Hazardous Operations Research Articles

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

When training for hazardous operations, real-time stress detection is an asset for optimizing task performance and reducing stress. Stress detection systems train a machine-learning model with physiological signals to classify stress levels of unseen data. Unfortunately, individual differences and the time-series nature of physiological signals limit the effectiveness of generalized models and hinder both post-hoc stress detection and real-time monitoring. This study evaluated a personalized stress detection system that selects a personalized subset of features for model training. The system was evaluated post-hoc for real-time deployment. Further, traditional classifiers were assessed for error caused by indirect approximations against a benchmark, optimal probability classifier (Approximate Bayes; ABayes). Healthy participants completed a task with three levels of stressors (low, medium, high), either a complex task in virtual reality (responding to spaceflight emergency fires, n=27) or a simple laboratory-based task (N-back, n=14). Heart rate, blood pressure, electrodermal activity, and respiration were assessed. Personalized features and different interval window sizes were compared. Classification performance was compared for ABayes, support vector machine, decision tree, and random forest. The results demonstrate that a personalized model with time series intervals can classify three stress levels with high accuracy better than a generalized model. However, cross-validation and holdout performance varied for traditional classifiers vs. ABayes, suggesting error from indirect approximations. The selected features changed with windows size and differed between tasks, but blood pressure contributed to the most prominent features. The capability to account for individual difference is a prominent advantage of personalized models, and will likely have a growing presence in future detection systems.

Pre-control of Operational Hazards in Thermal Power Plant Centralized Control

Pre-control of Operational Hazards in Thermal Power Plant Centralized Control

Residual risk in Automatic Train Protection systems: evaluation and management

The paper on typical rail operation hazards, such as Signal Passed at Danger (SPAD) and Failed Braking Application by Driver (FBAD). The protection against these hazards is normally by Automatic Train Protection (ATP) systems and emergency braking activation, operated by the onboard systems over a certain Minimum Release Speed (MRS). The determination of such speed is a key design parameter related with the achievable performances in terms of safety and capacity. The present research deal with a systematic analysis of the operational conditions potentially affecting the determination of such speed values by modelling the dynamics of the problem taking into account the features of the infrastructures (tracks layout, gradients, etc.), the vehicles (mass, braking performances, etc.) and the geography of signalling and ATP systems themselves. In fact, the position of main signals protecting the dangerous points (switches, level crossings, etc.) emerges as key player, affecting both the design and the operation of the systems.

Microbial Lignocellulolytic Enzymes for the Effective Valorization of Lignocellulosic Biomass: A Review

The urgent demand for alternative energy sources has been sparked by the tremendous burden on fossil fuels and the resulting acute energy crisis and climate change issues. Lignocellulosic biomass is a copious renewable and alternative bioresource for the generation of energy fuels and biochemicals in biorefineries. Different pretreatment strategies have been established to overcome biomass recalcitrance and face technological challenges, such as high energy consumption and operational costs and environmental hazards, among many. Biological pretreatment using microbial enzymes is an environmentally benign and low-cost method that holds promising features in the effective pretreatment of lignocellulosic biomass. Due to their versatility and eco-friendliness, cellulases, hemicellulases, and ligninolytic enzymes have been recognized as “green biocatalysts” with a myriad of industrial applications. The current review provides a detailed description of different types of lignocellulolytic enzymes, their mode of action, and their prospective applications in the valorization of lignocellulosic biomass. Solid state fermentation holds great promise in the microbial production of lignocellulolytic enzymes owing to its energy efficient, environment friendly, and higher product yielding features utilizing the lignocellulosic feedstocks. The recent trends in the application of enzyme immobilization strategies for improved enzymatic catalysis have been discussed. The major bottlenecks in the bioprocessing of lignocellulosic biomass using microbial enzymes and future prospects have also been summarized.

