Andalasian International Journal of Applied Science, Engineering and Technology

The Effectiveness of Gambir Leaves (Uncaria gambir roxb) as A Biocoagulant in Reducing Pollutant in Palm Oil Mill Effluent

2024-07-20T07:38:02+07:00

Crude Palm Oil (CPO) production increased to 46.82 tons in 2022 (BPS), and this manufacturing process produces wastewaste that is harmful to the environment. The quality standards for palm oil mill effluent (POME) include BOD 100 mg/L, COD 350 mg/L, TSS 250 mg/L, oil and grease 25 mg/L, and total nitrogen 50 mg/L. POME with high organic content can cause eutrophication, siltation, and death of aquatic organisms. One method of treating this wastewater can be carried out through coagulation. In water treatment, chemical coagulants are typically used to remove impurities, but biocoagulants from Gambir leaves offer a natural and environmentally friendly alternative. These biocoagulants can effectively treat water while reducing the need for synthetic chemicals, presenting a sustainable solution. Gambir leaves contain polyphenolic compounds that are effective as biocoagulants. This study examines the effectiveness of Gambir leaves as a biocoagulant in reducing palm oil effluent pollutants, including TSS, BOD, COD, oil and grease, and total nitrogen. This study includes the initial steps in making biocoaglant until examining POME wastewater. Based on the results of the study, it was found that Gambir leaves extract has the potential to reduce pH and remove TSS, BOD, COD, oil and grease, and total nitrogen contained in POME with a concentration variation of 1000 mg/L, 2000 mg/L, 3000 mg/L, and 4000 mg/L.

Prediction of the Residual Compressive Strength of Rice Husk Ash Concrete after Exposure to Elevated Temperatures Using XGBoost Machine Learning Algorithm

2024-10-06T12:04:51+07:00

The study aimed to assess the applicability of XGBoost in determining the residual compressive strength of rice husk ash concrete exposed to elevated temperature, reducing the need for costly, time-consuming laboratory experiments. Data was collected from the available literature, with 75% used for training and 25% for testing. Synthetic data was created using the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). The model accuracy was checked using statistical scores: coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE). SHAP values were used for feature importance analysis. Coding was done in Python using Jupyter Notebook. With the original data, the model produced RMSE, R², and MAE test values of 3.351, 0.939, and 2.994, respectively, indicating excellent performance. The combined original and synthetic dataset gave RMSE, R², and MAE values of 0.071, 0.941, and 0.053, respectively, signifying improved performance. The feature analysis identified higher temperature, unheated compressive strength, and water-cement ratio as the most significant factors in the XGBoost prediction. The exposure duration, alumina content, and iron oxide had minimal influence.

Feature Extraction and Segmentation Methods in Plant Disease Detection: A Multimodal Approach

2024-10-06T12:10:05+07:00

Plant disease detection is essential for improving agricultural productivity. Deep learning models have shown great potential in identifying plant diseases because they can leverage large datasets. However, while efficient, traditional machine learning methods often face challenges with generalization when trained on small datasets using basic features like shape, color, and texture. A promising approach to overcome this is the combination of deep feature extraction with machine learning classification, enabling more accurate disease detection. Traditional classifiers trained on smaller datasets can still offer viable solutions in resource-limited environments. By extracting critical features and employing classical techniques, these models remain practical for constrained settings. Integrating deep learning models with traditional methods allows for better handling of disease variability across plants and conditions, enhancing adaptability and accuracy. This review explores deep learning and traditional machine learning approaches for feature extraction and segmentation in plant disease detection. It highlights how combining deep feature extraction with machine learning classification improves accuracy and addresses the challenges posed by limited datasets. The potential of multimodal techniques for enhanced detection is also discussed, leading to more robust and scalable solutions for plant disease management.

