Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering)

Analysis of Waste Utilization of Refuse Derived Fuel (RDF) Into Briquettes for Steam Power Electric Generation

2024-10-30T14:24:51+00:00

Waste in Indonesia reaches 65.2 million tons/year, dominated by organic and inorganic waste. Around 36% of the total waste has not been managed properly, including 4K1P waste (paper, wood, cloth, rubber, and plastic), which is flammable and can be made into RDF briquettes using the pyrolysis method and paraffin adhesive which will used three variations of raw materials, namely from organic, inorganic and mixed waste. The quality of the three briquettes was compared based on calorific value, ash content, water content, and PLTU fuel requirements if used as fuel for a 7.5 MW capacity PLTU. The weight of the initial raw material for briquettes is 2 kg which produces 875 grams of paper carbon charcoal, 648 grams of wood, 1.09 kg of plastic, 856 grams of cloth, and 1.16 kg of rubber, resulting in 10 pieces of organic, inorganic and mixed briquettes each. The test results show that inorganic briquettes have the best quality with a calorific value of 8,075.92 cal/g, water content of 1.75%, and ash content of 10.84%, where the calorific value and water content meet SNI-01-6235-2000. The minimum fuel requirement for a PLTU using RDF briquettes is inorganic briquettes, namely 3.05 tonnes/hour with an annual requirement of 26,205.6 tonnes/year. It can be concluded that inorganic briquettes have the best quality and require the least amount of fuel to be used in PLTU.

Performance of High-Gain Observer (HGO) For Level Estimation in Continuous Stirred Tank Reactor (CSTR)

2024-10-30T14:24:51+00:00

This paper presents an estimation of level in CSTR system. Due to the non-linearity in the model of the CSTR system, our focus is on observers capable of handling this non-linearity. We proposed to use a High Gain Observer (HGO) for performing level estimation. The desired characteristics for estimation include minimal error, fast convergence time, and robustness in the presence of noisy measurements. A comprehensive analysis of the observers, including the evaluation of their effectiveness and robustness, is presented through simulation

Implementation OEE in Integrating Siemens S7-1200 Data with Odoo ERP

2024-10-30T14:24:51+00:00

The integration of perational Technology (OT) and Information Technology (IT) is crucial for enterprise-level decision-making regarding company effectiveness and productivity. This integration is achieved by developing an integrator system in the form of an API service, which facilitates the connection between Programmable Logic Controller (PLC) and Odoo Enterprise Resource Planning (ERP). Integration allows IT systems synergize with OT systems to processing data obtained from the performance of industrial machines. Analysis was conducted using the Overall Equipment Effectiveness (OEE) method to monitor the company’s production outcomes. A prototype sorting system with PLC for controlling was created as an OEE implementation. The API service acquires data from the plant and performs preliminary analysis in Odoo ERP. The analysis in Odoo ERP is conducted through the development of a manufacturing module that applies the OEE method calculations to monitor the effectiveness and efficiency of production procces. Once the calculation result is obtained, these data are monitored on Grafana in the form of pie charts and graphs. This research yielded insight into the design and implementation of an integration system for the bottle sorting process. The result also could be monitored in real-time using Odoo ERP and Grafana. Furthermore, the OEE analysis was matched with manual calculations in order to ensure its validity. Thus, this research enables the production process to be optimized and provides significant benefits to the company.

Testing Smoker Detection Using Google Cloud Services and Infrastructure

2024-10-30T14:24:51+00:00

Smoking remains a significant public health challenge globally, contributing to a wide range of detrimental health outcomes including cardiovascular diseases, cancer, and respiratory disorders. Despite concerted efforts to curb smoking rates through policy interventions, effective monitoring and enforcement remain complex and resource-intensive tasks for health authorities and organizations. Innovative approaches leveraging advanced technologies such as visual detection systems powered by deep learning offer promising solutions to enhance smoking behavior detection and monitoring. Integrating the Google Cloud Vision API enables real-time identification of smoking indicators and discrimination from complex visual backgrounds. This capability not only supports proactive health monitoring but also strengthens the enforcement of public health policies aimed at reducing smoking prevalence. The research methodology utilizes a dataset of 600 images sourced from the Kaggle platform, encompassing diverse scenarios to optimize model training. Techniques such as image segmentation, feature extraction, and machine learning-based classification are employed to achieve high levels of precision and recall in identifying smokers and cigarette smoke. Despite the advantages of scalability, robust infrastructure, and high availability facilitated by cloud computing, the study acknowledges challenges such as bandwidth constraints and security risks associated with handling sensitive health data. Nevertheless, technological innovations in visual detection systems and cloud services are underscored as pivotal in mitigating the health impacts of smoking and advancing public health initiatives.

