Engineering Journal (Faculty of Engineering, Chulalongkorn University, Bangkok)
Not a member yet
1223 research outputs found
Sort by
Optimization–based Solution for Reducing Water Scarcity in the Greater Chao Phraya River Basin, Thailand: Through Re–operating the Bhumibol and Sirikit Reservoirs Using Non–linear Programming Solver
Get PDFWater scarcity problem in Thailand has been intensively addressed over decades to realize its impact and to promote a systematic modernization framework and technological advancement for effective and sustainable water resources management. Accordingly, the optimization–based solution with three scenarios was conducted by aiming to reduce water scarcity in the Greater Chao Phraya River Basin through re–operating the Bhumibol (BB) and Sirikit (SK) Reservoirs using non–linear programming solver. The results reveal that water deficit can be definitely reduced by the implementation of Fmincon optimization. Water allocation between BB and SK Dams was shared in the existing 0.44:0.56 ratio for scenario 1 and current operation and 0.45:0.55 ratio for scenario 2 and 3. The proportion of water released from SK Dam in dry years and normal years is still higher than BB Dam for all scenarios and higher than the current operation particularly in normal years. However, Fmincon optimization proposes to supply water from BB Dam higher than SK Dam in wet years with the average water sharing ratio of 0.54:0.46, 0.55:0.45, and 0.55:0.45 for scenario 1, 2, and 3, respectively. This leads to the increase in water storages of two main dams for a long–term reservoir operation
Prescription Based Recommender System for Diabetic Patients Using Efficient Map Reduce
Get PDFHealthcare sector has been deprived of leveraging knowledge gained through data insights, due to manual processes and legacy record-keeping methods. Outdated methods for maintaining healthcare records have not been proven sufficient for treating chronic diseases like diabetes. Data analysis methods such as Recommendation System (RS) can serve as a boon for treating diabetes. RS leverages predictive analysis and provides clinicians with information needed to determine the treatments to patients. Prescription-based Health Recommender System (HRS) is proposed in this paper which aids in recommending treatments by learning from the treatments prescribed to other patients diagnosed with diabetes. An Advanced Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering is also proposed to cluster the data for deriving recommendations by using winnowing algorithm as a similarity measure. A parallel processing of data is applied using map-reduce to increase the efficiency & scalability of clustering process for effective treatment of diabetes. This paper provides a good picture of how the Map Reduce can benefit in increasing the efficiency and scalability of the HRS using clustering
Consistent and Sustainable Supplier Evaluation and Order Allocation: Evaluation Score based Model and Multiple Objective Linear Programming Model
Get PDFThis paper is to develop an integrated approach of supplier evaluation and order allocation to suppliers that suggests the buyer to place more orders to the supplier that has higher evaluation score (consistent order allocation) considering sustainability issues including economic, social, environmental, and disruption of supply chain issues. The proposed approach is handled by an Evaluation Score based Linear Programming (ESLP) Model. Performances of ESLP model is compared with those of Multiple Objective Linear Programming (MOLP) model that does not explicitly consider the evaluation scores of suppliers for order allocation. Experimental results show that ESLP model offers consistent order allocation while MOLP model offers inconsistent order allocation. Moreover, MOLP model has different priorities of suppliers for order allocation when the customer demands are changed. Inconsistent order allocation makes the purchasing process nontransparent, unexplainable, and susceptible for biased decisions. ESLP and MOLP models generate compromised solutions that are nondominated. They are better and worse for some performances. This paper emphasizes a need of further research that develops consistent order allocation methods
Experimental Evaluation on Engineering Properties and Drying Shrinkage of No-Cement Mortar Produced by Alkaline Activation of Fly Ash-Slag Mixtures
Get PDFTurning locally available industrial by-products such as fly ash (FA) and ground granulated blast-furnace slag (GGBFS) into cement-free materials has been recently received much attention from researchers. Following this trend, the present study produces alkali-activated mortars (AAFS) using a mixture of FA and GGBFS as a precursor activated by an alkaline solution of sodium hydroxide and sodium silicate. Five AAFS mixtures were prepared for the evaluation of engineering properties, drying shrinkage, and microstructural observation using various FA/GGBFS ratios of 30/70, 40/60, 50/50, 60/40, and 70/30. The experimental results show that the proportions of FA and GGBFS significantly affected the performance of the AAFS in both fresh and hardened stages. Higher GGBFS content resulted in a reduction in flowability and higher fresh unit weight. The GGBFS-rich AAFS developed its mechanical strength faster than the FA-rich AAFS and the strength gain of the GGBFS-rich AAFS was significantly higher than that of the cement-based mortar at only 1-day old, confirming the applicability of AAFS as a structural material and its potential to replace cement in the no-cement mortar production. The AAFS sample incorporating 60% of GGBFS and 40% of FA exhibited the highest strength, lowest water absorption, and less drying shrinkage with a relatively dense microstructure among the AAFS samples
