Perform Risk Assessment Research Articles

Modeling EV dynamic wireless charging loads and constructing risk constrained operating strategy for associated distribution systems

Dynamic wireless charging (DWC) is an emerging technology that enables the charging of electric vehicles (EVs) while they are in motion. However, previous load modeling methods have not thoroughly explored the detailed analysis of DWC load characteristics. Existing research only considers the single-node supply mode for dynamic wireless charging roads (DWCRs), and the assessment of operational risks arising from the uncertain DWC loads has not been addressed. This paper begins by conducting an equivalent circuit analysis of a typical EV DWC system with multiple segmented coils. We present a more accurate trapezoidal power model for a single EV. Subsequently, we model the aggregated EV DWC load, accounting for traffic flow and headway using Poisson and negative exponential distribution functions, respectively. In the operation process, we consider a multi-node supply mode for DWCRs. To address the inaccuracy of long-term predictions, we propose a rolling optimization model to coordinate DWC and renewables with heterogeneous uncertainties by introducing a risk metric to manage potential uncertain risks. The proposed optimization model is transformed into a mixed-integer second-order cone programming (MISOCP) problem after convex relaxation. Finally, we conduct case studies to validate the proposed methods.

Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality

Currently, the operational risk assessment of quay crane operators at ports relies on manual evaluations based on experience, but this method lacks objectivity and fairness. As port throughput continues to grow, the port accident rate has also increased, making it crucial to scientifically evaluate the risk behaviors of operators and improve their safety awareness. This paper proposes an automated evaluation method based on a Deep Q-Network (DQN) to assess the risk behaviors of quay crane operators in virtual scenarios. A risk simulation module has been added to the existing automated quay crane remote operation simulation system to simulate potential risks during operations. Based on the collected data, a DQN-based benchmark model reflecting the operational behaviors and decision-making processes of skilled operators has been developed. This model enables a quantitative evaluation of operators’ behaviors, ensuring the objectivity and accuracy of the assessment process. The experimental results show that, compared with traditional manual scoring methods, the proposed method is more stable and objective, effectively reducing subjective biases and providing a reliable alternative to conventional manual evaluations. Additionally, this method enhances operators’ safety awareness and their ability to handle risks, helping them identify and avoid risks during actual operations, thereby ensuring both operational safety and efficiency.

An operational risk assessment method for petrochemical plants based on deep learning

The petrochemical industry is an important guarantee for the development of people’s lives, and the operational risk assessment method of personnel in the operation of petrochemical enterprises is the most important. Based on a deep learning algorithm, a new method based on the micro-Doppler effect and fuzzy analytic hierarchy process is proposed to evaluate the operational risk of personnel in petrochemical enterprises. The original monitoring image of petrochemical equipment is invoked, and micro-Doppler analysis is performed based on the original image to identify the activity target of the personnel on the job site and generate the activity map of the petrochemical plant operators. Based on the analysis data of the micro-Doppler effect, the fuzzy function and hierarchical analysis method are combined. The fuzzy theory is introduced into the analytic hierarchy process, and the inherent expert evaluation scores are transformed into fuzzy numbers. It makes the expert evaluation of petrochemical plants more accurate in the subsequent coupling and improves the objectivity of the traditional analytic hierarchy process. The scheme proposed in this paper can monitor real-time operation safety and provide a guarantee for the personal safety of field operators. This method plays an important role in improving the safety of petrochemical plants.

A novel risk score based on exercise capacity to predict all-cause mortality in adults with congenital heart disease