Design of LPG Leakage Detection Device using MQ-2 Sensor Equipped with Hazard Warning System

2024-11-02T02:52:58+00:00

The use of liquefied petroleum gas (LPG) as household fuel in Indonesia has increased because it has greater heating power and equipment efficiency than other fuels. LPG is flammable and can explode if a leakage occurs and is exposed to a fire source. Therefore, explosions can be anticipated by installing a gas leakage detector equipped with a danger warning feature called Tor-G (LPG Detector) based on the NodeMCU ESP8266 microcontroller using an MQ-2 sensor with a buzzer and LED as a danger warning feature. Tor-G can detect when a gas leakage occurs and provides a danger signal in the form of a sound from a buzzer which can be heard several meters away by the homeowner so that an explosion can be anticipated quickly. This research carried out four tests: the sensor accuracy test, the sensor responsiveness test, the distance between the sensor and the leakage source test, and the responsiveness of the buzzer and LED test. The results of the tests show that the sensor only detects the LPG. The sensor readings are accurate since the value of Rs/Ro on the Tor-G is not much different from that of Rs/Ro on the datasheet, thus producing an error value in the 0% -0.08% range. The ideal installation position of the Tor-G is 75 cm from the gas cylinder and the danger warning system from the buzzer and red LED lights up within 18.88 seconds.

Design and Implementation of LPG Leakage Detection System using IoT-based Regulator Lever Automation on a Household Scale

2024-11-02T02:54:30+00:00

Liquefied Petroleum (LPG) is very common in daily cooking activities. Therefore, preventive measures must be taken to reduce the risk of fire and explosion due to LPG leakages. One action that can be taken is to install a regulator lever that can open automatically when a leakage is detected. In this research, Pulse Width Modulation (PWM) is used to control the rotational speed of the servo motor on the regulator lever, which aims to implement an automatic control system that is responsive when an LPG leakage is detected. Using servo motor control to automatically open the regulator lever can reduce the impact of accidents or losses due to LPG leakages. The test results show that the regulator lever can be opened effectively with a pulse value of 0.55 ms at a rotation angle of 0° in 0 seconds, while it can be closed with a pulse value of 2.45 ms at an angle rotation of 180° in 1.66 seconds. This shows that the regulator responds appropriately to the level of danger detected and is precise in adjusting position. The use of PWM on servo motors in the regulator control system has proven effective in increasing responsiveness to LPG leakages, as seen from the fast response (0 to 1.66 seconds) and accuracy (100% success percentage for 15 trials). This shows that using PWM can respond to emergencies quickly and precisely.

Implementation of Natural Language Processing for Bullying Complaints with Voice to Text Conversion

2024-11-02T03:05:32+00:00

Bullying in high school is frequent and negatively affects students' psychological well-being. The lack of effective reporting mechanisms makes students hesitant to report bullying cases for fear of their identity being exposed, which can lead to stigma or retaliation. The lack of data on bullying incidents also hampers prevention and intervention measures. The study designed and implemented a mobile application that uses natural language processing (NLP) for speech-to-text conversion, enabling anonymous and convenient reporting of bullying cases. The app ensures the anonymity of whistleblowers and facilitates the collection of accurate data on bullying incidents, helping schools respond with appropriate preventive measures. Software engineering methodologies are used with a focus on requirements analysis, system design, implementation, and application testing. NLP technology is used to interpret verbal instructions into text, with the Support Vector Machine (SVM) method for text classification, ensuring high accuracy in detecting bullying incidents. The trial application in several high schools showed the relevance and effectiveness of the application. Ethical and security considerations are top priorities, with an emphasis on whistleblower identity protection and data security. The test results showed that the application achieved 92% accuracy, 90% precision, and 88% recall, demonstrating its effectiveness in collecting bullying reports anonymously and accurately.