Influence of Soaking Time on Deep Cryogenic Treatment of CuCoNiBe Alloy
Get PDFDeep cryogenic treatment (DCT) was investigated at different soaking times to determine the microstructural transformation and mechanical properties of copper beryllium (CuCoNiBe) alloy. Lattice shrinkage/distortion resulting from differences in thermal contraction/expansion between the alpha phase and gamma phase caused internal stress, with large atomic dislocations leading to the formation of beryllides. Average beryllide size decreased with increasing DCT time by a maximum of 37% compared to non-DCT because new small beryllides were formed. Beryllides increased and distributed in the ⍺ phase with longer soaking time. Highest beryllide number and volume fraction found at the longest soaking time of 72 h were approximately 200% and 5%, respectively higher than for non-DCT. Increasing the number of beryllides played an important role in enhancing hardness and wear resistance. Maximal increase in hardness at 12% was observed for 72 h DCT, with reduction in wear volume of 30%. Residual stress as compressive stress showed high variation, with uneven distribution over the DCT sample. Impact strength of the DCT samples decreased by 50%. Analysis of fracture surfaces suggested that beryllide shape and beryllide at the grain boundaries played important roles in reducing fracture resistance. Thermal conductivity measurements of DCT-12 h and DCT-72 h samples indicated microstructural change, with the DCT-72 h sample recording a 2% drop in thermal conductivity compared to non-DCT
Prediction of PM2.5 Dispersion in Bangkok Pathumwan District) Using CFD Modeling
Get PDFCity configuration, meteorological conditions and emission source are the important factors affecting the dispersion and concentration of pollutants within urban street canyon. The dispersion of PM2.5 emitted from traffic in Pathumwan district, Bangkok which has the characteristics of street canyon was predicted using a Computational Fluid Dynamics (CFD) model with the RANS standard k-ε turbulence model. The studied area covers the area with high aspect ratio such as the shopping center area with the skytrain structure. The Discrete Phase Model (DPM) was used for simulating the PM2.5 injection and dispersion. The concentrations of PM2.5 were investigated under different conditions to demonstrate the effects of skytrain structure in the street canyon, meteorological conditions, and city lockdown due to the COVID-19 pandemic on PM2.5 concentration. The numerical model was validated with the measured data from the air quality monitoring station of the Environment Bureau, Bangkok. The reduction of emission rate during the city lockdown causes the PM2.5 concentration to decrease by 1.35 times from the normal time. In addition, the city configuration with the skytrain structure located between the tall buildings results in higher PM2.5 concentration than the case without the skytrain structure in Pathumwan district by around 1.2 times. Moreover, the meteorological conditions must be considered, especially wind speed and direction. Finally, the results obtained from the simulation will be used for proposing the guidelines to reduce the concentration of PM2.5
A Review of Sandwich Composite Structures with 3D Printed Honeycomb Cores
Get PDFSandwich structures have picked up ubiquity in engineering applications due to its lightweight nature, high bending stiffness, high fatigue resistance and ability to absorb energy. It is difficult to retain the lightweight execution of a sandwich construction whereas moreover getting great bending stiffness and strength. The mechanical characteristics of Acrylonitrile Butadiene Styrene (ABS) and Polylactic Acid (PLA) produced via additive manufacturing are investigated in this article. The most often used materials for cores are ABS and PLA. ABS appears to have more flexural strength and elongation before failure than PLA. The behaviour of cores is also examined. The bending stiffness was discovered to be enhanced by the re-entrant core which is the core that exhibits negative Poisson’s ratio or auxetic behaviour. The bending and fatigue performance of sandwich structures is controlled by the core densities, core designs, component materials, face sheet thickness, and face sheet stacking sequence. Furthermore, the findings revealed that finite element analysis may be utilized to investigate the mechanical characteristics of sandwich constructions with honeycomb cores. The discoveries presented here open the path for the development of a new class of sandwich structures, greatly expanding their design space and potential future applications
