Engineering and Technology Horizons

Assessing GHG Emission Reductions for Organization through the Installation of Solar PV Rooftop On-grid System

2024-08-15T15:02:03+07:00

The Earth's surface temperature is steadily increasing due to the accumulation of greenhouse gases, a phenomenon known as global warming. Human activities are the root cause of this significant global issue. Reducing greenhouse gas (GHG) emissions is one of the most critical actions in climate change mitigation. Organizations can engage in activities that promote change and reduce greenhouse gases by acknowledging the significance of addressing climate change. By reducing GHG emissions and promoting the use of renewable energy, organizations can begin to address environmental issues. Therefore, the purpose of this investigation is to assess the reduction of GHG emissions in an educational institution by substituting electricity consumption from the electrical grid with renewable energy in the form of a solar PV rooftop on-grid system. The School of Renewable Energy's GHG emissions were assessed, covering three scopes of GHG emissions activities: direct emissions, indirect emissions, and other indirect emissions. The organization's activity data were collected over a 12-month period. Without installing a solar panel system, the organization reported total GHG emissions of 310.40 tCO2e, relying solely on imported electricity for internal use. The highest GHG emissions were from Scope 2, amounting to 239.38 tCO₂e, primarily due to electricity importation. Scope 3 had the second highest GHG emissions, totaling 65.76 tCO₂e, resulting from employee commuting and the use of purchased goods such as paper and tap water. Scope 1 had the lowest GHG emissions at 5.26 tCO₂e, produced by the combustion of diesel and gasoline in both stationary and mobile sources, as well as CH₄ emissions from the septic tank. The percentage of GHG emissions from Scope 2 activities was 77.12%, which was considered to have a significant environmental impact and contribute to global warming. This was because 478,851 kWh of electricity were imported. The installation of on-grid solar cells for power generation reduced imported electricity to 113,120 kWh. Consequently, GHG emissions from Scope 2 decreased to 56.55 tCO₂e, leading to an overall reduction in the organization's GHG emissions to 127.57 tCO₂e. The organization's GHG emissions decreased by 182.83 tCO₂e as a result of using alternative energy to generate electricity. This assessment can serve as a database for educational institutions and prepare the government to report greenhouse gas emissions. Furthermore, it can serve as carbon credits for trading and exchanging carbon with other organizations to offset GHG emissions from various activities. In addition, it endorses the government's goal of achieving carbon neutrality and net zero emissions in the future.

Correlation of Tensile Strength between Mechanical and Chemical Anchors with Concrete’s Compressive Strength

2024-07-26T15:14:14+07:00

This study evaluated the preliminary relationship between anchor tensile strength and concrete compressive strength through semi-destructive pull-out tests. Anchors were tested in concrete with compressive strengths of 18, 24, and 32 MPa, using mechanical (drop-in and wedge) and chemical anchors sized 8 and 10 mm, embedded at 40 mm depth. Results indicated that higher concrete compressive strength correlates with increased anchor tensile strength. On average, a 30–60% rise in concrete strength led to a 15–70% increase in anchor tensile strength, varying with anchor type and size. Chemical anchors demonstrated the highest tensile strength, followed by drop-in and wedge anchors, respectively. Additionally, 8 mm anchors generally exhibited higher tensile strength than 10 mm anchors. Two primary failure modes were observed: concrete failure and threaded rod failure. Concrete failure predominated in the tests. Despite yielding valuable insights, the study has limitations such as fixed drilling depth and limited anchor and concrete strength variations. Nevertheless, these findings lay a foundation for further research and development aimed at optimizing anchor performance in construction applications. In summary, this study underscores the initial link between anchor tensile strength and concrete compressive strength, emphasizing the influence of anchor type and size. Future studies could benefit from expanding these parameters to enhance the accuracy and applicability of anchor performance assessments in diverse construction scenarios.

The Development of a Real-Time Electricity Calculator System Using Machine Learning to Enhance Energy Efficiency

2024-08-14T11:43:40+07:00

The development of the electric meter system described in this article is titled as Development of a Real-Time Electricity Calculator System Using Machine Learning to Enhance Energy Efficiency. The primary objective is to improve household energy management through prediction and alerts regarding energy usage. The system is designed to collect electricity usage data and various environmental parameters from households, and it employs five machine learning models to identify the best model for this purpose. The chosen model, Support Vector Regression (SVR), is used to predict energy consumption. In the research methodology, the system records real-time energy usage data into a CSV file. The predictive features include temperature, number of occupants, house size, and appliance usage. This data is standardized before being used to train the SVR model. After training, the model’s predictions are evaluated using the Root Mean Square Error (RMSE). The experimental results show that the SVR model effectively predicts electricity consumption, with a normalized RMSE of 57.56 and a cross-validation RMSE of 58.38, indicating the model’s accuracy. The visualizations provide a clear understanding of the overall relationship between actual and predicted values. Household electricity usage prediction enables users to plan energy consumption more efficiently, potentially reducing costs and improving energy efficiency. The development of this system can be applied in various fields, including industry and agriculture, to promote energy conservation and reduce environmental impact.

