Engineering Journal (Faculty of Engineering, Chulalongkorn University, Bangkok)
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CO2 Capture Performance on Wall Paint Modified by K2CO3 and CA(OH)2 for Added-Value CO2 Capture Paint Development
Get PDF“Smart paint” is an application that can adsorb CO2 contributed by transportation in urban areas. This research investigated the adsorbent and their effect which can enhance the CO2 adsorption in paint. CO2 capture was tested after drying coated paint. The apparatus was designed as a layered box under open indoor system for investigating the effect of modified paints. The paints were modified by adding K2CO3 or Ca(OH)2. In order to carry out the experiment, 100 g of modified paint were applied to the board inside the CO2 adsorption unit while the gas flow rate and moisture were controlled at the ambient temperature. The results showed that the increase in solid adsorbent loading in the modified paint with K2CO3 could raise the adsorption capacity of the paint. However, the adsorption capacity was limited by some properties in the paint, such as glossy. The adsorbent loading influenced the long-term stability of the paint. On the other hand, the modified paint with Ca(OH)2 could adsorb CO2 with higher capacity and maintain better long-term stability than that with K2CO3. Also, the effect of gas flow rate and humidity showed that the increase in either flow rate or humidity could also enhance CO2 adsorption capacity. The best case of modified paint with Ca(OH)2 in this study could adsorb CO2 at 9.61 mg-CO2/g-paint, which is equivalent to 4.58 g-CO2/m2, while the conventional paint (non-modified paint) could adsorb CO2 of approximately 0.06 g-CO2/m2. Finally, this study demonstrates that modified paint with K2CO3 and Ca(OH)2 can be a new feature to add value to the paint industry
Inventory Policy Improvement with Periodic Review for Perishable Goods: A Case Study of a Retail Coffee Shop in Thailand
Get PDFInventory management is a fundamental component of successful retail operations. Effective techniques in retail inventory management are important in fulfilling customer demands, minimizing costs, and enhancing profitability for business in the competitive environment. This study aims to improve the inventory management strategy for perishable goods in a Thai coffee shop case study. The primary goals include minimizing occurrences of inventory surplus or shortage and indicating the most suitable inventory management approach for each stock-keeping unit (SKU). The most efficient inventory strategy is determined by evaluating the total inventory costs, composing of waste costs, potential loss costs, and holding costs. To this end, computational experiments are employed, deploying three varied periodic inventory policies per SKU. These policies differ in term of utilizing mean weekly demand, average daily demand, and modifying delivery schedules and frequencies. In addition to exploring various policies, the service level for each SKU is adjusted according to profit-cost ratio of each SKU to determine the most suitable service level corresponding to the most effective inventory management strategy. Following the experiments, an effective inventory policy for each SKU is determined. Results show that the new proposed policies can reduce costs by 60.74%, or about 256,922 Baht yearly, compared to the current policy. The new policy, based on daily demand and delivery adjustments, leads to smaller order, more frequent deliveries, allowing the perishable goods to be more refreshed
Eco-Friendly Production of Decorative Concrete Blocks Using Coal Fly Ash and Coconut Husk Fiber Admixtures: Mixture D-Optimal Design Optimization
Get PDFThe present study investigates the production of decorative concrete blocks using coal fly ash and coconut husk fiber as admixtures to cement and sand. Using the Mixture D-optimal design, the decorative concrete blocks with a volume of 3,350 cm3 were produced by varying the amount of coal fly ash (2.33-28.33 wt.%) and coconut husk fiber (3-9 wt.%) while using a constant amount of 10% of cement and 58.67% of sand throughout the study. These were cured for 28 days and tested in terms of compressive strength, density, and water absorption capacity. Results revealed that the density of the produced decorative block at optimum conditions was 1153.27 kg/m3, which is lighter than the commercial one, which was 1165.39 kg/m3 because of the raw materials used. Meanwhile, a high water absorption capacity was recorded at 24.79%. Furthermore, the recorded compressive strength of 0.467 MPa of the produced block is higher than the commercial one with 0.453 MPa, which means that this can replace them, considering its lower production cost. This study presents an innovative approach to utilizing industrial waste materials and producing a new product that can reduce solid waste generation and environmental pollution
The Applications of Deep Learning in ECG Classification for Disease Diagnosis: A Systematic Review and Meta-Data Analysis