Research on safety evaluation of assembly building construction by integrating entropy power method and network analysis model

Unlike the traditional construction mode of rough operation, assembly building construction implements the concept of green development in terms of energy consumption and environmental adaptability. Although assembly construction can effectively reduce construction energy consumption and improve the environmental resilience of building construction work, there is an urgent need for an effective safety assessment model for construction development due to the imperfect operation system and harsh construction environment in the construction industry. Therefore, the study analyzes the relationship of construction safety factors by using Analytic Network Process (ANP) to filter safety evaluation indexes according to the importance ranking. At the same time, the objective weights of safety indicators were determined by the entropy weight method, and the subjective weights determined by the ANP method were combined to construct the safety evaluation model for the construction of assembled buildings. The experiment shows that the maximum similarity between the comprehensive evaluation results of the model in the simulation of safety evaluation of high-rise residential construction and the actual evaluation criteria is 0.772. The experiment proves the reliability of the evaluation of the model, which reduces the safety loopholes and operation hazards for the construction of assembled buildings.

Estimating and visualizing the exposure to tower crane operation hazards on construction sites

As an increasing number of building components are modularized and prefabricated, tower cranes are required to lift larger and heavier loads. This situation increased the risk of crane failure and the severity of crane accidents, challenging the effectiveness of conventional reactive safety management approaches. Thus, early hazard recognition and proactive control (Prevention through Design, PtD) are necessitated to manage Tower Crane Operation (TCO) hazards. This study proposes an automatic PtD method to manage the Hazard Exposure (HE) brought by one or multiple tower crane operations on construction sites in the construction planning stage by leveraging a path-finding algorithm and Building Information Modelling (BIM)-enabled spatial–temporal analyses. The dynamic distribution of TCO hazards is estimated based on safe and practical lift paths generated by a path-finding algorithm, and visualized as HE heatmaps in BIM models. Three application scenarios of the HE estimation and visualization are explored, including managing critical lifts, improving daily hazard communication on site, and contextualizing scheduling/planning tasks with hazard information. Estimating and visualizing HE in these scenarios are expected to allow planners and safety managers arrange activities and workspaces to minimize the risks of TCO hazards. The contributions of the paper include (1) proposing a generic energy-based quantification model for different types of TCO hazards, (2) increasing the spatial–temporal granularity of the HE estimation and visualization, and (3) promoting proactive controlling measures over the TCO hazards through planning, scheduling, and the coordination of crane operations with other on-site activities.

Classification Method in Fault Diagnosis of Oil-Immersed Power Transformers by Considering Dissolved Gas Analysis

Fault detection in the incipient stage is necessary to avoid hazardous operating conditions and reduce outage rates in transformers. Fault-detected dissolved gas analysis is widely used to detect incipient faults in oil-immersed transformers. This paper proposes fault diagnosis transformers using an artificial neural network based on classification techniques. Data on the condition of transformer oil is assessed for dissolved gas analysis to measure the dissolved gas concentration in the transformer oil. This type of disturbance can affect the gas concentration in the transformer oil. Fault diagnosis is implemented, and fault reference is provided. The result of the NN method is more accurate than the Tree and Random Forest method, with CA and AUC values 0.800 and 0.913. This classification approach is expected to help fault diagnostics in power transformers.

Design and Evaluation of a Learning-Based Vascular Interventional Surgery Robot

Interventional therapy is one of the most effective methods for diagnosing and treating vascular-related diseases at present. It relies on achieving precise and safe navigation of intravascular tools within a patient’s vasculature. Vascular Interventional Surgical Robots (VISR) can reduce surgeons’ exposure to operational hazards including radiation. However, the absence of apt position control and force feedback remains a challenge. This study presents an isomorphic master–slave VISR for precise navigation of endovascular tools viz. catheters and guidewires. The master console aids operators in issuing manipulation commands and logs feedback from the force, rotation, and translation data. The slave manipulator uses the commands received from the master platform for actual tool navigation. However, precise master–slave position control and force feedback are precursors for optimal patient outcomes. This study utilized a fuzzy-PID controller for precise tool navigation and a neural network model for resistance force modulation with 50 mN precision. Furthermore, we evaluated the performance of using the learning-based models within our VISR and compared it with the performances from conventional methods. Results show that the models enhanced the proposed robotic system with better navigation precision, faster response speed, and improved force measurement capabilities.