Enhancing Workspace Ergonomics to Mitigate Musculoskeletal Disorders in Stamped Batik Production (Case Study at PT.XYZ)

2024-10-06T07:50:35+07:00

Batik is one of the works owned by the Indonesian Nation, which has noble values and is recognized by other nations. The rapid development of batik makes batik makers need to pay attention to the production process carried out, In carrying out the activities of making stamped batik, it has not been supported by a special work table. Still using an ordinary stamp table that is less comfortable to use for stamped batik. So that in doing their work craftsmen have low productivity and efficiency. This research was conducted at PT XYZ to evaluate and reduce the risk of musculoskeletal disorders in stamped batik makers through ergonomic work table redesign. The research method used is case study-based quantitative, with a population of PT XYZ employees who work as stamped batik makers. Primary data was collected through observation, questionnaires and interviews. Posture measurements were carried out using the NBM (Nordic Body Map) questionnaire, to determine musculoskeletal complaints including 28 muscle parts in the musculoskeletal system on both sides of the body right and left. Then posture measurements were taken using the REBA (Rapid Entire Body Assesment) method. The results of the NBM questionnaire showed that before the improvement of the stamp batik had a high chart of complaints of pain in the musculoskeletal muscles and also the results of the REBA score showed a high risk of musculoskeletal disorders, especially in the neck, shoulders, bronze, arms and legs. After the implementation of a more comfortable stamp work table design and stamp stove table (Adjustable) there was a significant decrease in the REBA score decreasing from 8 to 1. This shows a significant reduction in the risk of musculoskeletal disorders, so that ergonomic work table design can reduce the risk of musculoskeletal disorders at PT XYZ and increase worker comfort.

Evaluation of Life Cycle Costs for Agarwood Oil Extraction with Microwave and Ultrasonic-Assisted Techniques

2024-10-26T04:32:26+07:00

Agarwood oil extraction is a crucial process in the agarwood industry. However, this extraction is seldom performed by communities due to its lack of profitability and the extensive duration of the process. This study aims to investigate innovative techniques such as ultrasonic and microwave-assisted maceration to enhance the yield of agarwood oil extraction. Additionally, it seeks to calculate the efficiency of time and cost by determining the life cycle cost of agarwood oil extraction. The methodology employed in this study involved conducting extractions using ultrasonic-assisted maceration, microwave-assisted maceration, and a combination of ultrasonic and microwave-assisted maceration. The results indicated that the extraction method yielding the highest output utilized a combination of microwave-assisted maceration for 3 minutes and ultrasonic-assisted maceration for 60 minutes, producing a yield of 0.15%. When extrapolated to 24 hours, this method would yield 18 grams of agarwood oil extract with an additional cost of Rp13,037. In contrast, the solvent maceration process using microwaves yielded 0.096% of agarwood oil at a lower cost of Rp3,190, producing 12 grams of extract over a 24-hour extraction period. the compounds that make up the composition of agarwood oil are 2-((2S,4aR)-4a,8-Dimethyl-1,2,3,4,4a,5,6,7-octahydro-naphthalen (11.38%), Hydrocinnamic acid (11.13%), Hydrocinnamic acid (11.13%) and (3R,3aR,4aS,5R,9aS)-3,5,8-Trimethyl-3a,4,4a,5,6,7,9,9a-octahydro (10.90%).

Performance Analysis of Channel Estimation Schemes for Phase Shift Optimization: An Analysis of Bit Error Rate and Armijo Step Size Behavior

2024-11-03T13:53:52+07:00

In the recent past, there has been a growing need for ultra-low latency and high-data-rate communication. In Non-Line-of-Sight (NLoS) communication, the channel capacity and accuracy of transmission are significantly affected by interferences, lowering the Quality of Service (QoS). An intelligent Reflecting Surface (IRS) has risen as a potential solution to challenges associated with NLOS communication including low data rate, multipath fading, and high BER. However, to leverage the performance gains of the IRS, effective and highly accurate channel estimation is crucial as it facilitates optimal phase shift optimization. This work investigated the performance of four main channel estimation algorithms in an IRS-aided system; LS, DD, DFT, and MMSE in terms of their BERs and effects on the convergence behavior of the Stochastic Convex Approximation (SCA) algorithm following the Armijo rule. Results indicate that in cases without statistical knowledge of the channel, the DD method provides the best performance. This work shows that the communication needs, complexity, and accuracy should be carefully considered when selecting the channel estimation method for IRS-aided communication systems.

