Science and Engineering Connect

Erratum to Prediction of Ultimate Tensile Strength of Ductile Iron by Artificial Intelligence

Witchuda Thongking — 2025-12-29

วารสาร Science and Engineering Connect ปีที่ 47 ฉบับที่ 3 กรกฎาคม-กันยายน 2567 หน้า 244-256

การแก้ไขบทความ : การทำนายความแข็งแรงดึงของเหล็กหล่อเหนียวด้วยปัญญาประดิษฐ์

Erratum : Prediction of Ultimate Tensile Strength of Ductile Iron by Artificial Intelligence

รชยา สินธุสิงห์, ธาริกา พันธุ์เลิศ, ยุทธนา น้อยเมือง, อุษณีย์ กิตกำธร, ภูษิต มิตรสมหวัง, รัตน บริสุทธิกุล^*Rotchaya Sinthusing, Dharika Phanlert, Yutthana Noimueang, Usanee Kitkumthorn, Pusit Mitsomwang, Rattana Borrisutthekul^*สำนักวิชาวิศวกรรมศาสตร์ มหาวิทยาลัยเทคโนโลยีสุรนารี จ.นครราชสีมา ประเทศไทย
Institute of Engineering, Suranaree University of Technology, Nakhon-Ratchasima, Thailand
*Corresponding author E-mail: rattana@g.sut.ac.th
URL: https://ph04.tci-thaijo.org/index.php/SEC/article/view/7699

ข้อผิดพลาดที่พบ :
ผู้เขียนขออภัยสำหรับข้อผิดพลาดในการพิมพ์ชื่อภาษาอังกฤษผู้เขียนร่วม 1 ท่านในไฟล์บทความที่เผยแพร่ โดยได้แก้ไขให้ถูกต้องแล้ว ดังนี้ จากเดิม Dharika Phanlert เปลี่ยนเป็น Tharika Puntlert

วิชชุดา ธงกิ่ง
Witchuda Thongking
คณะวิศวกรรมศาสตร์ มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าธนบุรี จ.กรุงเทพฯ ประเทศไทย
Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
รชยา สินธุสิงห์, ธาริกา พันธุ์เลิศ, ยุทธนา น้อยเมือง, อุษณีย์ กิตกำธร, ภูษิต มิตรสมหวัง, รัตน บริสุทธิกุล^*Rotchaya Sinthusing, Tharika Puntlert, Yutthana Noimueang, Usanee Kitkumthorn, Pusit Mitsomwang, Rattana Borrisutthekul^*สำนักวิชาวิศวกรรมศาสตร์ มหาวิทยาลัยเทคโนโลยีสุรนารี จ.นครราชสีมา ประเทศไทย
Institute of Engineering, Suranaree University of Technology, Nakhon-Ratchasima, Thailand
*Corresponding author E-mail: rattana@g.sut.ac.th

Utilization of Spent Mushroom Substrate for Biomass Fuel Pellets Production: Effect of Moisture Content on Pelletization

Siwakorn Nonsawang — 2025-12-26

Background and Objectives: Spent mushroom substrate (SMS) is an agricultural byproduct generated in large quantity from mushroom cultivation, posing environmental concerns if not properly managed. Converting SMS into biomass pellets presents a sustainable waste management approach that enhances resource utilization and supports circular bioeconomy principles. However, moisture content significantly affects the pelletization process and the final product quality. This study aimed to investigate the impact of moisture content level on the production efficiency and quality of biomass pellets derived from SMS, focusing on bulk density, mechanical durability, calorific value and specific energy consumption.

Methodology: SMS obtained from mushroom farms in Thailand was utilized. The substrate was sun-dried for 1, 3, 6, 7, and 9 days to achieve different moisture content levels, i.e., MC32 (31.52%), MC27 (27.05%), MC21 (21.41%), MC15 (15.23%) and MC8 (7.77%), respectively. Each dried substrate was pelletized using a rotary die pellet mill with a die diameter of 6 mm. The produced biomass pellets were evaluated for several properties, including production efficiency, pellet formation capacity, dimensional characteristics, bulk density, mechanical durability and calorific value. All results were compared with relevant standards to determine the optimal moisture content for biomass pellets production.

Main Results: Moisture content significantly influenced both production efficiency and pellet quality. High moisture content (MC32) resulted in the highest production rate (27.78 kg/hr) and the lowest energy consumption (92.26 Whr/kg); mechanical durability of the pellets was nevertheless below the standard value. Conversely, low moisture content (MC8) provided pellets with sufficient durability, but resulted in low production efficiency and the lowest pellet formation percentage (78.56%). The optimal moisture content range was MC15–MC21, which resulted in high pellet formation (90.72%), maximum mechanical durability (97.79%) and bulk density of 540–568 kg/m³, aligning with EN ISO 17225-6 standards, although slightly below the Thai Industrial Standard TIS 2772-2560 for bulk density. The calorific value ranged from 14.63–14.68 MJ/kg, exceeding all minimum standard requirements.

