Risk assessment and risk reduction of an acrylonitrile production plant

Reducing accident occurrence in petrochemical plants is crucial, thus appropriately allocating management resources to safety investment is a vital issue for corporate management as international competition intensifies. Understanding the priority of safety investment in a rational way helps achieve this objective.In this study, we targeted an acrylonitrile plant. First, Dow Chemical's Fire and Explosion Index (F&EI) identified the reaction process as having the greatest physical risk. We evaluated the severity of accidents in the reaction process using the Process Safety Metrics advocated by the Center for Chemical Process Safety (CCPS); however, this index does not express damages a company actually experience. To solve this problem, we proposed a new metric that adds indirect cost to CCPS metrics. We adopted fault tree analysis (FTA) as a risk assessment method. In identifying top events and basic events, we attempted to improve the completeness of risk identification by considering accidents from the past, actual plant operation and equipment characteristics, natural disasters, and cyber-attacks and terrorist attacks. Consequently, we identified the top events with high priority in handling because of serious accidents as fire/explosion outside the reactor, fire/explosion inside the reactor, and reactor destruction. The new CCPS evaluation index proposed in this study found that fire and explosion outside the reactor has the highest severity. We considered the creation of the fault tree (FT) diagram of the top event, estimating the occurrence probability, and identifying the risk reduction part and capital investment aimed at risk reduction. As an economically feasible selection method for risk reduction investment, using the difference in loss amounts before and after safety investments indicated investment priority.     

Safety assessment of the film boiling chemical vapor infiltration (FB-CVI) process through a system-theoretic accident model and process (STAMP)

Film boiling chemical vapor infiltration (FB-CVI) is considered as one of the fastest process methodologies for manufacturing carbon-carbon (C–C) composite products and possesses various advantages compared to conventional methodologies. However, there are safety concerns associated with this process for large-scale manufacturing, mainly owing to the intrinsic nature of the precursor and the process conditions. Considering the multifunctional interactions of the various systems during the process, a system-theoretic process analysis (STPA)/system theoretic accident model and process (STAMP) model is used to perform a safety analysis of the hazardous states of the FB-CVI process at the system level. As a case study, the FB-CVI process equipment employed for the manufacturing of C–C composites is considered. The safety constraints present in the system are assessed for adequacy through a hazard analysis by STPA/STAMP. The analysis through STPA/STAMP demonstrated the capability to create proactive strategies for the design and realization of process equipment that can be employed to manufacture C–C composite products through the FB-CVI process.     

A risk reduction approach for academic research labs: A case study on naphthenic corrosion

Academic research and development (R&D) labs are a significant part of academic life. But there can be physical, environmental, and experiment quality risks associated to this activity. Academic labs can present specific experiments, which have associated risks for researchers. Academic labs are also characterized by a high turnover of students and many of them are not fully aware of the level of physical and environmental risks of their activity. Accidents in academic labs with injuries and loss of life are facts that have to be tackled through risk management approaches. The objective of this paper is to present an integrated management approach, tackling risk management and analysis methods. HAZOP (Hazard and Operability Study) and PFMEA (Process Failure Mode and Effects Analysis) enabled, respectively, the analysis of safety and environmental risks. By quantifying the level of risk according to the type of experiment and the research context, it is possible to provide safety to the system. The resulting Digital Poka-Yoke – a mistake-proofing approach – has brought about the desired quality of results in experiments. The proposed approach was validated through a case study monitoring naphthenic corrosion experiments conducted by the Lab of Surface Electrochemistry and Corrosion (LSEC) at the Federal University of Paraná (UFPR). As a consequence, this approach is currently in use at this lab.     

Studies on polycyclic aromatic hydrocarbons in two sediment cores from the huaxi reservoir,china: Assessment of levels,sources, and ecological risk

This study investigated the levels, sources and ecological risks of 16 polycyclic aromatic hydrocarbons (PAHs) in two sediment cores that were collected along the Huaxi Reservoir. The spatial distributions and residue levels of the 16 priority PAHs in the sediments from the Huaxi Reservoir were analyzed for their potential ecological risk, source apportionment and contribution to the total PAH residue. The concentration level of the total PAHs (TPAHs) was in the range 1805 ng·g^?1 to 20023 ng·g^?1 based on dry weight, and the content of PAHs in the Huaxi Reservoir exhibited a gradual upward trend. The PAH congener ratios fluoranthene/(fluoranthene + pyrene) and indeno[1, 2, 3-cd]pyrene/(indeno[1, 2, 3-cd]pyrene + benzo[g, h, i]perylene) were used to identify the source. The main source of the low molecular weight PAHs was wood and coal combustion, whereas the high molecular weight PAHs were primarily from petroleum combustion sources. The results of an ecological risk assessment demonstrated that ACE poses a potential ecological risk, while FLU, NAP, ANT, BaP, DBA, PHEN and PYR can have serious ecological risks.     