Application of Digital Twin in Handling and Transportation of Hazardous Chemicals

In China, the Ministry of Transport stressed the need to “Strengthen the application of active safety technology”. The transportation of hazardous chemicals represented by LNG, LPG, and liquefied hydrocarbons is the weak link in traffic safety. The aim of this study is to apply digital twin (hereinafter referred to as DT) technology to the whole process of handling (including loading and unloading) and transportation of hazardous chemicals to help improve the anti-risk ability of road networks at all levels. The method is intended to design a monitoring system covering operation visualization, information fusion, cargo tracking, and hazard source monitoring that is based on DT technology and multi-source data acquisition technology. First, DT technology in the areas of hazardous chemicals handling and transportation is discussed. Then, the DT system is designed, including the system construction, functions, and the means of achieving these functions. Finally, taking the procedure in LNG road transportation as an example, we illustrate the application of DT in its four stages. This system is used to present the evolutionary path of accidents that occur in different links and assist in testing the rationality of the comprehensive disposal plan.

Synthetic Fault Mode Generation for Resilience Analysis and Failure Mechanism Discovery

Abstract Traditional risk-based design processes seek to mitigate operational hazards by manually identifying possible faults and devising corresponding mitigation strategies—a tedious process which critically relies on the designer’s limited knowledge. In contrast, resilience-based design seeks to embody generic hazard-mitigating properties in the system to mitigate unknown hazards, often by modelling the system’s response to potential randomly generated hazardous events. This work creates a framework to adapt these scenario generation approaches to the traditional risk-based design process to synthetically generate fault modes by representing them as a unique combination of internal component fault states, which can then be injected and simulated in a model of system failure dynamics. Based on these simulations, the designer may then better understand the underlying failure mechanisms and mitigate them by design. The performance of this approach is evaluated in a model of an autonomous rover, where cluster analysis shows that elaborating the faulty state-space in the drive system uncovers a wider range of possible hazardous trajectories and failure consequences within each trajectory than would be uncovered from manual mode identification. However, this increase in hazard information gained from exhaustive mode sampling comes at a high computational expense, highlighting the need for advanced, efficient methods to search and sample the faulty state-space.

Integrated Geomechanical and Digital Photogrammetric Survey in the Study of Slope Instability Processes of a Flysch Sea Cliff (Debeli Rtič Promontory, Slovenia)

This work presents an integrated study approach that combines the results of a geomechanical survey with data obtained using digital photogrammetry (DP), to assess slope instability processes affecting a sea cliff at the Debeli Rtič promontory (Slovenia). The investigated cliff is 4–18 m-high and is made up of an alternation of sandstones and marlstones belonging to the Flysch Formation of Trieste, which is Eocene in age. The studied cliff was subjected to localized slope failures that occurred in the past and is currently subject to frequent rock collapses, thus resulting in its partial and episodic retreat. Field evidence acquired through a traditional survey was integrated with outputs of the DP technique based on 1399 images that were collected using both a commercial unmanned aerial vehicle (UAV) and a mobile phone (MP). UAV-derived images were useful for performing rock mass structure analysis in the upper part of the investigated cliff, where the traditional survey was not possible due to hazardous operating conditions. In addition, the use of a MP was observed to be a useful tool for the rapid collection of images at the toe of unsafe marine cliff environments. This study highlights that UAV-DP and MP-DP techniques can only be effective if the outcomes obtained from the 3D model reconstruction are validated by direct measurements acquired by means of the traditional field survey, thus avoiding improper or even erroneous results while enlarging the amount of data and the area of investigation. The study approach presented herein allowed for the assessment of slope instabilities affecting the Flysch Sea cliff, whose retreat is caused by the combined action of marine erosion and slope gravitational processes.

