Klongboonjit, Sakon

Preferred name

Klongboonjit, Sakon

Alternative Name

Klongboonjit, S.

Klongboonjit, Sakol

Main Affiliation

School of Engineering

sakon.kl@kmitl.ac.th

Now showing 1 - 10 of 25

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Effects of Demand Fluctuation and Mitigation Strategy in Low Voltage EV Charging Station by Battery Energy Storage System
(2021-01-01)
Jirasuwankul, Nirudh
;
Klongboonjit, Sakon
;
Manop, Chalermchat
In this paper, the effects of fluctuating demand on a low voltage electric utility system equipped with electric vehicular charging station have been studied and mitigation strategy has been proposed. By applying Battery Energy Storage System (BESS) and demand management (DMM), the stored electric energy during an off-peak period is controlled to supply the spiky demand during the EV charging period. Employing different approaches of discharging control, the BESS can response and shape the peaky demand into the smoothed one with an elevated load factor. Therefore, the result is that energy cost of EV charging has been reduced by cutting the demand charge during the on-peak period and the power quality of the system has also been improved. These findings are confirmed and illustrated by simulation results.
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PREDICTION OF STOCK PRICE USING HYBRID NEURAL NETWORK: A CASE OF COAL PRODUCTION COMPANY
(2025-01-15)
Kiatcharoenpol, Tossapol
;
Klongboonjit, Sakon
Stock market prediction is a critical issue in the field of economics. As machine learning technologies advance, an increasing number of algorithms are being utilized to forecast stock price movements. Nonetheless, predicting stock market trends remains a challenging task due to the inherent noise and volatility in stock market data. This paper addresses this challenge by proposing a novel hybrid neural network model designed to predict stock market prices using parameters related to commodity prices and stock indices. A case study company is mainly in coal production business in Thailand, which produce coal, sale, distribute and operate coal-fired power plants as well. The Multiple Linear Regression (MLR) and Back propagation neural network (BPNN) as traditional prediction technique are employed to comparatively investigate the accuracy and performance of the proposed HNN. Experiment results show that the prediction accuracy of HNN is superior to MLR but similar to that of the BPNN model. However, HNN has a good performance both in accuracy, speed and practice. It can help investing analysts and investors make their wise decisions.
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A lean manufacturing approach to waste minimization: a case of industrial rack plant
(2024-01-01)
Kiatcharoenpol, Tossapol
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Chaosamthong, Kirati
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Rawirangsun, Phuwit
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Klongboonjit, Sakon
The concept of Lean Manufacturing is a methodology focused on eliminating waste in various activities through the application of five key principles: defining value, mapping the value stream, ensuring flow, establishing pull, and pursuing perfection. Starting with the identification of value based on product sales, it was determined that industrial rack product is the top-selling product. A Current Value Stream Map (Current VSM) was created to illustrate value-adding activities within the production process and highlight sources of waste. The analysis identified inefficiencies in the production processes of side frames, beams, and packaging, which hindered the ability to meet the target takt time of 60 seconds per unit. Once the processes requiring improvement were identified, strategies for optimization were developed, leading to the creation of an Improved Value Stream Map (Improve VSM). The implementation of waste reduction measures resulted in a significant decrease in production cycle time, from 104 seconds per unit to 60 seconds per unit—a reduction of 42.31%, enabling compliance with the target takt time. Ultimately, the complete elimination of waste led to the creation of an Ideal Value Stream Map (Ideal VSM). This was achieved by incorporating pull system and continuous u-shaped line principles and consolidating workstations to minimize non-value-adding activities. These improvements not only enhanced production efficiency but also maximized the ability to meet customer demands and contributed to building a competitive advantage.
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Quality Evaluation of Wind Energy Data with Complete Linkage Clustering
(2022-12-01)
Klongboonjit, Sakon
;
Kiatcharoenpol, Tossapol