Get PDFThe supremacy of deep learning in artificial intelligence (AI) contexts, including image and speech recognition, computer vision, and medical imaging, among others, has established it as AI’s dominant approach. Several studies have been conducted on the use of deep learning in physiological signals, especially in ECG signals, in recent years, but there has been a lack of comprehensive review on the use of deep learning in ECG for biometric systems. This review is divided into two main sections: it provides a comprehensive bibliographic review of deep learning for ECG classification towards assisting in disease diagnosis in the first part while presenting an overview of the field, pioneers, and landmark studies. The second part offers comprehensive information on the subject, starting with the mathematical background of deep learning algorithms, the ECG signal processing, and the function of the heart. Using a PRISMA framework, 309 research papers were initially identified through specified keywords. After applying inclusion criteria, 90 articles were retained for detailed analysis, excluding 24 documents based on exclusion criteria EC1 and the remainder due to EC2. Key findings reveal that deep learning models achieve an average accuracy improvement of 10-15% over traditional methods, with convolutional neural networks (CNNs) and recurrent neural networks (RNNs) demonstrating superior performance in capturing complex ECG patterns. Through ECG databases, deep learning algorithms, assessment frameworks, metrics, and code availability, this review designs a systematic view from different perspectives to highlight the trends, challenges, and opportunities of deep learning for ECG arrhythmia classification. This paper’s goal is to contribute to the knowledge of both new and experienced researchers and practitioners in the field so that they can learn and understand the various processes involved in ECG signal processing using deep learning
Development of Stress-Based Forming Limit Curves for Predicting Crack Occurred during Deformation of UHSS DP980’s Parts
Get PDFUnless the strain-based forming limit diagram (e-FLD) is a useful tool in failure prediction for sheet metal, the complex shape can lead to a non-linear strain history, which e-FLD cannot adequately describe. This issue can be improved by using the stress-based forming limit diagram (s-FLD). However, determining the s-FLD through direct theoretical calculation is complex for beginners. This research uses a forming simulation software named PAM-STAMP to transform e-FLD to s-FLD by simulating the Nakajima stretch test. Because a yield criterion and hardening model are required in s-FLD determining procedures, this research also studies the accuracy of Hill48, Barlat89, and Yld2000 (in the absence of balanced biaxial condition test results) when integrated with the Swift hardening model and the Yoshida-Uemori (Y-U) model. The thickness and springback of an automotive part named Panel-RF FRT HRD were used to compare the accuracy of the forming simulation. It was found that the s-FLD predicted a crack on the workpiece better than the e-FLD, no matter whether there were differences in combination in yield criteria or hardening models. In the thickness and springback prediction, the r-based Hill48 couple Y-U model showed the best result, followed by Barlat89 and Yld2000, and was less accurate when Hill48 was coupled with the Swift hardening model
Enhancing Salt Purification in Oli’o Village, East Nusa Tenggara: A Case Study on Optimizing Precipitating Agent Addition Methods and Salinity Levels
Get PDFThe East Nusa Tenggara province holds significant potential as a contributor to Indonesia's national salt production. However, salt production in this area often needs to meet quality standards due to the high presence of impurities, such as calcium, magnesium, and sulfate. The limitations in technology and salt purity in Indonesia necessitate the country to import high-quality salt. This research aims to develop and enhance the purity of NaCl in the salt production of Oli'o Village through the addition of chemical substances. The method employed to improve salt purity is the precipitation method, utilizing precipitants Na2CO3, NaOH, and BaCl2 to precipitate calcium, magnesium, and sulfate impurities. The primary research involved adding precipitants in a 1:1 stoichiometric ratio and stirring for 45 minutes. Experimental variables encompassed the method of precipitant addition and salinity levels (16, 20, 23, 25 °Be). The analysis of impurity concentrations before and after the addition of precipitants was conducted through complexometric titration for calcium and magnesium, argentometric titration for NaCl, and turbidimetry for sulfate. The research reveals that the direct addition of the three precipitants yields the best results. In terms of salinity variations, it is observed that salinity at 16 °Be provides the highest purity compared to other salinity levels. The increase in salinity enhances impurity concentrations, decreasing the impurity removal percentage’
Human-like Trajectory Planning for Autonomous Vehicles Using Flexible Virtual Reference Points in Car Overtaking Motorcycle Scenarios
