Journal of Science and Technology Sukhothai Thammathirat Open University

Cover

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Front part

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Content

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An Assessment Study on Greenhouse Gas Emission and Mitigation Measures: A Case Study of a Combined Cycle Power Plant in Rayong Province

2025-05-08T23:14:35+07:00

This study aims to investigate greenhouse gas (GHG) emissions resulting from various activities within a combined cycle power plant located in Rayong Province, Thailand. The assessment focuses on emissions data collected throughout the calendar year 2024, specifically from electricity generation processes. The evaluation follows the methodology outlined by the Thailand Greenhouse Gas Management Organization (Public Organization), which categorizes emissions into three scopes: (1) direct GHG emissions (Scope 1), (2) indirect GHG emissions from energy consumption (Scope 2), and (3) other indirect GHG emissions from external sources (Scope 3). The study utilizes both primary and secondary data, collected from the plant's operations between January and December 2024. The results reveal that the total GHG emissions from the plant's activities amounted to 424,240 metric tons of carbon dioxide equivalent (tCO₂eq), consisting of 331,513 tCO₂eq from direct emissions (Scope 1), 119 tCO₂eq from energy-related indirect emissions (Scope 2), and 92,608 tCO₂eq from other indirect sources (Scope 3).The analysis indicates that the majority of GHG emissions originate from the production of electricity, heat, and steam—both for internal use and for distribution to external users—as well as from energy losses during transmission. The primary GHGs emitted in these processes include carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O). Therefore, it is recommended that the organization consider adopting appropriate mitigation strategies or technologies to reduce GHG emissions from its electricity generation processes, taking into account both the technical feasibility and cost-effectiveness of such measures.

Development of an Information System for Online Admission: A Case Study of Saint John Thaboam School, Chiang Khan District, Loei Province

2025-05-07T22:21:14+07:00

This research aimed to: 1) develop an information system for online admissions, using Saint John Tha Bom School, Chiang Khan District, Loei Province as a case study; 2) evaluate the efficiency of the online admission information system; and 3) study user satisfaction with the system. The sample group consisted of 5 experts for system quality evaluation and 400 student applicants for satisfaction assessment. The research tools included: 1) a system efficiency evaluation form and 2) a satisfaction questionnaire. Data were analyzed using percentage, mean, and standard deviation.

The research results revealed that: 1) the system successfully facilitated the management of admission data in the form of a web application compatible across all platforms. It supported administrators, teachers, staff, and applicants, streamlining the admission process to be more convenient, fast, and accurate, especially in managing applicant data, payment status tracking, and information monitoring; 2) the system's efficiency was rated at a good level. It functioned accurately, matched user requirements, was highly efficient, easy to use, and maintained a high standard of security; and 3) users expressed the highest level of satisfaction with the system, citing its effectiveness and usefulness in practical application.

Optimization of Biochar Yield from Sweet Corn Residue Through Slow Pyrolysis Using Response Surface Methodology

2025-05-13T10:13:54+07:00

This research aimed to optimize the biochar production process from sweet corn residues through slow pyrolysis. The experimental design was based on the Box-Behnken Design (BBD) to evaluate the effects of pyrolysis temperature, heating rate, and holding time on the biochar yield. The experimental data were analyzed using Response Surface Methodology (RSM) to develop a quadratic model. The results indicated that pyrolysis temperature had a significantly negative impact on %Yield, and significant interactions among the three factors were also observed. Numerical optimization revealed that the highest predicted yield of 49.2% could be achieved at 300 °C, 2 °C/min heating rate, and 2 hours holding time. The model’s reliability was confirmed by additional randomized experiments, with an average absolute deviation of only 1.97%.

Floating Solar Power Generation Forecasting Model Based On Long Short-Term Memory And Extreme Gradient Boosting Methods

2025-06-03T21:27:30+07:00

The purposes of this research were to (1) build a forecasting model for floating solar energy generation based on Long Short-Term Memory and Extreme Gradient Boosting methods and (2) to evaluate the performance of a model based on Long Short-Term Memory and Extreme Gradient Boosting methods. The research methodology follows the deep learning pipeline, which consists of three main steps: In the first step, the data were collected from the electricity generation records and sensor readings obtained from a floating solar power facility with a capacity of 45 megawatts during February–October 2023, which consisted of 6,511 examples and 11 features. In the second step, feature extraction and classification were applied using the hybrid model by using two deep learning algorithms: 1) Long Short-Term Memory (LSTM) for memorizing long-term dependencies of time-series data, and 2) Extreme Gradient Boosting (XGBoost) for learning from uncertain data and predicting high performance; and In the third step, model evaluation was assessed using metrics including the mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) for the indicated values from the forecasting model. The experiment result shows an average MAE of 0.0577, an average MSE of 0.0143, and an average RMSE of 0.1196 that represent suitable values in a real situation.

Appendix

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