Abstract Background Impaired exercise capacity in adults with congenital heart disease is associated with mortality. Thus, exercise capacity is a potential tool for predicting risk in this population. The present study attempts to further explore the usefulness of exercise capacity in risk assessment of all-cause mortality, together with a limited number of easily accessible clinical variables. Methods The national register of congenital heart disease was searched for adults (18-60 years of age) with at least one bicycle ergometer exercise test and follow up in the register. The exercise capacity was classified based on percent of predicted exercise capacity (EC%pred); normal (> 70%), moderately impaired (50-70%) or severely impaired (<50%). All patients were followed for 10 years or until death, if occurring earlier than 10 years after the exercise test. Data were analyzed using logistic regression modelling. A risk evaluation model was constructed based on percent of predicted exercise capacity, age, and complexity of the heart lesion. Finally, the model was converted into a 5-point (p) risk score used to predict 10-year mortality (EC%pred >70%=0p, 50-70=1p, <50%=2p, age <25=0p, 25-40=1p, 40-60=2p, simple lesion=0p, moderate/severe complexity of heart lesion=1p). Results 1171 patients (mean age at exercise test 30.5 years, 44% women) were included with 70 deaths occurring within 10 years. A score 0-1 corresponded to <1% 10-year all-cause mortality, 2p 5%, 3p 9%, 4p 21% and 5p 33% mortality risk (Table 1). The area under curve for the model was 0.75 (95% CI 0.69-0.81). Conclusion An easily performed risk assessment containing % of predicted exercise capacity, age and complexity of heart lesion performs well in detecting patients at both low and high risk of all-cause mortality within 10 years after an exercise test.

Hazard Identification and Risk Assessment During Simultaneous Operations in Industrial Plant Maintenance Based on Job Safety Analysis

The risk of accidents during simultaneous operations (SIMOPS) in plant maintenance has been increasing. However, research on methods to prevent such accidents has been limited. This study aims to develop a novel framework, hazard identification and risk assessment of simultaneous operations (HIRAS), for identifying and evaluating potential hazards during concurrent tasks. The framework developed herein is expected to be an effective safety management tool that can help prevent accidents during these operations. To this end, the job location and hazard information in job safety analysis (JSA) were standardized into four attributes. The standardized information was then synchronized spatially and temporally to develop a HIRAS model that identifies and assesses the impact of hazards between operations. The model was tested using 40 JSA documents corresponding to maintenance operations at Company P, a South Korean steel-making company. The model was tested in two scenarios: one with planned operations and the other with unplanned operations in addition to planned operations. The performance evaluation results of the first scenario showed an F1-score of 98.33%. In this case, a recall of 97.52% means that the model identified 97.52% of the hazard-inducing factors. The second scenario was compared with the results of a review by six subject matter experts (SMEs). The comparison of the results identified by the SMEs and the model showed an accuracy of 89.3%. This study demonstrates the potential of JSA, which incorporates the domain knowledge of workers and can be used not only for individual tasks but also as a safety management tool for surrounding operations. Furthermore, by improving the plant maintenance work environment, it is expected to prevent accidents, protect workers’ lives and health, and contribute to the long-term sustainable management of companies.

Risk Assessment of Polar Drillship Operations Based on Bayesian Networks

Risk Assessment of Polar Drillship Operations Based on Bayesian Networks

Design and implementation of an accelerating rate calorimeter based on Modelica modeling

The safety of self-reactive chemicals has garnered attention due to their immense destructive power. The use of an accelerating rate calorimeter allows for more accurate measurement and understanding of the kinetic parameters and reaction mechanisms of self-reactive chemicals, thereby reducing the occurrence of major accidents. In this study, a new accelerating rate calorimeter is designed and constructed by combining Modelica and a fuzzy Proportional-Integral-Derivative algorithm. The feasibility of the calorimeter data was verified by using two solutions with significantly different reaction rates: 20 % mass fraction di‑tert‑butyl peroxide/toluene and tert-butylperoxy-2-ethylhecanoate. Their thermal hazard characteristic parameters were compared with the literature data. In addition, the risk level of thermal runaway was determined using the Stoessel risk assessment method. These results demonstrate that the accelerating rate calorimeter based on Modelica modeling meets the accuracy of thermal hazard characteristic parameters. It is capable of performing risk assessments for runaway reactions of self-reactive chemicals.