Reliability Analysis of Coal Feeder Instrumentation Using Failure Mode and Effect Analysis (FMEA) at PT. PLN Nusantara Power UP Tenayan

2024-11-02T03:53:34+00:00

PLN Nusantara Power UP Tenayan is a coal-fired power plant supplying electricity to the Riau region with a capacity of 2 x 110 MW. Operating 24/7, the plant faces frequent failures, particularly in the instrumentation components of the coal feeder. This research employs the Failure Mode and Effect Analysis (FMEA) method to identify and analyze these failures. The Risk Priority Number (RPN), calculated by multiplying severity, occurrence, and detection ratings, helps determine the failure level. Research stages include a preliminary study, problem identification, observations and interviews, data collection and processing, FMEA analysis, and conclusion drawing. The results indicate that the instrumentation components are generally reliable, with RPN values below the critical threshold of 200. The speed sensor, with the highest RPN value of 160, is still categorized as reliable. Failures in these components can lead to operational failures, suboptimal coal feeder performance, and monitoring difficulties. The study recommends preventive maintenance every six months to enhance reliability and minimize disruptions.

The Characterization of A Thermoelectric Generator Type TEC1-12706 Hybridized On 50W Polycrystalline PV

2024-11-02T09:25:59+00:00

The potential of solar energy is very large in generating electrical energy conversion using TEG and PV technology. Therefore, it is important to understand how the technology works. This study aims to characterize the electrical generation of 12 TEG modules type TEC1-12706 hybridized with 2 PV panels. One PV panel is left without a TEG module as a comparison. Meanwhile, on the cold side of the TEG attached to the PV, a heatsink with 3 fins is placed. The left and right fins are air allowed to flow naturally, while the middle fin is flowed with cold water fluid through a 0.01 m diameter water hose with 2 flow rates; 0.02 and 0.05 m / s. The results of the study showed that the fluid flow rate of 0.02 presented a better TEG performance effect than 0.05 m / s at an irradiance of 450 W/m². Different things are shown by PV solar panels, where the power generation and efficiency are better in TEG at a flow rate of 0.05 m/s compared to 0.02 m/s for the same irradiance. Overall, the performance of PV-TEG is better at a TEG cooling fluid flow rate of 0.05 m/s.

Reliability Analysis of Steam Turbine Instrumentation Using the Failure Mode and Effect Analysis (FMEA) Method at PT. PLN Nusantara Power UP Tenayan

2024-11-02T10:06:14+00:00

PT. PLN Nusantara Power Unit Generation Tenayan is a company operating in the Steam Power Plant sector in Pekanbaru, Indonesia. One of the main components of a steam power plant (PLTU) is a steam turbine. A steam turbine is a machine that functions to convert kinetic energy into electrical current. Operational failures are often caused by less than optimal instrumentation in the steam turbine. This research uses the failure mode and effect analysis (FMEA) method with the aim of identifying the type of failure, the cause of the failure, the effect of the failure, and determining the RPN value. The analysis results from this research show that the turbine instrumentation components still meet performance standards because the risk priority number (RPN) value is less than 200. The conclusion from this research is that errors in steam turbine instrumentation are inaccurate sensor readings, switches cannot deactivate the equipment, and readings control room does not match local readings, the instrumentation component with the highest risk priority number (RPN) is the solenoid valve with a value of 140, and the instrumentation component with the lowest risk priority number (RPN) is the pressure indicator with a value of 30. The component given top priority in action The recommendation is for the solenoid valve component, namely to carry out maintenance every 6 months and add an overspeed protection system

Implementation of Natural Language Processing in the Reporting and Handling System of Sexual Violence Cases on Campus

2024-11-02T14:31:33+00:00

Sexual violence in the campus environment is a serious problem that requires an effective reporting and handling system. This research aims to develop a Natural Language Processing (NLP)-based system that can improve the process of reporting and handling cases of sexual violence on campus. The methodology used includes the application of NLP techniques such as sentiment analysis and entity recognition to automate the identification and handling of reports. The Support Vector Machines (SVM) algorithm is used for the classification of text in this system. The data is collected from various sources, pre-processed, and used to train NLP models. The results of the study show that the system developed has an accuracy level of 91%, precision of 93%, and recall of 87%, which illustrates its effectiveness in collecting reports of sexual violence anonymously and accurately. Feedback from early adopters shows that the system improves the efficiency and accuracy of the reporting process. The conclusion of this study is that the implementation of NLP can significantly improve the reporting and handling system of sexual violence on campus. Further research is suggested to expand the scope of the system and improve its analysis capabilities.