Conclusions: Production of biomass fuel pellets from SMS is plausible, with moisture content playing a crucial role in determining the pellet quality. Moderate moisture levels (15–21%) were found to be optimal for pelletization as they enhance pellets formation, mechanical durability and bulk density, while also providing calorific values that meet standard requirements. Controlling the moisture content of a raw material is a key strategy for improving the pellets production process from this type of agricultural waste.

Practical Application: Utilizing SMS as a raw material for the production of biomass pellets as an alternative renewable energy source exhibits potential for the reduction of waste from the mushroom cultivation industry. The present study provides a guideline for improving the efficiency of biomass pellets production and may be extended to other agricultural residues in the future. Furthermore, it aligns with the principles of circular economy by promoting efficient resource utilization.

PM2.5 Emission Inventory of Urban Areas in Nakhonchaiburin Provincial Cluster

Sudjit Karuchit — 2025-12-26

Background and Objectives: Particulate matter smaller than 2.5 microns (PM_2.5) is a major problem in the country, having adverse effects on public health and causing much concern to all parties. Although some local areas have not yet encountered the problem, as the economy develops, they may eventually face air quality issues. Therefore, building the capacity to cope with the PM_2.5 problem for the public and local agencies, such as municipalities and towns, is important and necessary for their operations and to prevent the problem from increasing in the future. The present research aimed to create an inventory of PM_2.5 sources and emissions of urban areas in the Nakhonchaiburin provincial cluster, namely, Nakhon Ratchasima Municipality, Chaiyaphum Municipality, Buriram Municipality, and Surin Municipality, and to study and compare the proportion of PM_2.5 emissions between sources, as well as spatial analysis by creating PM_2.5 emission maps of the study areas.

Methodology: The methodology consisted of reviewing relevant studies and selecting significant sources, which were divided into 3 main groups: (1) point type, including industrial plants, temples, and large establishments; (2) mobile type, including road transport and rail transport; and (3) area type, including residences and commercial buildings, markets, and agricultural areas. Then, data on sources were collected through field surveys, questionnaire data collection, contacting agencies for information, and traffic volume surveys. Emissions were estimated using the emission factor method, with the base year set to be 2023.

Main Results: Road transport group was the most important source of PM_2.5 emissions in all study areas, accounting for more than half of all sources, or 51-61 percent. The second most significant source in all areas was the residential and commercial group, accounting for 19-33 percent. The third most significant source was the industrial factory group in 3 areas, except for Chaiyaphum Municipality, which is an agricultural area group. The sources that contributed the least to PM_2.5 emissions in all areas were temples, large businesses, rail travel, and markets. When considering the pollutant emission rate per unit area, it was found that Buriram Municipality, which has the smallest area of only 6 sq.km, exhibited the highest pollutant emission rate per area, at 2,384 kg/sq.km/year, while Nakhon Ratchasima Municipality had a slightly lower pollutant emission rate per area at 2,239 kg/sq.km/year. Surin and Chaiyaphum municipalities ranked 3^rd and 4^th with values of 1,725 kg/sq.km/year and 1,004 kg/sq.km/year, respectively. The pollutant emission rate per capita, ranked from the highest to the lowest, was Chaiyaphum, Nakhon Ratchasima, Buriram, and Surin municipalities with values of 0.84, 0.72, 0.61, and 0.53 kg/person/year, respectively.

Conclusions: The main sources of PM_2.5 emission were the road transport group, the residential and commercial group, and the industrial group. The emission rate per unit area was in the range of 1,004 - 2,384 kg/sq.km/year. The emission rate per population was in the range of 0.53 - 0.84 kg/person/year. The results of the spatial emission analysis for large municipalities clearly show that higher emission areas were along the main roads.

Practical Application: Results are useful for the four municipalities, government agencies involved in air quality management, public health agencies, academics, and the general public. Knowing the pollutant main sources and emission rates would lead to effective measures to reduce emissions and solve PM_2.5 problem in the study areas.

Navigating the Transition to Industry 5.0: Risk and Resilience in Technology Startups

Nattida Tachaboon — 2025-12-26

Background and Objectives: The rapid advancement of digital technologies has driven Industry 4.0, yet emerging research emphasizes a shift toward Industry 5.0, which prioritizes human-centric, sustainable, and resilient innovation. Despite the growing importance of this transition, there is limited understanding of how early-stage technology startups manage risks while aligning with these Industry 5.0 principles, particularly in emerging economies. This study aims to examine how Thai technology startups perceive and navigate risks associated with product development, market adoption, and ecosystem dependencies in the context of Industry 5.0.