Bidirectional Transfer Learning of Multi-Objective Reinforcement Learning for Efficient Online VNF Profiling

2024-08-29T14:39:23+07:00

This research investigates the effectiveness of transfer learning combined with multi-objective reinforcement learning (RL) for profiling diverse VNFs, including Snort (in both Passive and Inline modes) and virtual firewalls. We compare the resource allocation predictions of an RL model with those of a standard machine learning approach, such as a multilayer perceptron (MLP). While MLPs can outperform RL models in certain scenarios, they lack adaptability. Unlike RL, MLPs require retraining when conditions change. To address this limitation, we propose adaptable RL profilers that dynamically allocate resources (CPU, memory, and link capacity) based on the performance needs of the VNFs. The experiments were conducted in four scenarios: two cases of transferring from Snort (Passive Mode and Inline Mode) to a virtual firewall (vFW) and two cases of transferring from vFW to Snort. Our results reveal a trade-off between computational resource utilization (CPU and memory) and link capacity. In the transfer learning scenario from Snort's Inline Mode VNF to vFW, the Q-Learning model with transfer learning (TL) achieved approximately a 20% reduction in vCPU usage compared to the MLP approach. However, it did not perform as effectively as the MLP in reducing link capacity utilization. Conversely, in the transfer learning scenario from vFW to Snort Inline Mode VNF, the Q-Learning with TL model reduced link capacity usage by 20% compared to other models, although it was less efficient in reducing CPU usage.

Experiment of PI Controller for Indirect Current Control for Single – Phase Shunt Active Filter

2024-08-29T09:34:28+07:00

Due to harmonic problems caused by various factors that resulted in damages and malfunctions of electrical equipment in the power system, eliminating harmonics in the electrical system has become an important issue for both industrial and household sectors. This article focuses on developing a current control method for a single-phase shunt active power filter (APF) that controls current by an indirect method. The detection of load current to generate reference signals uses the dq-axis reference frame for a single-phase electrical system. The design of circuit parameters and PI controller parameters for compensating current and DC bus voltage are based on a simple calculation method. Simulation and experimental results confirm that the proposed current control method and PI controller design effectively eliminate harmonics, with the percent of total current harmonic distortion (THDi) values of the supply current after compensation within the IEEE Std 519-2022 standard limit.

Plant Layout and Design of Central Supply Laundry Department: A Case Study of a Hospital in Songkhla Province

2024-09-10T15:09:34+07:00

This research focuses on minimizing the distance clean clothes are moved from the drying process to the shipping process by modifying the sorting area for efficient adjustments. The analysis employs the Systematic Layout Planning (SLP) methodology, categorizing relationships between activities into three types: Type A (3 relationship connections), Type I (2 relationship connections), and Type O (2 relationship connections). Two factory layout designs were developed based on this analysis. Layout 1 achieves a total movement distance of 2,014.72 meters from drying to daily shipping, resulting in a significant reduction of 1,160.06 meters compared to the original layout, representing a 37.69% decrease. Layout 2 reflects a total movement distance of 2,073.46 meters, which corresponds to a reduction of 1,218.80 meters from the original layout, or a 35.88% decrease. Qualitatively, the new factory layout enhances workplace safety by reducing the risk of accidents from collisions involving cloth carts, fostering a more positive work environment and increasing employee satisfaction. The design optimizes space efficiency, improving overall organization and workflow within the facility. Furthermore, although the article does not directly address service quality, the reduction in movement distance and time for transporting clean clothes indirectly contributes to improved service quality. This efficiency allows for quicker delivery of clean clothes to various departments, thereby enhancing responsiveness and operational effectiveness.

Effect of Residence Time on Liquid Product Yield through a Designed Pyrolysis Reactor with Six Series-Connected Condensers

2024-11-18T10:58:18+07:00

The objective of this study was to investigate the effect of residence time on the product yields of multilayer plastic (ML) waste through pyrolysis using a household reactor. The system employed six series-connected condensers operating without cooling water for heat transfer. The research focused on determining the optimal residence time within the range of 60–120 min, with a heating rate of 5–15°C/min. Liquefied petroleum gas (LPG) served as the primary fuel for the pyrolysis process, while non-condensable gases were recirculated into the burner as supplementary fuel. The study analyzed the characteristics and quantities of the resulting products: solid residue, liquid oil, and non-condensable gases. The results indicated that the proportion of non-condensable gases ranged from 27.1% to 47.1%, while the liquid yield condensed from four of the six condenser tubes varied between 46.5% and 59.1%. The pyrolysis of ML waste produced solid residue ranging from 6.8 % to 13.8 % of the total products. The residence time significantly influenced the liquid yield, with the maximum liquid product of 591 g per 1 kg of ML feedstock obtained at a residence time of 60 min. To characterize the liquid product as biofuel oil, its chemical composition was analyzed using distillation gas chromatography (DGC) and gas chromatography-mass spectrometry (GC/MS). The analysis revealed that the liquid product contained fuel oil components, including kerosene, diesel oil, benzene, and fuel oil. Additionally, the liquid product exhibited a high heating value of 10,691 cal/g. Furthermore, substituting pyrolysis gas for LPG significantly reduced LPG consumption. This study provides valuable insights into the development of community-based pyrolysis systems.

Solving the Scheduling Problem of Stainless Steel and Alloy Factory: A Case Study of Stainless Steel and Alloy Factory in Uttaradit Province

2024-10-07T09:20:46+07:00

This study addresses production scheduling inefficiency in stainless steel and alloy manufacturing by developing mathematical models and employing Greedy (GdyA) and Genetic Algorithms (GA). A 23-factorial design tested GA efficiency, optimized by ANOVA analysis. Genetic Algorithm (GA) methods are to establish a program for production scheduling. The research found that the small problem would get the appropriate parameter value. Optimal GA parameters varied with problem size: the smaller problems favoured a population of 50 or 200 generations, crossover of 1, and mutation of 0.3, while the larger problems performed best with a population of 200 or 50 generations, crossover of 1, and mutation of 0.3. This study is in line with the objective of giving factories an alternative to use the results of the production scheduling answer values to solve the problem. This approach effectively tackles the NP-Hard nature of production scheduling, with GA consistently finding solutions matching the minimum bound. GdyA also achieved the minimum bound in 50% of the tested cases. It can be implemented in a real-world setting. Optimized GA consistently delivered optimal scheduling solutions over three months, confirming the potential to significantly enhance production efficiency.