Utilizing self-organization systems for modeling and managing risk based on maintenance and repair in petrochemical industries

Maintenance is essential to ensure safe operation of equipment in normal conditions. Therefore, managers must identify the relative priorities and equipment maintenance requirements. Moreover, based on the results of equipment vulnerability assessments, maintenance programs can be developed and managed properly. There are different methods and techniques in the process of risk assessment and management and vulnerability of equipment. Seventy-six samples with different properties have been used in this study. Networks used in this study are self-organizing networks with constant weight, which include Kohonen networks. For this purpose, operation impact, operation flexibility, maintenance cost, impact of safety and environment and frequency parameters had been considered as input; and using this model, the risk level is calculated. Utilizing genetic algorithms, the structures of all self-organizing systems are optimized. In order to evaluate the accuracy of the model, we compare it with the fuzzy model, and the results indicate that self-organizing systems optimized with the genetic algorithm have higher ability, flexibility and accuracy than the fuzzy model in predicting risk

Criticality analysis of petrochemical assets using risk based maintenance and the fuzzy inference system

Assets failure is widely considered as one of the main causes of major accidents in chemical industries such as fires, explosions, and toxic gas releases. Assets criticality analysis is vital to prevent such accidents. Risk-based maintenance (RBM) is among the most advanced comprehensive risk assessment methodologies for the criticality analysis of assets. The present study applies both traditional RBM and Fuzzy RBM (FRBM) methods for the risk analysis of petrochemical assets failure. Four consequence factors comprising operational impact, operational flexibility, maintenance cost, and impact on safety and environment are considered for the risk evaluation of assets failure. Moreover, frequency and risk factor scales are localized for both traditional RBM and Fuzzy RBM methods using an expert panel. The results of the case study show suitability of the FRBM model. Fuzzy numbers show that out of 107 assets, 10 are at the semi-critical level, and the remaining 97 are at the non-critical level. The highest fuzzy risk numbers were obtained for two blowers, where the assets failure value was 99.145. The criticality evaluation results show that the plant in the case study is at the semi-critical level. Given this, it is recommended that risk managers of the plant should customize and prioritise their maintenance planning according to the FRBM value for each asset failure. To this end, maintenance-related recommendations are offered to facilitate and assist decision-makers

Methodology for modeling of city sustainable development based on fuzzy logic: a practical case

Information on sustainability can be used for future development planning. This study presents an approach for assessing urban sustainability. Delphi and fuzzy logic methods and the Kruskal–Wallis test were the discovery and verification techniques used. The city system comprised social, economic, and environmental subsystems. The seven orientors of existence, effectiveness, freedom of action, security, adaptability, coexistence, and psychological need were measured using different indicators. The final sustainability output was obtained by aggregation of the multiple orientors and subsystems sustainability values into a unified measure. A fuzzy sustainability index was developed to compare the importance of the sustainability orientors and subsystems. The model was applied to Mahshahr, an industrialized coastal city in Iran. The model output for the subsystems showed significant differences between the economic and environmental subsystems and the social subsystem. The final sustainability output showed that the effectiveness orientor gave the highest sustainability value. The model is dynamic and can be modified for different purposes by changing the indicators. With this model, policy-makers can evaluate existing city sustainability and predict future sustainability by varying the indicators. This can be done on local, regional, and global scales for security and adaptation strategies, mitigation plans, and sustainable development management

Criticality analysis using risk assessment-based maintenance of a petrochemical company

To improve maintenance management many evaluations are used to assess the failure risk of hazards in industries. The aim of this study is critical analysis using the risk-based maintenance technique of petrochemical industries. This research is applied in the Fajr Petrochemical Company in southwestern Iran. The assets based on the risk output are prioritized. The criticality analysis showed that 11 failures out of 22 identified failures were at the semi-critical and 11 were at non-critical levels of risk. By this research we can reduce the maintenance cost and prioritize the failures based on their HSE effects and consequence factors

Identification of sustainability indicators for oil and petrochemical industries development in southwest coast of Iran

This study aims to develop an integrated sustainable development model for oil and petrochemical industries. There are five specific objectives. The first objective is to identify appropriate sustainability model indicators for the city environmental system. The second objective is to identify and evaluate the sustainability indicators for industrial systems. The third is to conduct a risk assessment model of petrochemical companies. The fourth is to determine the sustainability management model in the industry system. The final objective is to develop an integrated sustainable development model for oil and petrochemical industries. The city environmental system was based on Mahshahr, located near the major Petrochemical Economic Free Zone (Petzone) in the southwest coast of Iran. The industrial system investigated was for the Fajr Petrochemical Company (PC), in the Petzone. Two models were developed. The city environmental system included social, economic and environmental subsystems. The industrial system comprised economical, social, environmental, safety, and health subsystems. The city sustainability indicators were identified Delphi group method, Analytical hierarchy structure, and fuzzy logic methods. Quantitative and fuzzy methods are used to evaluate the risk and vulnerability models for industrial system. The city environmental and industrial systems models were then combined to develop an integrated sustainable development model. For the city system total of 218 indicators were identified. The fuzzy model showed that Mahshahr city was at the medium level of sustainability (54.5%). For industrial system a total of 48 indicators were identified. The industrial system model indicated that Fajr PC was also a medium level of sustainability (56.1%). The results of the risk assessment showed a non-critical level of industrial vulnerability. The sustainability management model results, for Fajr PC from qualitative (65.9%) and fuzzy (57.2%) approaches were at the high and medium levels respectively. The integrated sustainability model of Fajr PC showed that the sustainability systems (50%) and sustainability assets (48.56%) were at medium levels. There was no significant difference between the two results. This study quantified and aggregated the city environmental and industrial sustainability systems into an integrated sustainable development model. This allowed the overall sustainability of the oil and petrochemical company within an urban environment to be estimated. This sustainable development study is the first carried out in Iran. The results can serve as benchmarks for the improvement. These indicators and models identified in this study may be employed for other sectors. This type of information can support decision making in order to assist government policymaking towards the sustainable development of coastal zones and oil and petrochemical industries