Failure analysis and control of natural gas pipelines under excavation impact based on machine learning scheme

Third-party excavation operations pose a serious threat to the safe operation of natural gas pipelines, and quantifying the failure conditions of pipelines can effectively identify the hazards of excavation operations. Considering the current lack of test data on the full-size critical conditions of pipelines under different failure modes, to make the research results have better field application, this study aims to develop a failure prediction model, which is adopted for predicting the failure modes of the pipeline under different excavation conditions in order to propose control strategies. In this work, finite element analysis is combined with machine learning algorithms. The finite element analysis process derives the critical loads for different failure modes of the pipe and establishes the failure condition data set. Correlation analysis and sensitivity analysis were employed to investigate the influence pattern of the features. The prediction performance of different machine learning combination algorithms was tested, and a hybrid data-driven prediction model was established and combined with the excavation equipment parameters to determine the risk level of the excavation equipment and the risk area of the operation. The results demonstrate that the critical load value for failure grows when the strength of the pipe increases. The four features of yield strength, strength limit, pipe diameter, and wall thickness exhibit the highest importance scores. The bucket tooth wedge angle only influences the magnitude of the puncture critical load, with correlation and sensitivity coefficients being 0.315 and 0.116, respectively. In the test of the combined algorithm, the RFECV-CSVR-NSGAIII algorithm built in this study has the highest generalization performance, the mean absolute error percentage (MAPE) is lower than 0.0476, the coefficient of determination R2 reaches over 0.9960, and the prediction model has excellent accuracy. The prediction model was subjected to case studies to obtain rapid identification of risk levels and operational risk areas of excavation equipment.

Complementary Dense Datasets Acquired in a Low-to-Moderate Seismicity Area for Characterizing Site Effects: Application in the French Rhône Valley

Abstract Superficial geological layers can strongly modify the surface ground motion induced by an earthquake. These so-called site effects are highly variable from one site to another and still difficult to quantify for complex geological configurations. That is why site-specific studies can greatly contribute to improve the hazard prediction at a specific site. However, site-specific studies have historically been considered difficult to carry out in low-to-moderate seismicity regions. We present here seismological datasets acquired in the framework of the French–German dense array for seismic site effect estimation project in the heavily industrialized area surrounding the French Tricastin Nuclear Site (TNS). TNS is located above an ancient canyon dug by the Rhône River during the Messinian period. The strong lithological contrast between the sedimentary fill of the canyon and the substratum, as well as its expected confined geometry make this canyon a good candidate for generating site effects that are variable on short spatial scales. To investigate the impact of this geological structure on the seismic motion, we conducted complementary seismic campaigns in the area. The first main campaign consisted of deploying 400 nodes over a 10 × 10 km area for one month and aimed at recording the seismic ambient noise. A second seismic campaign involved the deployment of 49 broadband stations over the same area for more than eight months. This complementary campaign aimed at recording the seismicity (including local, regional, and teleseismic events). These different designs allowed us to target a variety of seismic data at different spatial and temporal scales. Beyond the interest for local operational seismic hazard applications, these datasets may be valuable for studying seismic wave propagation within complex kilometer-scale sedimentary structures. In this article, we present the deployment designs as well as initial analyses to provide information on the characteristics and the overall quality of the data acquired to future users.

Development of a Clinical Risk Score Prediction Tool for 5-, 9-, and 13-Year Risk of Dementia

Although researchers have devoted substantial efforts, money, and time to studying the causes of dementia and the means to prevent it, no effective treatment exists yet. Identifying preclinical risk factors of dementia could help prevent or delay its progression. To develop a point risk score prediction model of dementia. This study used a large UK population-based prospective cohort study conducted between March 13, 2006, and October 1, 2010. Data analysis was performed from June 7 to September 15, 2021. Individual analyses of time end points were concluded at the first dementia diagnosis during the follow-up period. The data were split into training and testing data sets to separately establish and validate a prediction model. Outcomes of interest included 5-, 9-, and 13-year dementia risk. Least absolute shrinkage and selection operator and multivariate Cox proportional hazards regression models were used to identify available and practical dementia predictors. A point risk score model was developed for the individual prediction of 5-, 9-, and 13-year dementia risk. A total of 502 505 participants were selected; the population after exclusions for missing data and dementia diagnosis at baseline was 444 695 (205 187 men; mean [SD] age, 56.74 [8.18] years; 239 508 women; mean [SD] age, 56.20 [8.01] years). Dementia occurrence during the 13 years of follow-up was 0.7% for men and 0.5% for women. The C statistic of the final multivariate Cox proportional hazards regression model was 0.86 for men and 0.85 for women in the training data set, and 0.85 for men and 0.87 for women in the testing data set. Men and women shared some modifiable risk and protective factors, but they also presented independent risk factors that accounted for 31.7% of men developing dementia and 53.35% of women developing dementia according to the weighted population-attributable fraction. The total point score of the risk score model ranged from -18 to 30 in men and -17 to 30 in women. The risk score model yielded nearly 100% prediction accuracy of 13-year dementia risk both in men and women. In this diagnostic study, a practical risk score tool was developed for individual prediction of dementia risk, which may help individuals identify their potential risk profile and provide guidance on precise and timely actions to promote dementia delay or prevention.

