSF) to enhance its properties. LSF is a natural material and is available locally at a low price, usually used as filler material in control asphalt mixtures. The study outcomes reveal that LSF is an effective material and plays a fundamental role in improving the properties of NB since it enhances the resistance against temperature susceptibility and improves the ability of NB to disperse in asphalt mixture. Furthermore, treated NB with LSF boosts the mechanical characteristics, increases the stiffness, and strengthens the resistance against water damage for NB mixtures. Particularly, this research clarified that high Marshall stability is achieved with a treated Al-Mamora sulfur spring-NB mixture, which is 30.4% higher than that of the control mixture. At the same time, the treated Al-Askaree sulfur spring-NB mixture has a stiffness index and tensile strength ratio more than the control mixture by 45% and 3.36%, respectively. In conclusion, adding LSF to NB that is extracted from sulfur springs can produce a new type of asphalt binder more suitable for use in road pavement.

Experimental study on the stabilization of laterite soil with chitosan biopolymer for subgrade applications

2025-01-28T14:38:46+07:00

Laterite soil, commonly found in the Konkan region in Maharashtra, India, often needs better engineering properties, posing challenges for its use in construction. By adding chitosan, a naturally occurring biopolymer derived from chitin, the study aims to enhance the geotechnical properties of laterite soil. Both untreated and chitosan-treated laterite soil samples were subjected to various laboratory tests, including the California Bearing Ratio (CBR), compaction, Atterberg limits, and direct shear testing. The chitosan was added in varying percentages (0.15%, 0.30%, and 0.45 % by weight) to determine the optimal dosage for laterite soil stabilisation. As the chitosan concentration increases from 0% to 0.3%, the Maximum dry density (MDD) value increases from 1.41 gm/cc to 1.92 gm/cc; adding chitosan further slightly decreases the MDD. The un-soaked CBR value of laterite soil containing 0.30% chitosan biopolymer increased by 108.81%, while the soaked CBR value increased by 142.83%. The soil's cohesiveness and internal friction angle increased by 120% and 15%, respectively, with the optimal dose of 0.30% chitosan. Utilising chitosan in T8 subgrade soil costs INR 6.48 crores, which is 2.57 times more than laterite soil, and requires a pavement depth of 1395 mm. In the case of T9 subgrade soil, at a depth of 1495 mm, the cost is INR 8.08 crores, which is 3.14 times more than that of laterite soil. This study highlights the potential of chitosan biopolymer as an eco-friendly and sustainable soil stabilizer for subgrade applications. It also offers a comprehensive cost-benefit analysis for pavement applications, demonstrating the financial feasibility of using chitosan biopolymer despite its higher initial costs.

Reinforced concrete and embodied carbon in construction: Challenges and pathways to reduction in Thailand

2025-01-05T21:01:24+07:00

The increasing demand for construction in Thailand, driven by urbanization and infrastructure development, has heightened the urgency of addressing embodied carbon emissions in reinforced concrete projects. This study evaluates the life cycle carbon emissions of a six-story outpatient department building, focusing on the manufacturing, transportation, and construction stages. Data were collected through stakeholder interviews and analysis of project-specific documents, such as bills of quantities and construction plans. Results reveal that the manufacturing stage contributes 92.75% of total emissions, with concrete and steel as the primary sources, accounting for 6.506 kt CO₂eq. Transportation and construction contribute 6.41% and 0.84%, respectively. The study identifies practical strategies for reducing embodied carbon, including material substitution with supplementary cementitious materials, optimizing logistics, and improving energy efficiency in construction practices. These strategies not only mitigate environmental impacts but also address economic and quality considerations, ensuring feasibility in the local context. The findings provide actionable insights for policymakers and construction professionals to integrate low-carbon practices, contributing to the national and global goals of sustainable development. This research offers a comprehensive framework for reducing embodied carbon emissions while maintaining economic viability and construction quality, positioning Thailand as a leader in sustainable construction.

