DEVELOPMENT OF A WEB-BASED RBI PROGRAM FOR LNG PLANT CONSIDERING CRYOGENIC ENVIRONMENT

ABSTRACT Recently, Risk-Based Inspection (RBI) evaluation technique based on API 581 has become a preferred approach to determine economic feasibility and safety to plants. However, there are limitations of applying API 581 to Liquefied Natural Gas (LNG) plant because its liquefaction process is operated in cryogenic temperature under -162 ℃ . It could affect the risk of main components in liquefaction process, but API code considered the temperature range of about -40~149℃ to evaluate the brittle fracture damage factor. The objectives of this paper are to develop a RBI program based on a web-based reality environment to resolve the above issue and to evaluate the risk of equipment in LNG plant. To achieve these, Minimum Design Metal Temperature (MBDT) region of impact test exemption curves were extended to about -196℃. Risk evaluation results considering cryogenic temperature and applicability of the proposed RBI program are fully discussed in the paper. The proposed RBI program will be useful to evaluate risk of the major components in cryogenic environment

A Model for Robot Arm Pattern Identification using K-Means Clustering and Multi-Layer Perceptron

Predictive maintenance of industrial machines is one of the challenging applications in Industry 4.0. This paper presents a comprehensive methodology to identify robot arm (SCARA) movement patterns to detect the mechanical aging of the robot, which is determined by the abnormal movement of the robot arm. The dataset used is two robot arm movements that go from point A to B and then back to point A. Accelerometer data is used to measure the signal of SCARA actions, mainly focus on the non-linear movement. The identification of the movement pattern of the robot arm is made by combining k-means and multilayer perceptron. The proposed approach first extracts valuable features as characteristics of the two datasets from the time domain statistical value parameters. K-means clustering technique is initiated to label the training dataset. In this phase, the elbow curve is used to determine the number of clusters in the dataset, which is 2 clusters. Moreover, the assumption is used to determine which cluster is labeled as a normal and abnormal movement.  Hence, a multilayer perceptron approach is proposed to predict the testing dataset. The proposed multilayer perceptron model yields an accuracy of 94.14%, whereas its cross-validation yields an accuracy of 96.12%

Temperature and Humidity Control System for Pole-Mounted Metering Circuit Breaker with Artificial Neural Network Methods

Pole-mounted Metering Circuit Breaker (PMCB) is a medium voltage protection device. Problems in the PMCB because operating at medium voltage causes insulation problems. The isolation problem that arises is due to partial discharge. Partial discharge can trigger the risk of flashover. In addition, corona discharge causes corrosion of the conductor, the effect is a failure and disconnection of electricity. This control system aims to maintain the temperature and humidity of the PMCB at the nominal values according to the standard. Based on SPLN D3.021-1:2020, it is known that under normal service conditions, the ambient air temperature does not exceed 40°C and the average temperature for 24 hours does not exceed 35°C and the highest relative humidity is 100% RH. The control system uses an AC voltage controller which is used to control the input voltage of the heater and exhaust fan so that the temperature and humidity can reach nominal operating conditions. The control method used is an artificial neural network (ANN) to find the ignition angle of the AC voltage controller as a TRIAC control. The test results using the ANN control method, system simulation produces a temperature error of 1.029% and humidity error of 2.48% and the hardware system produces a temperature error of 2.364% and humidity error of 8.673% compared to the set point temperature of 35°C and humidity of 50% RH. It can be concluded that the ANN control method can maintain the PMCB temperature and humidity according to standard

ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITION

This paper examines the use of an artificial neural network approach in identifying the origin of clove buds based on metabolites composition. Generally, large data sets are critical for an accurate identification. Machine learning with large data sets lead to a precise identification based on origins. However, clove buds uses small data sets due to the lack of metabolites composition and their high cost of extraction. The results show that backpropagation and resilient propagation with one and two hidden layers identifies the clove buds origin accurately. The backpropagation with one hidden layer offers 99.91% and 99.47% for training and testing data sets, respectively. The resilient propagation with two hidden layers offers 99.96% and 97.89% accuracy for training and testing data sets, respectively