scholarly journals Fuzzy Reliability and Fuzzy Availability of the Serial Process in Butter-Oil Processing Plant

2009 ◽  
Vol 5 (1) ◽  
pp. 65-71 ◽  
Author(s):  
Kumar
Keyword(s):  
Processing Plant ◽  
Fuzzy Reliability ◽  
Butter Oil ◽  
Oil Processing ◽  
Serial Process ◽  
Fuzzy Availability

scholarly journals Analysis of the Reliability of the Butter-Oil Processing Plant using CAS Mathematica and Maxima

2020 ◽  
Vol 8 (6) ◽  
pp. 4966-4972
Keyword(s):  
Differential Equations ◽  
Comparative Study ◽  
Death Process ◽  
Processing Plant ◽  
Manufacturing Plant ◽  
Butter Oil ◽  
First Order ◽  
Oil Processing ◽  
First Order Differential Equations ◽  
Comparative Manner

The aim of the present paper is to analysis the reliability of the system by using CAS Mathematica and also, it’s comparative study with CAS Maxima. The butter oil manufacturing plant consists of seven units i.e. separator, pasteurizer, continuous butter making, melting vats, butter oil clarifier, packaging and standby state. Model is developed by using Markov birth-death process. The first order differential equations are derived by using model and then solved for comparative study of reliability to give accuracy in result. By using Mathematica, we calculate probability of each state, which is beneficial to plant owners because better accuracy in result enhance reliability of the plant Graphs are plotted and tables are developed with the help of CAS Mathematica and Maxima, graphs show the variation and tables shows the fluctuations in reliability in comparative manner.

An expanded HAZOP-study with fuzzy-AHP (XPA-HAZOP technique): Application in a sour crude-oil processing plant

2020 ◽  
Vol 124 ◽  
pp. 104590 ◽  
Author(s):  
Panagiotis K. Marhavilas ◽  
Michail Filippidis ◽  
Georgios K. Koulinas ◽  
Dimitrios E. Koulouriotis
Keyword(s):  
Crude Oil ◽  
Fuzzy Ahp ◽  
Processing Plant ◽  
Oil Processing ◽  
Sour Crude

scholarly journals Instrumental measurement of odour nuisance in city agglomeration using electronic nose

2018 ◽  
Vol 28 ◽  
pp. 01012 ◽  
Author(s):  
Bartosz Szulczyński ◽  
Tomasz Dymerski ◽  
Jacek Gębicki ◽  
Jacek Namieśnik
Keyword(s):  
Electronic Nose ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Reference Method ◽  
Monitoring Network ◽  
Ambient Air ◽  
Processing Plant ◽  
Production Plant ◽  
Oil Processing

The paper describes an operation principle of odour nuisance monitoring network in a city agglomeration. Moreover, it presents the results of investigation on ambient air quality with respect to odour obtained during six-month period. The investigation was carried out using a network comprised of six prototypes of electronic nose and Nasal Ranger field olfactometers employed as a reference method. The monitoring network consisted of two measurement stations localized in a vicinity of crude oil processing plant and four stations localized near the main emitters of volatile odorous compounds such as sewage treatment plant, municipal landfill, phosphatic fertilizer production plant. The electronic nose prototype was equipped with a set of six semiconductor sensors by FIGARO Co. and one PID-type sensor. The field olfactometers were utilized for determination of mean concentration of odorants and for calibration of the electronic nose prototypes in order to provide their proper operation. Mean monthly values of odour concentration depended on the site of measurement and on meteorological parameters. They were within 0 – 6.0 ou/m3 range. Performed investigations revealed the possibility of electronic nose instrument application as a tool for monitoring of odour nuisance.

scholarly journals Development and Application of a Data-Driven System for Sensor Fault Diagnosis in an Oil Processing Plant

Processes ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 436
Author(s):  
Nayher Clavijo ◽  
Afrânio Melo ◽  
Maurício M. Câmara ◽  
Thiago Feital ◽  
Thiago K. Anzai ◽  
...  
Keyword(s):  
Process Monitoring ◽  
Predictive Analytics ◽  
Data Driven ◽  
Processing Plant ◽  
Common Belief ◽  
Early Failure ◽  
Oil Processing ◽  
Root Cause ◽  
Real Process ◽  
Driven System

Predictive analytics is usually cited as one of the most important pillars of the digital transformation. For the oil industry, specifically, it is a common belief that issues like integrity and maintenance could benefit from predictive analytics. This paper presents the development and the application of a process-monitoring tool in a real process facility. The PMA (Predictive Maintenance Application) system is a data-driven application that uses a multivariate analysis in order to predict the system behavior. Results show that the use of a multivariate approach for process monitoring could not only detect an early failure at a metering system days before the operation crew, but could also successfully identify, among hundreds of variables, the root cause of the abnormal situation. By applying such an approach, a better performance of the monitored equipment is expected, decreasing its downtime.

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