Agricultural drought characterization for sugarcane management in Thailand

2025-01-07T14:10:11+07:00

One of the main factors affecting sugarcane production in Thailand is drought hazard due to reliance on rainfed irrigation. Sugarcane agricultural drought was studied over four regions of dense cane areas from 1990 to 2020 to help inform appropriate policies and practices for mitigating future damages. The research aimed to assess agricultural drought variability of 15 locations throughout the mainland Thailand during the three decades. Actual evapotranspiration (ETa) was used as an indicator and then transformed into the standardized ETa index (SEaI12) over a 12-month timescale, as the sugarcane crop cycle in Thailand is approximately 12 months. For the last decade, the SEaI12 series of each location was correlated with sugarcane yield, sweetness (CCS), and Nino 3.4 resulting in very good, poor, and fair, respectively. All SEaI12 time series were quantified for drought event time series using the run theory. The severity of drought was characterized by duration and deficit, and since they were closely related, only duration was explored. All evaluation processes from ETa to SEaI12 to drought characteristics were cautiously performed in detail. Drought characterizations were demonstrated in two conditions: (i) spatial distribution of the longest drought from each location; (ii) comparison of distribution and frequency of droughts among the three decades. The highest value of the longest drought is located at the highest latitude and decreases towards the lower latitudes. Among the three decades, the 1990s, an El Nino dominated decade, showed highest accumulated drought and frequency values and the 2000s, a La Nina dominated decade, gave the lowest values.

An investigation of pyrolysis kinetics in microwave-assisted conversion of oil palm shell and activated carbon for sustainable bio-oil production

2024-11-13T16:44:19+07:00

This study explores microwave (MW) pyrolysis of oil palm shell (OPS) blended with activated carbon (AC) to produce bio-oil, focusing on the pyrolysis kinetics and the effect of different feedstock ratios. Three OPS mixtures (70:30, 75:25, and 80:20) were analyzed at a constant temperature of 400°C. Gas chromatography-mass spectrometry (GC-MS) revealed that acetic acid, phenol, and other oxygenated compounds were dominant in the resulting bio-oils. Pyrolysis kinetics were modeled using a first-order reaction approach, with rate constants and activation energies determined for each blend. The 75:25 ratio yielded the highest bio-oil content and exhibited the most favorable kinetic performance, showing strong alignment between experimental and simulated data (R² = 0.758). The study also examined the impact of pyrolysis temperatures (400°C, 500°C, and 600°C) at the fixed 75:25 ratio, demonstrating that temperature plays a key role in both enhancing bio-oil yield and altering its chemical composition.

Enhancing geopolymer mortars: The role of surgical face masks in modifying mechanical and thermal properties

2024-11-27T16:06:55+07:00

The primary aim of this study is to examine how expanded surgical face masks (SFMs) affect the mechanical properties, thermal conductivity, thermal insulation, and microstructure of geopolymer mortars. In the experimental phase, SFMs were used after removing the inner nose wires and ear loops, then cut into 5 mm × 5 mm pieces. These SFMs were added to various mixtures at different weight percentages (0%, 2%, 4%, and 6%), along with palm oil clinker (POC) and alkali activator. The mechanical properties of the mixtures, such as compressive strength, bulk density, and water absorption, underwent evaluation through testing. Moreover, thermal conductivity and thermal insulation measurements were carried out to gauge the effect of SFMs on this property. Microstructural analysis of the mixtures was conducted using scanning electron microscopy SEM to examine the impact of SFMs on mortar microstructure. The test results indicated that geopolymer mortar containing 2% SFM achieved a compressive strength of 67.3 ksc at 28 days, with an alkaline to fly ash ratio of 0.8 and POC to fly ash ratio of 3. The geopolymer mortars with POC containing SFMs had a bulk density ranging from 1,233 to 1533 kg/m³ at 28 days. Notably, the use of SFMs resulted in a substantial enhancement in thermal conductivity and thermal insulation. Additionally, a strong correlation was found between thermal conductivity and bulk density, suggesting a potential relationship between these properties.