Real-time risk assessment of distribution systems based on Unscented Kalman Filter

The continuous growth of renewable energy and the load level has posed increasingly severe operational risks to distribution systems. In view of this, this paper combines state estimation with risk assessment, and uses the results of distribution system state estimation based on Unscented Kalman Filter as the input of risk assessment. With the combination, the sampling based on probability distributions in traditional risk assessment methods is no longer needed, thus avoiding the difficulty of updating probability distributions timely according to the latest information in real-time operation. By applying the proposed risk assessment method, the real-time assessment of operational risks in perspectives of bus voltage, branch power, and renewable energy utilization is achieved. Meanwhile, the weight of each risk index is properly determined according to both subjective and objective knowledge by using Analytic Hierarchy Process method and entropy weight method. Case studies show that the proposed method achieves effective assessment of comprehensive risks in the operation of distribution system.

Early Operative Risk Assessment Using the Modified Mannheim Peritonitis Index in Patients With Generalised Peritonitis at a Tertiary Hospital in Abuja, Nigeria.

Introduction The use of validated prognostic scoring tools in resource-poor environments has been hampered by the cost of procuring and maintaining devices, such as arterial blood gas analysers, to measure critical physiological derangements.Attempts need to be made to adapt these applicable risk stratification tools so they can be easily adopted in developing countries like Nigeria. Aim This study assessed the usefulness of a modified Mannheim peritonitis index (mMPI) as arisk stratification tool in predicting surgical outcomes in managing patients with generalised peritonitis in Abuja, Nigeria. Methods This was a prospective study of consecutive adult patients managed for generalised peritonitis at a tertiary hospital in Abuja over 12 months. Approval numbers NHA/EC/037/2019 and AF/013/17/110/1514 were ascribed to the project by the Institute Review Board Committee (IBR) of the National Hospital Abuja (NHA) and the National Postgraduate Medical College of Nigeria. The MPI was modified using respiratory rate and suboptimal percentage oxygen saturation (SpO2). Patient characteristics and treatment outcomes obtained were entered into a structured proforma, checked, and analysed using IBM SPSS Statistics for Windows, Version 25.0 (Released 2017; IBM Corp., Armonk, New York, United States). The threshold score of the modified MPI and the accuracy, sensitivity, and specificity were derived from the receiver operating characteristic (ROC) curve analysis and its coordinates. The level of statistical significance was set at a p-value of <0.05. Results There were 49 patients with generalised peritonitis during the study period with a male-to-female ratio of 2.5:1. The commonest cause of peritonitis in this study was penetrating abdominal injury (n=15, 30.6%), followed by complicated appendicitis (n=12, 24.5%). This study's mortality and morbidity rates were 14.3% and 63.3%, respectively. On the ROC curve, the modified MPI best predicts mortality at a threshold score point of ≥26 (accuracy of 79.4%, sensitivity of 85.7%, specificity of 61.9%, p=0.013) and morbidity at a threshold score of ≥23 (accuracy of 78.4%, sensitivity of 77.4%, specificity of 72.2%, p=0.001). Conclusion The modified MPI can be used as a risk assessment tool to predict postoperative outcomes in adult patients operated on for generalised peritonitis within 30 days of operative intervention at Abuja.This modification may be helpful in low-resource centres with limited access to arterial blood gas analysers. However, the original MPI might be a more accurate tool.

Risk Assessment and Motion Planning for MAVs in Dynamic Uncertain Environments

Risk assessment to quantify the danger associated with a planned trajectory is critical for micro aerial vehicles (MAVs) navigating in dynamic uncertain environments. Existing works usually perform risk assessment by reasoning the occupancy status of the MAV’s surrounding space which only incorporates the position information of the MAV and the obstacles in the environment. In this paper, we further consider the MAV’s motion direction in risk assessment to reflect the fact that the obstacles in front of the MAV pose a higher risk while those behind pose a lower risk. In particular, we rely on a particle-based dynamic map which consists of a large number of particles to represent the local environment. The risk is defined to evaluate the safety level of a subspace in the map during some time interval and assessed by reasoning the occurrence of particles in the subspace. Those particles around the MAV are assigned different weights taking into account their relative positions to the MAV and its motion direction. We then incorporate the proposed risk assessment method into MAV motion planning by minimizing both the path length and the associated risk to achieve safer navigation. We compared our method with several state-of-the-art approaches in PX4+Gazebo simulations and real-world experiments. The results show that our method can achieve a 15% higher collision avoidance rate and a 20% lower flight risk in various environments with static and dynamic obstacles.