Optimization of facility location and reallocation in an industrial plant through a multi-annual framework accounting for economic and safety issues

The industrial layout traditionally has been addressed accounting for the facilities distribution and installation since the first day of operation of the plant; this is, without considering future expansions that involve additional facilities in the future operation years. This way, this paper proposes a mathematical programming formulation for the optimal facility sitting and reallocation in an industry accounting for future expansions and involving simultaneously economic and safety objectives. The proposed formulation is based on a multi-annual framework and this corresponds to a multi-objective mixed integer linear programming problem. The proposed optimization approach was applied to a case study for the facility sitting (office buildings and control rooms) in an ethylene oxide plant. The economic objective function involves the minimization of the total annual cost accounting for the value of the money through the time and the safety objective function involves the minimization for the accumulated risk over the operation time. Results show the applicability of the proposed approach.     

Risk assessment of sour gas inter-phase onshore pipeline using ANN and fuzzy inference system – Case study: The south pars gas field

Nowadays, pipelines have been extensively used for transporting oil and gas for long distances. Therefore, their risk assessment could help to identify the associated hazards and take necessary actions to eliminate or reduce the risk. In the present research, an artificial neural network (ANN) and a fuzzy inference system (FIS) were used to prepare a new model for pipeline risk assessment with higher accuracy. To reach this objective, the Muhlbauer method, as a common method for oil and gas pipeline risk assessment, was used for determining important and influential factors in the pipeline performance. Mamdani fuzzy model was developed in Matlab software by considering expert knowledge. The outcomes of this model were used to develop an ANN. To verify the developed model, the inter-phase shore pipe of phase 9–10 refinery in the South Pars Gas field was considered as a case study. The results showed that the proposed model gives a higher level of accuracy, precision, and reliability in terms of pipe risk assessment.     

How can process safety and a risk management approach guide pandemic risk management?

The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises.     

Uncertainties in QRA: Analysis of losses of containment from piping and implications on risk prevention and mitigation

Quantitative Risk Assessment (QRA) is commonly used in the chemical industry to support decision-making. Common practices are based on standard methods, such as fault tree, event tree, etc.; in this frame, risk is a function of frequency of events (probability) and associated consequences (negative outcomes), but relevant uncertainties often are not properly taken into account in the derived results. This paper presents the application of an extended risk analysis of loss of containments for a case-study with the following aims: firstly, the uncertainties related to the results of the analysis, which derive from assumption in the application of the standard models, are qualitatively assessed; secondly the application allows evaluating the impact of the uncertainties on the trustworthiness of the results and, finally, commenting about their use in the risk prevention and mitigation.     

A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis

IntroductionWith the development of industries and increased diversity of their associated hazards, the importance of identifying these hazards and controlling the Occupational Health and Safety (OHS) risks has also dramatically augmented. Currently, there is a serious need for a risk management system to identify and prioritize risks with the aim of providing corrective/preventive measures to minimize the negative consequences of OHS risks. In fact, this system can help the protection of employees’ health and reduction of organizational costs. Method: The present study proposes a hybrid decision-making approach based on the Failure Mode and Effect Analysis (FMEA), Fuzzy Cognitive Map (FCM), and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) for assessing and prioritizing OHS risks. After identifying the risks and determining the values of the risk assessment criteria via the FMEA technique, the attempt is made to determine the weights of criteria based on their causal relationships through FCM and the hybrid learning algorithm. Then, the risk prioritization is carried out using the MOORA method based on the decision matrix (the output of the FMEA) and the weights of the criteria (the output of the FCM). Results: The results from the implementation of the proposed approach in a manufacturing company reveal that the score at issue can overcome some of the drawbacks of the traditional Risk Priority Number (RPN) in the conventional FMEA, including lack of assignment the different relative importance to the assessment criteria, inability to take into account other important management criteria, lack of consideration of causal relationships among criteria, and high dependence of the prioritization on the experts’ opinions, which finally provides a full and distinct risk prioritization.     

Multi-criteria decision-making considering risk and uncertainty in physical asset management

In this work we present a method for risk-informed decision-making in the physical asset management context whereby risk evaluation and cost-benefit analysis are considered in a common framework. The methodology uses quantitative risk measures to prioritize projects based on a combination of risk tolerance criteria, cost-benefit analysis and uncertainty reduction metrics. There is a need in the risk and asset management literature for a unified framework through which quantitative risk can be evaluated against tolerability criteria and trade-off decisions can be made between risk treatment options. The methodology uses quantitative risk measures for loss of life, loss of production and loss of property. A risk matrix is used to classify risk as intolerable, As Low As Reasonably Practicable (ALARP) or broadly tolerable. Risks in the intolerable and ALARP region require risk treatment, and risk treatment options are generated. Risk reduction benefit of the treatment options is quantified, and cost-benefit analysis is performed using discounted cashflow analysis. The Analytic Hierarchy Process is used to derive weights for prioritization criteria based on decision-maker preferences. The weights, along with prioritization criteria for risk reduction, tolerance criteria and project cost, are used to prioritize projects using the Technique for Order Preference by Similarity to Ideal Solution. The usefulness of the methodology for improved decision-making is illustrated using a numerical example.