Optimization of green-synthesized silver nanoparticles using Mimosa pudica L. for enhanced photocatalytic degradation of methylene blue: A response surface methodology approach

2024-12-26T14:55:11+07:00

Silver nanoparticles (AgNPs) were synthesized using Mimosa pudica L. leaf extract as a reducing and stabilizing agent. AgNP yield was maximized using central composite design (CCD) based on response surface methodology (RSM). An AgNO₃ concentration of 3.06 mM, 732 µL extract volume, a 616 W microwave power level, and a 2.35 min reaction time were optimal conditions. Extract volume showed the highest impact on synthesis (F-value: 795.57), followed by microwave power (F-value: 168.84), reaction time (F-value: 51.38), and AgNO₃ concentration (F-value: 49.72). UV-Vis spectrophotometry (UV-Vis) was used to characterize the synthesized AgNPs. A surface plasmon resonance (SPR) peak at 428 nm was observed. It was found from diffuse reflectance spectroscopy (DRS) that the bandgap is 2.34 eV. Fourier transform infrared spectroscopy (FTIR) confirmed the presence of functional groups of plant metabolites that served as capping and reducing agents. Spherical nanoparticles with an average diameter of 7.39±2.37 nm were observed under transmission electron microscopy (TEM). XRD analysis confirmed the face-centered cubic crystalline structure of AgNPs. EDX was employed to study the elemental composition. Synthesized AgNPs were assessed as a photocatalyst for methylene blue degradation under solar light irradiation. A 79.07% conversion was achieved under optimized conditions. Kinetic studies revealed that the degradation of methylene blue followed a pseudo-second-order model with a rate constant of 0.0929 mg^-1 L min^-1 and R² of 0.9990. This green synthesis is a viable, sustainable, and cost-effective way to prepare AgNPs with good photocatalytic properties.

Impact of bentonite content on electrical resistivity and compressive strength of cement paste for grounding system applications

2025-02-18T14:03:34+07:00

This study investigates the influence of bentonite content on the electrical resistivity and compressive strength of cement pastes for grounding system applications. Ordinary Portland Cement (OPC) and Non-Shrink Grout (NSG) were modified by addition of bentonite contents ranging from 0 to 50% by weight. Bulk and surface resistivity testing was conducted to evaluate the electrical properties. Compression testing was performed to determine mechanical performance. The experimental results indicated that the bulk electrical resistivity was significantly reduced due to the increased bentonite content. It decreased from 55.65 to 0.86 Ωm for OPC and from 325.55 to 0.69 Ωm for NSG after curing for 28 days. Although its electrical properties were improved, compressive strength was decreased from 18.52 to 0.06 MPa for OPC and from 18.91 to 0.07 MPa for NSG. Electrical resistivity and compressive strength exhibited strong correlations following exponential and power law relationships with moisture content, respectively. Finally, it was found that a 30% bentonite content offers an acceptable trade-off between electrical and mechanical properties for grounding applications in high-resistivity soils.

The effects of voltage variations and surface on the performance of an HHO generator using a NaOH catalyst

2025-03-15T22:08:40+07:00

The objective of this work is to test the HHO generator by implementing innovations on the electrode plate surface and varying the input voltage. Some important indicators, such as H₂ gas production rate, output current, output temperature, and operating time, were carefully tested as an evaluation of the HHO generator's performance. The HHO generator's electrolyte was NaOH as a catalyst, and the concentration was maintained at 60 g/l. This study is mostly interested in how the voltage changed between 12V and 14V when different electrode surface textures were used. The findings demonstrated that an increase in voltage generally exhibits an increasing trend in H₂ gas production rate, output current, and output temperature, along with a decrease in operating time. The most striking was the H₂ production rate, which increased significantly by 422.13% with the voltage variations on the cross surface. Even the output current and output temperature were quite high on the cross surface as a function of voltage variation. In contrast, the operating time showed a sharp decrease with the increase in voltage on the cross surface, from 98.06 s at 12 V to 18.78 s at 14 V. It should be noted that the cross surface revealed the shortest operating time and was also the most efficient in the HHO gas production process compared to other types of surfaces. This research can aid in the addition of gas to improve the performance and emissions of gasoline engines.