Methodology: A qualitative multiple-case study design was employed to explore these issues in depth. Seven early-stage technology startups across diverse sectors, including education, healthcare, construction, and alternative medicine, were purposively selected to ensure variation in digital platforms and technology-based products. Semi-structured interviews were conducted with founders or co-founders, focusing on entrepreneurial motivations, business development processes, perceived risks, adaptation strategies, and ecosystem support. The interview data were analyzed using thematic coding, which involved initial coding to identify recurring challenges and risk factors, axial coding to group codes into broader themes, and interpretive analysis to align findings with the emerging Industry 5.0 framework. Themes were validated through cross-checking by multiple researchers and iterative comparison across cases.

Main Results: The findings reveal that startups integrate human-centric design, sustainability-oriented innovation, and resilience-building strategies to navigate uncertainty, while simultaneously confronting technology and ecosystem-related risks. Human-centric approaches allow startups to align their offerings with real user needs, often informed by the founders’ personal experiences and iterative feedback from target users. Sustainability considerations, including social and economic dimensions, contribute to long-term viability by supporting small businesses, promoting equitable access, and addressing societal challenges. Resilience mechanisms, such as pivoting, minimum viable product testing, and learning from failure, enhance startups’ adaptive capacity and ability to respond to environmental shocks. Technology-related risks, particularly the recruitment and management of skilled technical talent, along with limitations in funding and mentorship, were identified as critical ecosystem vulnerabilities. Access to incubator support, structured mentorship, and institutional resources played a central role in mitigating these risks and enabling startup survival.

Conclusions: These findings contribute to entrepreneurship and innovation literature by demonstrating that Industry 5.0 provides a comprehensive framework for understanding startup risk management that extends beyond purely technical and financial dimensions. The study highlights the importance of integrating human-centricity, sustainability, and resilience in entrepreneurial practice, while also emphasizing the role of ecosystem support in emerging economies.

Practical Application: The insights offer practical implications for entrepreneurs, incubators, and policymakers seeking to foster sustainable and resilient startup ecosystems, suggesting that targeted support mechanisms, combined with iterative and adaptive innovation strategies, can enhance both startup performance and societal impact.

Health Index Calculation for Distribution Transformer and Fault Identification

Sopa Sae-Heng — 2025-12-29

Background and Objectives: Distribution transformers are critical components in electrical power systems. Therefore, regular annual maintenance is essential to prevent operational failures. In typical distribution networks, a large number of transformers are installed, each exhibiting different conditions depending on age and operating environment. Identifying the condition of each transformer enables utilities to plan appropriate maintenance activities and frequencies, thereby reducing costs and improving operational performance. This research aimed to propose a method that can be used to determine the health index of distribution transformers to support the development of condition-based maintenance plans.

Methodology: The health index of distribution transformers was assessed by employing the weighted scoring method and the fuzzy logic method. The factors used for the assessment included dissolved gases in oil, breakdown voltage strength of the insulating oil, moisture content in oil, and transformer service age. Both methods were applied to the condition monitoring data obtained from 23 distribution transformers. Furthermore, patterns of internal transformer faults were identified by using the IEC gas ratio method integrated with fuzzy logic.

Main Results: The analysis of transformer health indices obtained from both methods reveals that consistent condition levels were produced for 6 transformers, while the remaining 17 units showed discrepancies. Among these, 15 transformers were classified by the fuzzy logic method as having worse health levels by one grade level compared with the weighted scoring method; 2 transformers were classified as worse by two grade levels. The fuzzy logic approach was found to provide more appropriate health-level classifications as the weighted scoring method relies on fixed boundary ranges for each factor, whereas fuzzy logic enables more flexible boundaries suitable for multi-factor evaluation without requiring rigid criteria. In addition, the fault pattern analysis indicates that internal faults occurred in five of the distribution transformers.

Conclusions: Regular maintenance of distribution transformers is crucial. Implementing condition-based maintenance plan can help reduce outages and enhance system reliability. While the weighted-score method is simpler and easier to implement, it is less effective in differentiating conditions when input factors are ambiguous. In contrast, fuzzy logic more accurately reflects real transformer conditions. Integrating the IEC gas ratio method with fuzzy logic enhances the accuracy of fault identification, enabling more effective maintenance planning.

Practical Application: The transformer health index results can support prioritization of maintenance activities based on transformer criticality, particularly for distribution utilities or facilities with a large number of transformers. This approach helps reduce maintenance time and labor requirements and promotes more cost-effective resource allocation.