Solar Drying Technology: Current Research Trends and Future Perspectives

2024-11-03T14:01:09+07:00

Solar drying technology has become a vital innovation for sustainable food preservation and renewable energy utilization, offering an energy-efficient alternative to conventional drying methods. This study provides a comprehensive bibliometric analysis of research trends, influential authors, key institutions, and geographical contributions in the field of solar drying technology from 2004 to 2024. We analyzed 108 relevant publications using the Scopus database and VOSviewer software, revealing a significant increase in research output since 2010, with a marked surge after 2016. India and China emerge as leading contributors, driven by their large agricultural sectors and favorable solar conditions. Key research efforts have focused on improving energy efficiency, optimizing drying processes, and preserving product quality. Despite these advancements, critical gaps remain, particularly in scaling solar drying systems and integrating hybrid technologies that combine solar energy with other renewable sources. This study highlights the importance of interdisciplinary collaboration to advance technological innovation and address challenges in food security and energy sustainability. Future research should focus on developing hybrid systems, finding better ways to store energy, and studying how solar drying affects the taste and nutritional value of food in order to make it more useful in a wider range of climates and farming situations.

Keywords: Solar Drying Technology, Bibliometric Analysis, Renewable Energy, Hybrid Systems, and Food Preservation.

Simulation of Ground Settlement Using Plaxis for the Pekanbaru-Padang Toll Road Construction Project: A Detailed Analysis

2024-10-07T07:48:28+07:00

Geotechnical issues often include settlement and soil bearing capacity, which serve as the foundation for toll roads. Each toll road is designed with specific loads and elevations, which sometimes cannot be supported by the consolidation of the existing soil due to the soft soil characteristics. Soil improvement methods such as replacement (soil material replacement) and preloading are commonly used to enchace the shear strength of soft soils. This analysis aims to evaluate and compare the amount of consolidation settlement analytically using modeling in Plaxis 2D with a very fine mesh type, using settlement plate data from the field. The analysis compares the amount of consolidation settlement through Plaxis 2D modeling with a very fine mesh type and field settlement plate data. From the analysis the consolidation time using Plaxis 2D modeling is found to be 149 days, while the settlement time from the settlement plate data is 39 days. The consolidation settlement obtained from the plaxis 2D model with a very fine mesh is 0.056 meters, whereas the settlement from the settlement plate data is 0.416 meters, with a percentage difference of 86.53%. It can be concluded that the significant difference between the field data and the Plaxis 2D analysis results is due to the lack of data available for each soil layer.

Implementation of Efficient Electric Vehicle Fast Charging System Using Dual Active Bridge Converter

2024-08-15T07:04:08+07:00

The paper presents the electric vehicle charging model with fast DC charging technology based on the mathematical modelling and theoretical studies. The problem in this study is to model the fast charging system in order to describe the control and stability of DC bus voltage. The research direction in this study is to design a novel charger in reality. A novel 200 kW fast charger was designed for modeling of fast charging system. The proposed fast charger consists of two portions such as an AC-DC converter and a DC-DC converter using dual active bridge (DAB) technique performing a charging function. The parameters of consumable devices used in the model are mentioned for performance specifications. The main section is a design of transformer that used in the DC-DC converter in order to obtain isolation between the AC system and electric vehicle. This leads to achieve the improvements in the power quality on the AC grid. The performances of the proposed fast charger system were verified through simulations results. The modeling and simulations are performed in MATLAB/SIMULINK and all results confirm the feasibility of proposed design for experimental studies. The recommendation of the system was also given based on the comparison of recent works which were met the objectives of the studies.