Integrating systematic layout planning and fuzzy analytic hierarchy process in the design of sorting and packing fresh fruit facilities: A case study in Thailand

2024-11-14T14:52:44+07:00

The aim of this study was to contribute to the sustainable development of small and medium-sized Thai fruit exporters who face challenges in business expansion due to limitations in the operational systems of their packing houses, which represent a critical unit in the fruit supply chain. The research had three objectives. The first is to develop a layout that is aligned with the business conditions of the case study entity, with a focus on efficient space utilisation and the incorporation of movement and sorting technologies guided by Muther's systematic layout planning principles. The second involves establishing criteria for selecting operational layouts for agro-businesses, based on the application of supply chain principles that emphasise operational efficiency, food production hygiene, and worker safety. These criteria were developed through a combination of literature review and expert input from agro-industry professionals and engineering academics, within the framework of the SCOR model. The final objective was to evaluate and select the developed layouts using MCDM methods. The results indicated that the requirements for a production system can be divided into export-oriented products, which demand more resource-intensive processes, and products for domestic auction markets. Activities were categorised into two types, processing and storage, and comprised seven activities, which were analysed to develop four distinct layout designs based on differences in input-output flow, production processes, and equipment positioning. Of the criteria considered for layout selection, the highest priorities for organisational management were efficient movement and maintenance (20.03%), effective space utilisation (19.93%) and non-overlapping flow directions (16.1%). Ultimately, the third layout alternative was selected at 30.40%, as it scored the highest on movement efficiency, and featured no overlapping transfer points, optimal space allocation, and minimal waste of space.

Development of a tourist behavior analysis system using public Wi-Fi data to enhance smart tourism management: A case study of Sri Chiang Mai Smart City

2024-11-21T15:43:06+07:00

Sri Chiang Mai Smart City faces challenges in tourism management due to its limited understanding of tourist behavior patterns and inefficient resource management at tourist attractions. The study aims to analyze tourist movement patterns and preferences across key locations, develop a privacy-preserving data collection framework, and provide data-driven recommendations for tourism resource allocation. The system analyzes Wi-Fi data from 11 strategic locations along the Mekong River using spatial and temporal analysis techniques. Tourist data is protected with SHA-256 encryption and tokenization of MAC addresses. Analysis of over 72 million connection records from May 2022 to December 2023 revealed significant patterns in tourist behavior. Tourist numbers showed dramatic increases during cultural events, with a 127.87% increase during the Songkran festival from April 13 to 15 compared to regular days in the same month at Boeng Wiang Courtyard. Four locations consistently showed higher visitor levels: Sri Chiang Mai Community Health Park, Naga Courtyard, In front of Had Pathum Temple, and Boeng Wiang Courtyard. Temporal analysis identified peak hours between 17:00 and 21:00, especially during the Songkran and longboat racing festivals. The findings enabled data-driven decisions in tourism resource allocation, event planning, and infrastructure development. However, the study is limited to tourists who connect to public Wi-Fi, potentially excluding those using mobile data or no digital devices. Despite these limitations, the system demonstrates how smart city infrastructure can effectively support smart tourism management through privacy-preserving data collection and analysis, contributing to the development of sustainable smart tourism initiatives.

Optimal boosted framework for fatigue cracking prediction in polyethylene terephthalate pavement

2024-11-21T15:00:13+07:00

Depleting natural resources for construction purposes and research needs in the field of civil engineering is becoming a dependable process. To minimize this degradation process several techniques are implemented, one such method is using software approaches in research fields. In the consideration of the former investigation works, the focus point of the works is mainly on Crack Prediction on bitumen pavements. Since the bitumen pavement undergoes repeated loading under wheel loads in static conditions during traffic, and is dynamically loaded during the movement of fast moving vehicles. The strength parameters related to the fatigue limit of the bitumen are not analysed. Hence the fatigue limit of the bitumen should also be analysed. To analyze such conditions, deep learning with standardizing features is implemented to estimate the effective Strength parameters and to forecast the essential outcomes. In this study, the Fatigue Cracking Prediction of the Polymer Modified Bitumen (PMB) of grade 40 in Polyethylene Terephthalate (PET) Pavement is carried out. The Suggested Methodology is termed as Hyena based Curriculum Learning (HbCL). It is implemented in the Accuracy Prediction of Fatigue Cracking behaviour of PET pavement at different temperatures in dynamic and static load conditions. The fitness function in the optimization was triggered initially, to determine the ideal strength parameters of the Polymer Modified Bitumen as well as the PET. The tensile strength of the proposed model is 410 KPa in dynamic loading. The compression strength of the proposed model decreases with an increase in temperature. The rutting depth of the proposed model is between 1.7728mm and 1.6870mm. The Marshall Coefficient value gained was 2.7 KN/mm as the maximum for static loading. A value of 1.77mm in maximum is achieved by fatigue crack depth under dynamical load. The mean fatigue crack prediction accuracy estimated was 96.1%.