Get PDFTraditionally, autonomous vehicles (AVs) prioritize safety and efficiency when planning trajectories. However, the lack of human-like driving behaviors can limit user trust and acceptance. This can be attributed to difficulties in communication and interpreting the intentions of other road users, particularly during interactions like overtaking maneuvers. This study proposes the Flexible Virtual Reference Point (FVRP) concept, an enhancement to model-based trajectory planning that facilitates human-like behavior during overtaking maneuvers. FVRP decomposes complex overtaking maneuvers into distinct phases. Within each phase, the concept strategically positions virtual reference points by considering both driver comfort data and prevailing traffic constraints. These reference points are then connected to form a piecewise trajectory, ensuring the optimization of both safety and comfort during overtaking maneuvers involving a motorcycle (MC). The results demonstrate that FVRP successfully generates trajectories that achieve both safety and driver comfort across all experimental settings. Furthermore, the trajectories generated by FVRP exhibit characteristics that resemble human drivers, while maintaining safe and comfortable distances compared to those generated by driver behavior models and optimal control models. The success of FVRP concept in car-to-MC overtaking maneuvers suggests its potential for adaptation to other driving maneuvers where comfort and safety need to be balanced
Design and Evaluation of a Small Axial Flow Sunflower Thresher Unit
Get PDFThe design of a small axial flow sunflower thresher for tractor installation needs to be developed and evaluated to obtain performance data suitable for sunflower seed production in Thailand. Therefore, the purpose of this research is to design and evaluate a small axial flow sunflower thresher. The design of this unit was done according to the concept of a plant thresher machine, which consists of a set of rotor drums and threshing sieves. The performance of the small axial flow sunflower thresher was evaluated in terms of sunflower moisture in the range of 8.92 to 21.72%, feed rate in the range of 800 to 1,600 kg/h, and linear rotor speed of spike-teeth in the range of 6 to 14 m/s. Evaluation of the threshing unit showed that these three factors had a statistically significant effect on sunflower threshing performance. The optimal parameters to achieve maximal performance are as follows. First, the sunflower moisture content should be in the range of 12 to 14% on a wet basis. Second, the feed rate should be in the range of 1,000 to 1,200 kg/h. Last, the linear velocity of the threshing rotor should range from 10 to 12 m/s. This will achieve greater than 98% threshing efficiency with threshing losses and grain breakage of less than 2%. Future research should investigate additional factors influencing the separation and cleaning of axial flow sunflower thresher machines
Mechanisms of Secondary Flows in a Straight Square Duct under the Effect of Rotation
Get PDFLarge eddy simulation with a dynamic kinetic energy subgrid-scale model is employed to simulate three dimensional incompressible fully developed turbulent flows through the non-rotating and rotating straight square ducts at the fixed friction Reynolds number of 300 with various spanwise friction rotation numbers. The study of secondary flows in the duct using the mean streamwise vorticity transport equation is extended up to the friction rotation number of 20. As the duct is rotated, the contribution of streamwise vorticity terms remain the same role for each terms. The reciprocal contributions of streamwise vorticity terms are discovered over the duct cross-sectional area as the duct is rotated. The reciprocal contribution of the convection and rotation terms are found in the upper bottom corner and the lower top corner. On the lateral wall, the diffusion and rotation terms are balanced by each other. The equivalent exchangeable contribution of convection and diffusion terms is also found at upper top corner. The consistent contribution ratio between turbulence to rotation terms is found at reattachment points of small secondary flow cells. Furthermore, the rotational effects tend to drive the turbulent flows to be neutralized into the directional preference along the rotational axis
Automatic Ecological Control and Mathematical Growth Prediction Models for Lettuce Seedling Nursery System
Get PDFThe research introduces an automatic nursery machine designed to enhance lettuce (green oak salad) seedling cultivation by regulating environmental conditions. The goal is to produce higher-quality lettuce in unfavourable settings. The study outlines two key components of this automatic ecological system: the environmental device design for lettuce control and a mathematical growth prediction model to support the machine's operation. The first component employs an Arduino microcontroller equipped with sensors to manage and accelerate the growth of nursery lettuce. The second aspect concentrates on growth prediction modelling, which informs and regulates the lettuce seedling nursery system. The automatic ecological system is implemented and tested against the community enterprise (CE) method, demonstrating superior results. The lettuce seedlings cultivated with the automatic nursery machine exhibit thicker, stronger stems, larger leaves, and a higher germination rate of 9.18% compared to the CE method. For the mathematical growth prediction models, multiple regression models are developed to correlate lettuce height (H) and stem width (W) with temperature, relative humidity, and light intensity within the automatic nursery machine. The goodness-of-fit analyses indicate reasonable model fits with R2, MSE, and RMSE values of (W = 0.521, 0.093, 0.305, H = 0.604, 28.025, 5.294), respectively. Therefore, the automatic nursery machine offers an effective means to accelerate lettuce growth, potentially opening opportunities for large-scale industrial applications