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 Base Oil Process Modelling Using Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6527
Author(s):  
Muhamad Amir Mohd Fadzil ◽  
Haslinda Zabiri ◽  
Adi Aizat Razali ◽  
Jamali Basar ◽  
Mohammad Syamzari Rafeen
Keyword(s):  
Machine Learning ◽  
Crude Oil ◽  
Pilot Plant ◽  
Operating Conditions ◽  
Support Vector ◽  
Processing Plant ◽  
Base Oil ◽  
Oil Processing ◽  
Extreme Gradient Boosting

The quality of feedstock used in base oil processing depends on the source of the crude oil. Moreover, the refinery is fed with various blends of crude oil to meet the demand of the refining products. These circumstances have caused changes of quality of the feedstock for the base oil production. Often the feedstock properties deviate from the original properties measured during the process design phase. To recalculate and remodel using first principal approaches requires significant costs due to the detailed material characterizations and several pilot-plant runs requirements. To perform all material characterization and pilot plant runs every time the refinery receives a different blend of crude oil will simply multiply the costs. Due to economic reasons, only selected lab characterizations are performed, and the base oil processing plant is operated reactively based on the feedback of the lab analysis of the base oil product. However, this reactive method leads to loss in production for several hours because of the residence time as well as time required to perform the lab analysis. Hence in this paper, an alternative method is studied to minimize the production loss by reacting proactively utilizing machine learning algorithms. Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR) and Extreme Gradient Boosting (XGBoost) models are developed and studied using historical data of the plant to predict the base oil product kinematic viscosity and viscosity index based on the feedstock qualities and the process operating conditions. The XGBoost model shows the most optimal and consistent performance during validation and a 6.5 months plant testing period. Subsequent deployment at our plant facility and product recovery analysis have shown that the prediction model has facilitated in reducing the production recovery period during product transition by 40%.

scholarly journals Safety Considerations by Synergy of HAZOP/DMRA with Safety Color Maps—Applications on: A Crude-Oil Processing Industry/a Gas Transportation System

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1299
Author(s):  
Panagiotis K. Marhavilas ◽  
Michail Filippidis ◽  
Georgios K. Koulinas ◽  
Dimitrios E. Koulouriotis
Keyword(s):  
Crude Oil ◽  
Occupational Safety ◽  
Transportation System ◽  
Decision Matrix ◽  
Processing Industry ◽  
Color Mapping ◽  
Oil Processing ◽  
Safety And Health ◽  
Sour Crude ◽  
Safety Considerations

A collaborative framework by the synergy of Hazard and Operability (HAZOP) process and the Decision-Matrix Risk Assessment (DMRA) in association with safety-color mapping (SCM) is presented, in order to identify critical points and prioritize risks, and also to visualize the occupational safety and health (OSH) situation, at the workplaces (i) of a sour crude-oil processing industry (SCOPI), and (ii) of a measurement and regulatory station (MRS) in a gas transportation system (GTS), situated in Greece. Firstly, the conventional HAZOP analysis is executed in order to identify the potential fault causes of abnormal conditions (deviations) in the plants. The application of the DMRA-modus is valuable to rank the identified risks (hierarchy of risks). In view of the results, both of the HAZOP pattern (for identifying the hazards) and also the DMRA one (for assessing and ranking the risks), SCMs have been derived for the specific workplaces of the SCOPI and the MRS/GTS station, which could be a precious means for safety managers to appraise the urgency of investing limited budgets in measures preventing particular types of deviations, and also protecting the employees.

An effective method based on multi-model fusion for research octane number prediction

2021 ◽  
Author(s):  
Ningchen Fu ◽  
Zicheng Lai ◽  
Yuping Zhang ◽  
Yan Ma
Keyword(s):  
Crude Oil ◽  
Octane Number ◽  
Research Octane Number ◽  
Model Fusion ◽  
Gasoline Engines ◽  
Oil Processing

The octane number is one of the important indicators in crude oil processing, and it is related to the anti-knock performance of gasoline engines. The loss of octane number in...

Studying demulsifier reactants for crude oil processing facilities

2021 ◽  
pp. 90-104
Author(s):  
L. V. Taranova ◽  
A. G. Mozyrev ◽  
V. G. Gabdrakipova ◽  
A. M. Glazunov
Keyword(s):  
Crude Oil ◽  
Bottom Water ◽  
Oil Content ◽  
Optimal Consumption ◽  
Residual Oil ◽  
Well Production ◽  
Oil Processing ◽  
Water Cut ◽  
Required Quality

The article deals with the issues of improving the quality of highly watered well production fluid processing using chemical demulsifier reactants at crude oil processing facilities; the analysis of the use of the reactants at the Samotlor field has been made. The article presents the results of the study of the effectiveness of the "Hercules 2202 grade A" and "SNPH-4460-2" demulsifiers in comparison with the indicators of oil and bottom water processing achieved in the presence of the reactants used at existing facilities; their optimal consumption has been determined. The study has shown that the selected demulsifiers provide the required quality of the oil and water under processing at the considered oil processing facilities and can be used along with the basic reactants for these facilities. On the basis of total indicators, the best results have been achieved using "Hercules 2202 grade A" with the improved indicators of water cut and residual oil content in water by 33.9 % and 2.8 % while reducing the reactant consumption by 9.7 % compared to the basic demulsifier.

scholarly journals A Techno-economic Evaluation of Adding Silica Nanoparticles to Enhance the Demulsification Process in a Crude Oil Central Processing Facility

2021 ◽  
Author(s):  
Shireen Hassan ◽  
Babiker Abdalla ◽  
Mustafa Mustafa
Keyword(s):  
Economic Evaluation ◽  
Crude Oil ◽  
Silica Nanoparticles ◽  
Production Cost ◽  
Net Present Value ◽  
Central Processing ◽  
Oil Processing ◽  
Software Model ◽  
Processing Facility ◽  
Demulsification Process

In this study, a techno-economic evaluation of the use of silica nanoparticles to enhance the demulsification process, in crude oil, has been investigated. A software model has been developed in MS Excel of the central processing facility (CPF). A sensitivity analysis of key parameters on production cost and Net Present Value (NPV) has been carried out for different flowsheet selection options. Comparison of flowsheets on an equal plant capacity basis results in a 19% reduction in the production cost whereas comparison on a fixed annual crude oil processing basis results in a reduction in production cost of only 3.7%.

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