In-Vessel bioconversion of garden waste into compost with an emphasis on process efficiency and compost quality

2025-01-05T20:04:34+07:00

With the fast-growing urbanization efficient management of garden waste, whether at residential or social levels, has now become a challenge and an opportunity to enhance soil quality. Three different trials T1, T2, and T3 were executed constituting different forms of garden waste like un-shredded, shredded and shredded dry leaves with additives with varying processing conditions carried for 42 days using cylindrical In-vessel composters made up of HDPE. The composting process is scrutinized regularly at fixed intervals for all parameters like temperature, moisture, pH, electrical conductivity, germination index, C/N, micro & macronutrients etc. The temperature reached thermophilic in two trials T2 and T3. The pH reached the basic range of 8-8.5 in the final compost of all trials. The moisture was maintained up to 65% throughout the composting period. The trial 3 was the best combination among all the trials in which the variation of pH was (7.7-8.5), total organic carbon (42.2-35.6), total nitrogen (0.7-1.4), C: N ratio (60.3-25.4), cellulose (43.4-4.2), hemicellulose (10.2-0.9) and lignin (13.1-0.15) at the end of 42 days. The degradation rate of Un-shredded leaves was found to be very slow due large size of dry leaves. The findings highlighted that effective garden waste management is possible by following particular guidelines and it reduces the environmental impact of current disposal techniques and in addition, improves soil health using garden waste compost.

Potential approach to assess seawater breakthrough using attenuated total reflectance-Fourier transform infrared spectroscopy

2025-01-30T10:16:25+07:00

Seawater is commonly utilized as the primary source of water injected into reservoirs as part of secondary recovery operations within oilfields, aiming to optimize production levels. Nevertheless, the implementation of such a procedure can bring about significant issues, such as the formation of sulphate scale, which may arise due to variations in composition between seawater and water present in the reservoir. Consequently, a prompt and straightforward methodology has been devised, leveraging attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), to supervise the chemical composition of the produced water for any potential signs of seawater breakthrough and to approximate the proportion of seawater present. Through the application of this method, the concentration of sulfate ions (SO₄^2-) known to be the primary factor responsible for scale formation, can be accurately determined within the produced water. The evaluation of SO₄^2-concentration in both synthesized and true seawater obtained from the shores of Aberdeen, Scotland revealed respective values of 2776 mg/L and 2834 mg/L whereas the detection limit (DL) and the quantification limit (QL) were 50 ppm and 100 mg/L, respectively. The approximated DL for SO₄^2-in both synthetic seawater fraction and natural seawater fraction stands at 100 mg/L for each, corresponding to a composition of 5% seawater and 95% formation water. Conversely, its QL is approximated to be 414 mg/L and 500 mg/L, respectively, aligning with compositions of 10% seawater and 90% formation water. Notably, the addition of supplementary ions within the water samples has no impact on the instrument’s discernment regarding identifying and quantifying the amount of SO₄^2- present. Finally, by plotting the correlation between the actual and measured concentrations, a strong relationship between the two sets of data was uncovered, affirming the potential of FTIR as a rapid, uncomplicated, reliable, and cost-effective approach for evaluating seawater breakthrough occurrences.

A double blade splitting machine for Cyperus sedge culms (Cyperus corymbosus Rottb.)

2024-09-30T14:54:49+07:00

Sedges are a natural material that can be made into mats and other products to replace plastic. Preparing sedge threads includes harvesting, sorting by size and color, splitting, and finally drying them. Splitting is an important step because using equipment can increase process speed. The objective of this research is to design, build, test, and evaluate the performance of a sedge-splitting machine. The main feature of this machine is its two pairs of blades, 7 mm apart, arranged in a perpendicular manner. This allows splitting the sedges into four threads while simultaneously removing the core. Five sedge clums are simultaneously processed. The machine was tested at three roller speeds, 197, 205, and 214 rpm. Its speed is not constant but varies between the lowest and highest speeds. This process was compared to one employing manual labor. The quantity of sedge threads that the machine split varied with its speed. Therefore, the machine’s overall production was estimated by averaging its output at these three speeds. It could process an average of 649.3 sedge culms/h, which is more than three times faster than the manual process, 195.4 culms/h. This research also studied the effect of culm moisture content on the machine’s operating capacity. It was found that moisture content had no effect on the working rate of either the machine or the manual process. However, it did affect the machine’s efficiency. Processing freshly cut culms enabled the highest efficiency, 90%. However, the manual process showed an average 98% efficiency when the culms had a moisture content between 84-90%.