Although wind is an important free energy and most investors or farmers would like to invest in wind energy projects, they sometimes lack of the wind quality data in alternative areas for making decision. It should be definitely good to have some simple methods to classify the quality of wind energy for alternative areas. In this study, Complete Linkage method combining with the Euclidean distance calculation, which is really a simple method for users, is introduced to cluster wind energy quality of alternative areas. In a case of 13 alternative areas in the south of Thailand, the data of average wind velocity along 12 months from the secondary data source can be used to generate the initial distance matrix before continuously improving with Complete Linkage method. Finally, these 13 alternative areas are suitable clustered at C.D. = 5.11 into 3 groups of the low wind quality area with I.D. = 1.21, the medium wind quality area with I.D. = 1.41 and the high wind quality area with I.D. = 1.45
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Machine Component Clustering with Connection Correlation Method
(2021-06-30)
Kongsin, Tanongsak
;
Klongboonjit, Sakon
This study aims to introduce a new method for modular design, which is called Connection Correlation Method (CCM), to clustering machine parts with inter-part connection conditions to classify independent modules for the Industrial Printer Powertrain set with 105 components. With the CCM technique, module members would be appropriately assigned for each module so that machine modules are independent of their function. The CCM technique should first calculate the distance coefficient matrix with surface contact conditions of bonded, rough, separation, and frictionless for machine components. Finally, this distance coefficient matrix is used to generate the machine dendrogram with the dependency coefficient between 2.4945 and 3.5593. At the dependency coefficient of 2.7913, the 105 components of the Industrial Printer Powertrain set are clustered into 7 modules: Module 1 with 75 interconnection surfaces and 8 parts, Module 2 with 136 interconnection surfaces and 14 parts, Module 3 with 176 interconnection surfaces and 17 parts, Module 4 with 61 interconnection surfaces and 8 parts, Module 5 with 200 interconnection surfaces and 21 parts, Module 6 with 209 interconnection surfaces and 19 parts, and Module 7 with 18 interconnection surfaces and 18 parts. The results show that the CCM technique can apply to design a modular machine like DSM technique, and multitudinous connectivity factors can also be analysed together with general factors.
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Applying Shainin’s Tools to Process Improvement for Reducing Cracking Defect of Sanitary Product
(2023-01-01)
Kiatcharoenpol, Tossapol
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Seeluang, Rachan
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Klongboonjit, Sakon
s: The objective of this research attempt is to implement the DMAIC (Define, Measure, Analyze, Improve, Control) approach, a part of the Six Sigma methodology, in order to diminish the loss of sanitary ware during the production process. Specifically, this study focuses on addressing the issue of cracking defects that often occur in the production of sanitary wares after the firing process. Cracking defects manifest as gaps on the surface of sanitary wares, resulting in nonconforming and aesthetically inferior products. The AG27 Model, a highly demand toilet bowl, was selected as the case study due to its significant occurrence of cracking defects, accounting for 20% of the total defective units. The research utilizes the Six Sigma methodology in conjunction with Shainin's tools to identify the root causes and enhance production yield. The employed Shainin's tools include the Family of variation (FOV's), Concentration chart, Paired comparison, and Better and current (B vs C). The primary focus area of investigation involves the variation in the forming process and the design of the plaster mold. Through the use of the concentration chart, it was determined that the cracking defects predominantly appear along the border line between the rim and body of the toilet bowl. Subsequent experiments, based on paired comparison, confirmed that the design of the border line, which incorporates a hollow body shape, and the potential degradation of mold quality due to frequent use, were the two significant factors contributing to the cracking defects. In order to address these issues, a new design was implemented to enhance the connection between the rim and solid body of the AG27 toilet bowl. The practicality of this solution was validated through the utilization of the B vs C tool during the improvement phase. As a result, the occurrence of cracking defects decreased from approximately 4.0% to 1.92% during the control phase, representing a potential reduction of defects by over 50%.
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Machine components clustering with DSM and repeating method: Case study of a soil mixing machine
(2018-08-14)
Kongsin, Tanongsak
;
Klongboonjit, Sakon
