Fuzzy dynamic risk-based maintenance investment optimization for offshore process facilities

A new approach to chemicals warehouse risk analysis using computational fluid dynamics simulation and fuzzy Bayesian network

This study aims to assess the risk of chemicals warehouse using a Bayesian networks (BNs) and computational fluid dynamics (CFD). A methodology combining Bow-Tie (BT), fuzzy set theory (FST), and Bayesian network was employed, in which the BT was drawn for chemical spill scenarios. FST was utilized for the estimation of the basic events (BEs) occurrence probability, and the probability of interaction among a set of variables was obtained using BNs. Pool fire scenario radiation heat flux was evaluated using CFD code, fire dynamic simulator (FDS), and the solid flame model (SFM). Fail in forklift brake system (BE1), was the most significant cause for a chemical spill. Based on the CFD model, the heat flux is 31 kW/m² at a distance of 3.5 m from the fire, decreasing to 6.5 m gradually. The maximum safety distance of 4 m is predicted by the CFD for heat flux that exceeds 12.5 kW/m²; however, SFM predicts approximately 4.5 m. According to the results, the amount of posterior risk is higher than the prior value. The framework presented in the chemicals warehouse for consequence analysis and dynamic risk assessment (DRA) of pool fire could be used for preventing the accidents and domino effects in the chemicals warehouse.

Fuzzy Risk-Based Maintenance Strategy with Safety Considerations for the Mining Industry

Enterprises today are increasingly seeking maintenance management strategies to ensure that their machines run faultlessly. This problem is particularly relevant in the mining sector, due to the demanding working conditions of underground mines and machines and equipment-operating regimes. Therefore, in this article, the authors proposed a new approach to mining machinery maintenance management, based on the concept of risk-based maintenance (RBM) and taking into account safety issues. The proposed method includes five levels of analysis, of which the first level focuses on hazard analysis, while the next three are connected with a risk evaluation. The final level relates to determining the RBM recommendations. The recommendations are defined in relation to the three main improvement areas: maintenance, safety, and resource availability/allocation. The proposed approach is based on the use of fuzzy logic. To present the possibilities of implementing our method, a case study covering the operation of selected mining machinery in a selected Polish underground mine is presented. In the case of mining machinery, fourteen adverse-event scenarios were identified and investigated; general recommendations were also given. The authors have also indicated further directions of research work to optimize system maintenance strategies, based on the concept of risk-based maintenance. Additionally, the discussion about the implementation possibilities of the approach developed herein is provided.

Asset maintenance optimisation approaches in the chemical and process industries - A review

The operational performance of a chemical process plant highly depends on the assets' condition and maintenance practices. As chemical processes are highly complex systems, increasing the risk frequencies and their interactions, the maintenance planning becomes crucial for stable operation. This paper provides a critical analysis of the recently developed approaches for asset maintenance approaches in the chemical industry. The strategies include corrective maintenance, time-based, risk-based, condition-based and opportunistic maintenance. Various methods on selecting the optimal maintenance strategy are discussed as well. This paper also evaluates reliability issues in chemical plants and integrated sites encompassing the maintenance optimisation. Several directions for potential future improvements are proposed based on this analysis, as follows: (i) potential study of exploiting production or other opportunities to postpone or conduct earlier maintenance; (ii) joint optimisation of spare part ordering strategy and data-driven maintenance planning study is needed; (iii) fault propagation modelling of structural dependent units to facilitate proper maintenance planning; (iv) a framework or tool that consider quantitative and qualitative time-variant data inputs is lacking for business-informed asset maintenance.

A Fuzzy Markov Model for Risk and Reliability Prediction of Engineering Systems: A Case Study of a Subsea Wellhead Connector

In production environments, failure data of a complex system are difficult to obtain due to the high cost of experiments; furthermore, using a single model to analyze risk, reliability, availability and uncertainty is a big challenge. Based on the fault tree, fuzzy comprehensive evaluation and Markov method, this paper proposed a fuzzy Markov method that takes the full advantages of the three methods and makes the analysis of risk, reliability, availability and uncertainty all in one. This method uses the fault tree and fuzzy theory to preprocess the input failure data to improve the reliability of the input failure data, and then input the preprocessed failure data into the Markov model; after that iterate and adjust the model when uncertainty events occur, until the data of all events have been processed by the model and the updated model obtained, which best reflects the system state. The wellhead connector of a subsea production system was used as a case study to demonstrate the above method. The obtained reliability index (mean time to failure) of the connector is basically consistent with the failure statistical data from the offshore and onshore reliability database, which verified the accuracy of the proposed method.

An integrated fuzzy-AHP and TOPSIS approach for maintenance policy selection

Purpose

The purpose of this paper is to select the best maintenance policy for different types of equipment of a manufacturer integrating the fuzzy analytic hierarchy process (FAHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) models.

Design/methodology/approach

The decision hierarchy of this research includes three levels. The first level aims to choose the best maintenance policy for different types of equipment of an acid manufacturer. These equipment pieces include molten sulfur ponds, boiler, absorption tower, cooling towers, converter, heat exchanger and sulfur fuel furnace. The second level includes decision criteria of added-value, risk level and the cost. Lastly, the third level comprises time-based maintenance (TBM), corrective maintenance (CM), shutdown maintenance and condition-based maintenance (CBM) as four maintenance policies.

Findings

The best maintenance policy for different types of equipment of a manufacturer is the main finding of this research. Based on the obtained results, CBM policy is suggested for absorption tower, boiler, cooling tower and molten sulfur ponds, TBM policy is suggested for converters and heat exchanger and CM policy is suggested for a sulfur fuel furnace.

Originality/value

This research develops a novel model by integrating FAHP and an interval TOPSIS with concurrent consideration of added-value, risk level and cost to select the best maintenance policy. According to the highlights of the previous studies conducted on maintenance policy selection and related tools and techniques, an operative integrated approach to combine risk, added-value and cost with integrated fuzzy models is not developed yet. The majority of the previous studies have considered classic fuzzy approaches such as FAHP, FANP, Fuzzy TOPSIS, etc., which are not completely capable to reflect the decision makers’ viewpoints.

An alternative approach to safety integrity level determination: results from a case study

Purpose

The purpose of this paper is to validate a fuzzy risk graph model through a case study results carried out on a safety instrumented system (SIS).

Design/methodology/approach

The proposed model is based on an inference fuzzy system and deals with uncertainty data used as inputs of the conventional risk graph method. The coherence and redundancy of the developed fuzzy rules base are first verified in the case study. A new fuzzy model is suggested for a multi-criteria characterization of the avoidance possibility parameter. The fuzzy safety integrity level (SIL) is determined for two potential accident scenarios.

Findings

The applicability of the proposed fuzzy model on SIS shows the importance and pertinence of the proposed fuzzy model as decision-making tools in preventing industrial hazards while taking into consideration uncertain aspects of the data used on the conventional risk graph method. The obtained results show that the use of continuous fuzzy scales solves the problem of interpreting results and provides a more flexible structure to combine risk graph parameters. Therefore, a decision is taken on the basis of precise integrity level values and protective actions in the real world are suggested.

Originality/value

Fuzzy logic-based safety integrity assessment allows assessment of the SIL in a more realistic way by using the notion of the linguistic variable for representing information that is qualitative and imprecise and, therefore, ensures better decision making on risk prevention.