Europium-doped soda lime borosilicate glass from agricultural wastes: Physical, structural, and optical properties

2024-11-28T21:11:45+07:00

Wheat husks and eggshells, while often regarded as waste, are available in millions of tons. Despite their status as by-products, these materials possess significant potential for various applications. Thus, this study explores the possibility of agricultural waste as a source of bio-silicate materials for glass production. Bio-silicate was extracted from leached wheat husks, and lime was obtained from eggshells. Europium-doped soda-lime borosilicate glasses were fabricated using the melt-quenching method having a chemical composition of 30Na₂O-(40-x)B₂O₃-20SiO₂-10CaO-xEu₂O₃ wherein x varied from 0.01 to 0.05 mol%. Chemical oxide compositions of wheat husk ash (88.64% SiO₂) and calcined eggshell (91.30% CaO) were determined using X-ray fluorescence (XRF). Functional groups were confirmed by Fourier transform infrared (FTIR) spectroscopy. It was confirmed by X-ray diffraction (XRD) that the glasses were amorphous. The glasses exhibited 2.61 to 3.03 g/cm³ densities and molar volumes of 10.91 to 14.37 cm³, increasing with higher europium doping levels, indicating a denser glass network. Optical features were characterised using Ultraviolet-visible (UV-Vis) spectroscopy, revealing direct band gaps ranging from 3.55 to 4.90 eV and indirect band gaps from 0.40 to 1.99 eV. Glasses doped with 0.05 mol fraction Eu₃⁺ showed the highest density, molar volume, and Urbach energy, suggesting suitability for UV-absorbing materials and high-energy photonic devices. Due to their unique emission properties under various excitation wavelengths and adaptable optical band gaps, the europium-doped borosilicate glasses derived from agricultural waste are useful in optical devices such as white LEDs.

Towards the decarbonization of residential buildings through roof External Thermal Insulation (ETI) in arid zones: A case study

2024-10-18T11:20:53+07:00

The objective of this article is to present a range of effective thermal insulation techniques for flat roofs, based on natural insulating materials, in order to reduce excessive energy consumption used for heating and air conditioning, improve the thermal comfort of occupants, reduce the high costs associated with energy bills, and minimize the emissions of harmful CO₂ gas from buildings. In this context, the methodology followed involves studying an existing residential apartment located in an arid region. To do this, three combinations of external thermal insulation systems (ETI) have been proposed: a hot roof system, an inverted roof system, and a combined roof system, supported by a numerical simulation carried out with natural-based thermal insulation systems (wood fiber and expanded cork). Subsequently, an economic study was conducted for all the tested systems. The numerical results obtained show that the combined roof thermal insulation system, with double insulation using wood fiber (3 cm) and expanded cork (4 cm), is the optimal solution, yielding savings of 7736.83 DZD per year. Additionally, the reduction in energy consumption (kWh/m². year) and CO₂ carbon emissions (kg/year) were approximately 52.34% and 40.65% respectively.

The improvement of germination method for producing the germinated brown rice using a water spraying system with a revolved sieve

2024-10-10T09:31:07+07:00

Water soaking is an important method in germinated brown rice (GBR) production that causes fermentation, leading to an unpleasant smell of GBR. In this research, a water spraying system with a revolved sieve is applied to produce the GBR. The increased speed and time of spray break led to higher moisture content and water absorption. The spray break of 30 min and revolved speed of 15 rpm provided the shortest time to obtain the paddy with a moisture content of 30% (w.b.). The incubation pattern with a revolved sieve and water spray provided the shortest incubation time for 90% germination. When producing the GBR with a water spraying system with a revolved sieve (GBR-WSSRS), it had a lower number of microorganisms compared to the GBR with a water soaking (GBR-WS), leading to higher scores of overall acceptability. However, the GBR-WSSRS had a lower GABA content than the GBR-WS.