Application of K-Nearest Neighbor Algorithm for Consumer Behaviour Identification and Product Personalisation Based on Big Data Analysis

2024-11-04T02:46:49+00:00

The rapid digital transformation has fundamentally altered the retail industry, presenting challenges such as shifting consumer behavior and intensified market competition. This research explores the application of the K-Nearest Neighbor (KNN) algorithm for identifying consumer behavior and developing product personalization systems based on big data insights. Utilizing a dataset comprising 10,000 transaction records from January to December 2023 and 5,302 product types, we implemented the KNN algorithm to predict consumer purchases. The data was processed into 89,908 distinct transaction records. Our evaluation, using 5-fold cross-validation, demonstrated that the optimal performance of the KNN model was achieved with 𝑘=10, yielding a precision of 0.8319, recall of 0.8311, and an F1-score of 0.8312. These findings highlight the effectiveness of KNN in enhancing consumer satisfaction through precise product recommendations. This study provides strategic insights for modern retailers aiming to leverage AI and big data to remain competitive and meet evolving consumer expectations.

Design of A Rectangular Patch Microstrip Array Antenna with Proximity Coupled On ADS-B Receiver

2024-11-03T09:15:03+00:00

One of the observation facilities available in Indonesia was Automatic Dependent Surveillance-Broadcast (ADS-B), a surveillance technology similar to Radio Detection and Ranging for monitoring air traffic. This system only received information transmissions from aircraft, broadcast on a frequency of 1090 MHz using a monopole antenna with large dimensions, ranging from 85 centimeters to 3,5 meters, and weighing between 1,5 kilograms to 26 kilograms. As an alternative, a microstrip antenna was chosen to reduce the large size and weight of the monopole antenna. This research aimed to design a 2x1 rectangular patch microstrip array antenna with a proximity coupled feeding method at a frequency of 1090 MHz. The antenna was designed using Antenna Design Software and fabricated using Epoxy substrate material with a copper ground plane. The simulation results showed a Return Loss of -28,87 decibels, VSWR 1,07, Bandwidth 28,87 MHz, Impedance 52,4 Ω, and Gain 8,49 decibels. The measurement results showed a Return Loss of -30,87 decibels, VSWR 1,07, Bandwidth 28 MHz, Impedance 48,91 Ω, and Gain 6,4 decibels. The test results demonstrated that the antenna successfully received signals from 32 aircraft, with the longest reception distance reaching 172,5 Nautical Miles (319.4 kilometer) at a maximum altitude of 36,950 feet.

Enhanced Depth Control and Stability in Submersible Pylon Inspection Robots Using IMU-Based Extended Kalman Filter and PID Control

2024-11-03T12:39:13+00:00

The research developed a Submersible Pylon Inspection Robot (SPIR) to clean and inspect underwater structures automatically, facing challenges in stability and depth of operation. Integrating the Inertial Measurement Unit (IMU) with the Extended Kalman Filter (EKF), as well as implementing customized PID controls, aims to improve the accuracy and stability of SPIR in dynamic underwater environments. The results show that the SPIR control system can follow the reference depth at shallow depths well, but has difficulty maintaining stability at deeper depths. The position error graph on the Z-axis shows the initial fluctuation that decreases over time, reflecting the increased calibration. The use of drive motors also shows different working patterns, with some motors active in depth settings and others for stabilization. This study shows that the integration approach of IMU, EKF, and PID control can improve SPIR performance, although further adjustments are required to meet the extreme challenges in underwater environments.

Control of Multi-Robot Arms in Object Retrieval Based on Human-Machine Interface

2024-11-03T13:18:22+00:00

Multi-robot arm control is the result of the development of advanced robotics technology. With the advancement in the field of human-machine interface, controlling robot arms has become more efficient and can be done intuitively by humans. In this study, we designed three robot arms of 4-DoF, each of which is controlled by a computer in a visual program interface. This research aims to develop a human-machine interface-based multi-robot arm control system that allows humans to interact with the robots directly. The movement method of each robot uses Trajectory Planning, which works when the operator gives motion commands through the interface display. Multi-robot communication with a computer using USB hub serial format. The computer is the master, and each Arduino on the robot is the enslaved. Three robot arms have been tested and controlled by the HMI computer, and all of them move according to the commands of the operator. The time required by each robot to complete its mission is ± 10 seconds. The results of this study are expected to open new opportunities in the application of robotics in various fields, such as the manufacturing industry, health services, and transportation.