FP7.18 - Early Operative Risk Assessment Using Modified Mannheim Peritonitis Index in Patients with Generalised Peritonitis at a Tertiary Hospital in Abuja, Nigeria

Abstract Aim This study assessed the usefulness of a modified Mannheim Peritonitis Index (MPI) as a risk stratification tool in predicting surgical outcomes in the management of patients with generalized peritonitis in Abuja, Nigeria. Methods This is a prospective study of consecutive adult patients managed for generalized peritonitis at a tertiary hospital in Abuja Nigeria within a one-year duration. The MPI was modified using respiratory rate and SpO2. Patient characteristics and outcome of treatment obtained were entered into a structured proforma, checked and analysed using the SPSS, version 25. The threshold score of the modified MPI, as well as the accuracy, sensitivity, and specificity, were derived from the receiver-operator characteristic (ROC) curve analysis and its coordinates. Results There were 49 patients with generalised peritonitis during the study period with a male-to-female ratio of 2.5:1. The commonest cause of peritonitis in this study was penetrating abdominal injury (30.6%) followed by complicated appendicitis (24.5%). This study's mortality and morbidity rates were 14.3% and 63.3%, respectively. On the ROC curve, the modified MPI best predict mortality at a threshold score point of ≥26 (Accuracy of 79.4%, sensitivity of 85.7% specificity of 61.9%, P-value = 0.013), and morbidity at a threshold score of ≥23 (Accuracy 78.4%, sensitivity of 77.4%, specificity of 72.2%, P-value = 0.001). Conclusion The modified MPI can be used as a risk assessment tool in predicting postoperative outcomes in adult patients operated for generalised peritonitis within 30 days of operative intervention at Abuja. This modification can be useful in low-resource centres with limited access to arterial blood gas analysers.

Risk assessment of energy performance contracting in Türkiye utilizing best–worst method

Risk assessment of energy performance contracting in Türkiye utilizing best–worst method

Risk Assessment of Hospital Waste Water Treatment Plant Operation – A Case Study of a Class B Hospital in Indonesia

Risk Assessment of Hospital Waste Water Treatment Plant Operation – A Case Study of a Class B Hospital in Indonesia

Quantitative assessment rules and models for dynamic disaster risk in high-density gas gathering stations: Practical application in a largest CBM gathering station

Coalbed Methane (CBM) gathering stations involve multiple engineering disciplines, with a concentration of pressure vessels and strong production continuity. Once accidents occur, they can easily result in severe casualties, property losses, and environmental pollution. Therefore, prior to the large-scale promotion and urban application of CBM, this paper proposes a dynamic quantitative risk assessment model for the operational and task-related risks in CBM gathering stations. Firstly, various methods are employed for risk factor identification. Then, a dynamic Bayesian network (DBN) model is constructed to assess the dynamic operational risks and task-related risks of CBM gathering stations under different maintenance conditions. This model provides the initial probability of operational risk occurrence at stations, predicts changes in the probability of equipment multi-state operational risks under different maintenance conditions over 10 years, and forecasts the probability of task-related risks during operations. Finally, a combined weighting model is established, incorporating instance information to comprehensively evaluate safety management and derive safety management correction coefficients. These coefficients are integrated with the probabilities of station operation and task-related risk occurrence to ultimately determine the likelihood of operational risk occurrence during station production, completing the dynamic quantitative assessment of operational risks in CBM gathering stations.

Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems

To manage remotely piloted aircraft system (RPAS) networks effectively, this research presents a multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, and also introduces a standardized framework to clarify the RPAS CO2 emission model. These integrations significantly decrease battery consumption in Remotely Piloted Aircraft Systems (RPAS) and lower transportation costs, while also optimizing delivery times, reducing operational risks, and minimizing CO2 emissions. The model’s enhancement for optimizing delivery schedules takes into account uncertain traffic conditions, thus improving accuracy in dynamic environments and further contributing to environmental sustainability. Risk assessment employs the Specific Operations Risk Assessment (SORA) standard, adding a third objective function. This combination of the model, further enhance the efficiency and sustainability of RPAS operations, by optimizing delivery schedules, reducing CO2 emissions and battery consumption, and improving accuracy under dynamic conditions. Also, it make RPAS logistics more practical and effective in real-world applications. As result, the NSGA-II algorithm achieves significant reductions across all objectives: 33.3 % in cost, 6.48 % in time, 33.3 % in risk, and 35.7 % in battery usage within 250 generations. The use of the NSGA-II meta-heuristic method for validation enhances the credibility and practicality of the model. The optimization model’s performance over 250 generations shows rapid initial improvements in cost, time, risk, and battery usage, followed by stabilization, indicating efficient convergence and effective evolutionary computation. Also the findings show that with a CO2 emission rate of 3.773 × 104 kg of CO2 per Wh, highlighting the model’s efficiency and effectiveness.

Midpoint characterization factors to assess impacts of turbine water use from hydropower production

PurposeLife cycle assessment (LCA) distinguishes three types of water use: (1) consumptive water use, (2) degradative water use, and (3) in-stream water use. When it comes to assessing the impact of turbine water use (TWU, major source of in-stream water use) in LCA, so far, no method exists to quantify the related environmental impacts. Here, we developed the first midpoint characterization factors (CFs) with global coverage for turbine water use of storage and pumped storage hydropower power plants.MethodsThe midpoint CF at the basin scale describes the hydropower regulation potential (HRP) [HDOR·y] per TWU [m3]. The HRP indicates the probability of how strongly the natural flow regime of a river is potentially affected by all upstream reservoir operation, calculated as the quotient between reservoir volume [m3] and the annual river discharge [m3/y]. The hydropower degree of regulation (HDOR) thereby equals the unitless m3/m3 fraction. The TWU depends on the electricity production [kWh] and the turbine efficiency [m3/kWh]. We tested the sensitivity of the input data on the calculated CFs for four parameters (discharge, turbine efficiency, multipurpose allocation, and plant type). Furthermore, we performed a case study to analyze if consumptive and TWU impacts of producing 1 kWh are correlated or not.Results and discussionThe calculated CFs for the 342 basins vary from 1.13E-13 HDOR·y/m3 to 3.28E10-7 HDOR·y/m3. The HDOR values range from 0.0015 to 16.66, and the TWU varies between 0.0030 km3 and 2824 km3. A HDOR ≥ 0.02 can be interpreted as affected basin, and only 23 out of 342 basins have a HDOR below this threshold. This confirms that TWU of hydropower production can have important environmental impacts. The sensitivity analyses revealed that discharge and turbine efficiency are the most sensitive parameters because they are influencing almost all basins. The results of the case study showed that a high consumptive water-use impact does not automatically lead to a high TWU impact and vice versa (R2 values of 0.0081 and 0.003).ConclusionOur study highlights that it is important to account for the environmental impacts of in-stream water use in LCA, as otherwise, the environmental impact can be underestimated, which could lead to wrong conclusions. However, the CFs are not meant to replace a local risk assessment of hydropower reservoir operation and should only be used for relative comparison between basins. The CF application in LCA will represent a step forward towards more sustainable hydropower development.

Biodiversity reporting by United Kingdom (UK)‐listed companies: A review of extent, content and readability of disclosures