A Parametric Study of the Insulation Thickness and the Emissivity of the Reflector during the Billet Transport
Get PDFAn investigation of the effect of the insulation thickness and the emissivity of the stainless steel reflector of the covering material during the billet transport on the amount of saving energy is presented. A mathematical model of the heat transfer from the billet through the covering material to the environment is developed. The fully implicit scheme of finite difference equations is employed. The numerical solution is obtained by using the linearization technique. The results are presented in terms of a parametric study of the average temperature of the billet and the amount of saving energy. The data from field measurement is used to verify the numerical result in case of the billet transport without the covering material. A good agreement is observed between those two. As the insulation thickness increases, the temperature drop of the billet during the transport decreases due to the lower heat loss to the environment. This result leads to increasing of the amount of saving energy increases from 219.7 to 272.5 MJ as the insulation thickness increases from 12.5 to 50 mm. As the emissivity of the stainless steel reflector increases, the temperature drop of the billet during the transport increases, corresponding to the decreasing amount of saving energy. In case of the 12.5-mm insulation thickness, the effect of the emissivity on both temperature drop and the amount of saving energy is observed when the emissivity is lower than 0.3. The effect of the emissivity on those two is less significant when the insulation thickness reaches 50 mm. The amount of energy saving is approximately 273 MJ regardless of the emissivity of the stainless steel reflector
The Impact of Organisational Climate on Employee Engagement and Performances in a Multinational Oil and Gas Exploration and Production Company in Thailand
Get PDFThis research aims to support the company to improve employee engagement by exploring antecedent issues that have not been previously captured by the company survey. Organisation climates are chosen since they focus on intrinsic values such as purpose and work harmony, i.e. internal communication, learning and development, and perceived organisation support, to close the engagement gap and translate them into feasible actions. In addition, the study further explores the performance indicator beyond conventional measures. Conventionally, the benefit of engagement is measured through self-rating questions, e.g. organisational commitment and job satisfaction. However, it lags off the link between those measurements and tangible benefits. The internal survey data are analysed by the structural equation model (SEM) to identify the relationship between antecedents and consequences, and then linear regression is applied to correlate each factor to the observed performance. To provide a complete understanding of each relationship, interviews are conducted to find an insightful view of the model outcome. The result emphasises the strong relationship between engagement and organisational commitment, especially organisation engagement. Learning and development show contradicting results from the previous study and the outcome cannot clear the myth that higher engagement leads to better performance but it has shown a promising correlation between engagement and performance. The finding could help the management team and HR department better understate the priority for each antecedent which also links to the benefits of engagement that ultimately supports future HRM
Pushing the Accuracy of Thai Food Image Classification with Transfer Learning
Get PDFFood image classification is a challenging problem, the solution of which can be of great benefit to many real-world applications such as nutrition and allergy estimation. Most of the previous studies proposed to use variations of convolutional neural networks to tackle the problem. However, due to the limited number of annotated food image datasets, there is still some room for improvement, especially in terms of accuracy and speed. Generally speaking, neural networks trained to solve image classification problems on a small dataset benefit from utilizing the weights of the networks that have been pre-trained on a large image classification dataset such as ImageNet. In this paper, we compare the trade-offs between training networks from scratch, deploying pre-trained networks as feature extractors, and fine-tuning the networks for Thai food image classification. By utilizing Transfer Learning with EfficientNetV1, we were able to achieve higher accuracy for Thai Food Image Classification on the largest publicly available Thai food image dataset, THFOOD-50. In particular, our proposed method improves upon the accuracy of the previous state-of-the-art method from 84.06\% to 91.49\% while maintaining the speed for the prediction at 103 ms and 1205 ms for GPU and CPU, respectively