In this study, components of the machine are analyzed to group all components into modular groups with a case study of a soil mixing machine. The study begins by creating a design structure matrix of all components. Next, the design structure matrix is transferred into a distance matrix of all components with Jaccard method. After that, the equation of complete linkage must be applied to change the distance matrix to a tree dendrogram for showing the relationship of machine components and dependent coefficient. With this tree dendrogram, six clusters are arranged:- the 1st cluster has 8 modules at the lowest dependent coefficient, the 2nd cluster has 7 modules, the 3rd cluster has 6 modules, the 4th cluster has 5 modules, the 5th cluster has 4 modules, and the 6th cluster has 2 modules at the highest dependent coefficient. Finally, the 1st cluster with 8 modules is considered to be the most proper cluster for this soil mixing machine by applying the repeating method to analyze all six clusters.
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Convection in deep vertically shaken particle beds. II. The relationship between convection and internal wave propagation
(2008-01-01)
Klongboonjit, S.
;
Campbell, C. S.
The convective motion in deep vertically shaken beds is not continuous but occurs only during a brief portion of a cycle that roughly repeats over three periods of vibration. The convection is coordinated by a series of waves that propagate through the bed, a compression wave formed as the flask's bottom pushes upward against the bottom of the bed, and two expansion waves: A Type 1 expansion wave that is the reflection of the compression wave and a Type 2 expansion wave that forms as the flask's bottom moves away from the bottom of the bed. Convection only is observed after Type 1 expansion wave has passed the convective zone, relaxing the stresses in the bed and leaving the particles free to move. However the convective motion is confined to the region above Type 2 wave and convection disappears as a Type 2 wave passes. © 2008 American Institute of Physics.
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LEAN IMPROVEMENT FOR PANTOGRAPH JACK PRODUCTION PROCESS USING VALUE STREAM MAPPING
(2023-05-01)
Kiatcharoenpol, Tossapol
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Wisayathaksin, Chayanan
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Chumongkon, Nopphawat
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Khuisangeum, Thapanon
;
Klongboonjit, Sakon
The objectives of this research are to study the current process and improve the production process of pantograph jack by using Lean manufacturing techniques. The Lean tool used is Value stream mapping (VSM). The study starts with collecting data from the upstream to downstream to create a Current-state VSM to identify wastes and problems. The first problem was the low efficiency of the production process due to the high lead time of 7 days 8 hours. The ratio of value-added time to the lead time was only 0.026%. The next problem was overproduction because the cycle time is 69.54 seconds/piece while the takt time is 140.15 seconds/piece, which is 46.62% of the takt time, resulting in the early stoppage of the production line to prevent over inventory of finished products. After identifying the problems, the next step is to define production process improvement approaches by creating a Future-state VSM. The tools for the improvement included the Kanban system, Supermarket, Line balancing, and Cellular manufacturing then a simulation model of both current and future states was created to compare the results before and after the process improvement. The result from the simulation shows that the total lead time was reduced to 4 hours 39 minutes or a decrease of 97.54% from the current state. The ratio of value-added time to the lead time was increased to 1.304%, which is 49.39 times more than before the improvements, and the cycle time was increased to 133.02 seconds/piece, which is 94.91% of the takt time.
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Convection in deep vertically shaken particle beds. III. Convection mechanisms
(2008-01-01)
Klongboonjit, Sakon
;
Campbell, Charles S.
Convection in a deep vertically vibrated two-dimensional cell of granular material occurs in the form of counter-rotating cells that move material from the walls to the center of the channel and back again. At least for deep beds, where for much of the cycle, particles are in long duration contact with their neighbors, convection only appears for a short potion of every third vibrational period. That period is delimited by the interaction of three types of internal waves, a compression wave, and two types of expansion waves. Four mechanisms are identified that drive the four basic motions of convection: (1) particles move upward at the center as the result of compression wave, (2) downward at the wall as a combined effect of frictional holdback by the walls and the downward pull of gravity, (3) from the center to the walls along the free surface due to the heaping of the bed generated by the compression wave, and (4) toward the center in the interior of the box to form the bottom of convection rolls due to the relaxation of compressive stresses caused by an expansion wave. Convection only occurs when the conditions are right for all four mechanisms to be active simultaneously. © 2008 American Institute of Physics.

Klongboonjit, Sakon

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