AbstractThis study examines biodiversity reporting by UK‐listed companies. An instrument is developed, based on earlier research, and used to analyse biodiversity‐related disclosures as either policy considerations or actions taken to conserve biodiversity. A mixed design is employed. The extent of reporting, information content of disclosures and their readability are used to provide an overarching perspective on how firms internalise biodiversity. Outliers are identified and their reports are examined interpretively to identify how leading reporters understand biodiversity as a policy imperative and factor it into their operational and managerial performances. This qualitative analysis highlights best practices that can be used by other entities interested in developing the accounting and management infrastructure necessary for reporting comprehensively on biodiversity. Best practices are, however, isolated and stand in stark contrast with how organisations are dealing with biodiversity on average. At the policy level, there is little on how biodiversity is defined and the rationale for wanting to protect biodiversity. When it comes to performance, few organisations are setting detailed biodiversity‐related targets linked clearly to operational plans, risk assessments and key performance indicators. Post‐implementation reviews of conservation initiatives are seldom conducted and biodiversity is not being factored into valuations, cost assessments and project appraisals. Understanding these limitations is timely given the international community's recent efforts to develop standards for extra‐financial reporting. The results will be relevant for regulators, scholars and practitioners interested in advancing the quality of biodiversity reporting and factoring biodiversity into mainstream business discourses.

Towards sustainable coastal management: a hybrid model for vulnerability and risk assessment

This paper presents the development of a Hybrid Model (HM) integrated with a Bayesian Network (BN) for comprehensive coastal vulnerability and risk assessment, with a focus on Konyaaltı Beach, Antalya, Turkey. The HM incorporates critical environmental parameters such as wind, waves, currents, and sediment transport to simulate conditions at vulnerable coastal areas and perform risk assessments for storm effects, flooding, and erosion. The model includes submodules for predicting coastal storms, quantifying sediment transport rates, assessing tsunami inundation severity, and categorizing storms based on beach typologies. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized for significant wave height predictions, enhancing the model's accuracy. The integration of hydrodynamic modeling, Bayesian networks, and ANFIS offers a robust framework for assessing coastal vulnerability and informing sustainable management practices. The study's results highlight the necessity for integrated risk management strategies, including adaptive infrastructure design, zoning and land use regulations, ecosystem-based management, and continuous monitoring and model refinement to enhance coastal resilience against dynamic environmental forces. This research provides valuable insights for mitigating the impacts of hazards on urban developments, contributing to the advancement of sustainable coastal management.

Workers’ Exposure to Chemical Risk in Small and Medium-Sized Enterprises: Assessment Methodology and Field Study

This study aimed to develop a state-of-the-art method for assessing chemical risks in small and medium-sized enterprises (SMEs). The method combined desk research findings and the authors’ practical knowledge to identify major shortcomings in performing risk assessments at the workplace. Data were collected from nine SMEs across three sectors: agriculture, laboratory research, and conservation and restoration laboratories. On-site observations, focus group discussions, and textual content analysis of occupational safety and health documentation were used to gather information on chemical risks and workers’ perceptions. The developed method uses numerical factors to account for the level of conformity with regulations and the duration of exposure to support an estimation of the probability of an incident. The method was designed to enhance risk assessment practices and tackle common issues encountered by SMEs, including the lack of data for estimating probability. Feedback from SMEs involved in the study indicated that the method’s performance was satisfactory, with its well-organized information flow and supportive components being particularly valued. The study highlights the importance of effective risk assessment in creating a safer and healthier work environment for workers.

Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective

In today’s industry, H2 is mostly produced from fossil fuels such as natural gas (NG), oil, and coal through various processes. However, all these processes produce both carbon dioxide (CO2) as well as H2, making them questionable in terms of climate change mitigation efforts. In addition to efforts to increase the conversion efficiency of green H2 technologies, work is also underway to make H2 production from fossil fuels more environmentally friendly by reducing/avoiding CO2 emissions. In this framework, these technologies are combined with geologic carbon storage. In a further step, the use of depleted hydrocarbon reservoirs for in situ H2 production is being investigated, with the co-generated CO2 remaining permanently in the reservoir. The objective of this paper is to provide a brief overview of the technologies that can be used to produce H2 from depleted and depleting hydrocarbon reservoirs (DHRs) in various ways. We evaluate the required processes from a reservoir engineering perspective, highlighting their potential for H2 generation and their technology readiness level (TRL) for applications. We also investigate the possibility of permanently storing the co-produced CO2 in the reservoir as a means of mitigating emissions. In addition, we provide a preliminary cost analysis to compare these methods with conventional hydrogen production techniques, as well as an assessment of operational risks and associated cost estimates.