Development Of Process Monitoring System Research Articles

PENGEMBANGAN SISTEM MONITORING PROSES PEMURNIAN DI INSTALASI PEMURNIAN DAN KONVERSI

It has been developed of a monitoring system for the purification process parameters, and the control of the feed motor in the purification installation and the conversion of the Pilot Conversion Plant (PCP), which is one part of the process of purifying yellow cake into uranium powder UO2. The operating parameters of the purification process displayed are always changing, on this basis it is necessary to observe a process data with monitoring system to record the process parameters that occur. The purpose of developing a monitoring system for the this purification process is to direct monitor of the process with a computer-based display and to store the process data. In addition, the control of the purification process feed motors can also be done on a computer. Development of process monitoring system using hardware and software modules. The hardware uses Advantech ADAM 4017 as an analog signal input 4-20 mA from transmitter output, Advantech 4069 as a relay output for motor control feed and Advantech 4561 as an RS 485 converter to a USB computer. The software developed is LabVIEW 2011 from National Instrument. From the test results, it can be concluded that the development of the dissolution process monitoring system can run well and can be used for the purposes of process data analysis.

PCA Based Data Reconciliation in Soft Sensor Development - Application for Melt Flow Index Estimation

Melt flow index (MFI) is a very important property of thermoplastic polymers. Laboratory measurements follow standard methods (ASTM D1238 or ISO 1133) to determine MFI and give accurate values, but these measurements are available only in 2-4 hour sampling intervals. Using soft sensors real time estimation of MFI is available for process control and monitoring. When detailed knowledge about the process is not available, data-driven soft sensors can be applied. In this case historical process data are used to build statistical models to determine the relationship between inputs and outputs. Since these methods are based on measurements, the performance of soft sensor depends on the quality of data. Measurements are always affected by errors so pre-processing of data should be necessary. The measurement noise and process variable can be correlated with each other so one opportunity to improve measurement accuracy is using multivariate statistical methods (PCA, PLS). Statistical methods can be improved when phenomenological knowledge is taken into account (e.g. balance equations). The aim of the presented research is to propose a methodology to support the data-driven development of process monitoring systems. We developed a method which improves the effectiveness of data based MFI soft sensor. This method includes advantages of a priori knowledge based models and data-driven multivariate statistical process monitoring tools. As a case study we developed a soft-sensor to estimate MFI of the products of industrial polypropylene reactor at TVK Plc. The proposed method is able to detect undesirable operation states and it can be used for fault detection.

Development of process monitoring system in drilling process using fuzzy rules

This study describes the development of process monitoring system in drilling process in high speed machining center. The system monitors the several state variables of the cutting process of a drill. However, the thrust force and torque signals have been chosen to detect and monitor the drilling process, the acoustic emission signal has been also analyzed. Experimental data have allowed defining statistical behavior of the variables for non-fault conditions, tool wear and breakage. The new approach in this project is to use the standard data acquisition software LabVIEW for the purpose of data collection, signal analysis, decision making and sending back the message to the machine.