Hazard identification in fabrication industry using Hazard Identification and Risk Assessment (HIRA) and Job Safety Analysis (JSA)

Mild steel roofing is increasingly being used in simple construction processes. Prima Land Company dominates the lightweight steel roof market share in Pandeglang. However, accidents can come in its manufacturing process, and the potential hazards have yet to be fully measured. Therefore, this study aims to identify, assess, and provide risk control to prevent and reduce work accidents at Prima Land Company. The methods used are Hazard Identification and Risk Assessment (HIRA) and Job Safety Analysis (JSA). HIRA is used to identify the hazard in the mild steel roofing process area, whereas JSA is used to identify hazards in forklifts and lift operations. The results showed that eight activities have potential risks in manufacturing lightweight steel roofs and 16 risks in operating forklift machines and finished loading goods into vehicles. In the HIRA method, the risk category assessment is uniform. However, the highest risk of 38% is extreme risk for sand sprinkling activities, untied and closed plug cables or switches, and transfer of coil raw materials.

Numerical risk analysis of gas freeing process in oil/chemical tanker ships

Gas freeing is one of the most hazardous and critical operations on oil/chemical tankers. The process aims to remove toxic gases from tanks and bring oxygen to a healthy level (21%) in the tank atmosphere. Significant shipboard operations such as hot work or entry into the tank after tank cleaning require a successful gas-freeing process; otherwise, the consequences may be devastating for human life, cargo, ship, and the marine environment. Therefore, proactive planning and comprehensive risk assessment are essential to enhance operational safety onboard tanker ships. In this study, the potential hazards of the gas-freeing process are evaluated under the Fine–Kinney based intuitionistic fuzzy TODIM (Tomada de Decisão Interativa e Multicritério) approach. While the Fine Kinney and TODIM methods are used for calculating the risk score with the probability, exposure, and consequence parameters and determining the risk priority, intuitionistic fuzzy sets (IF) help to overcome uncertainty in human decision-making. The research findings are anticipated to guide maritime professionals, safety inspectors, and shipboard personnel in enhancing the gas-freeing process's safety and potential mitigating risks on oil/chemical tanker ships.

Detection and mitigation of field flooding attacks on oil and gas critical infrastructure communication

Industrial Cyber-Physical Systems (ICPS) are highly dependent on Supervisory Control and Data Acquisition (SCADA) for process monitoring and control. Such SCADA systems are known to communicate using various insecure protocols such as Modbus, DNP3, and Open Platform Communication (OPC) Data Access standards (providing access to real-time automation data), which are vulnerable to a range of attacks. This leads to increased cyber risks faced in critical infrastructures, especially in the Oil and Gas sector. One of the most popular and critical attacks deployed against such infrastructure is Denial of Service (DoS), as it can have severe consequences that range from financial loss to loss of life. Such attacks can disrupt the ability of an operator to control hazardous operations leading to potentially unsafe scenarios. A novel Field Flooding attack is described which takes advantage of the packet memory structure of the Modbus protocol to perform a DoS attack. This attack can cause overflowing of the memory bank allocated in the Programmable Logic Controller (PLC) for Modbus operations. The attack is deployed and evaluated on a real industrial testbed and its impact against the Mitre ATT&CK framework is assessed, in order to identify which tactics an adversary could use to compromise the system. A novel mechanism that utilises supervised machine learning to detect this attack in industrial control system networks is also described. Experimental results show that the proposed mechanism, using the XGBoost algorithm, can identify this attack with 99% accuracy.

Risk Assessment of Subcritical Water Hydrolysis (SWH) System for Sugar Recovery using Failure Modes and Effects Analysis (FMEA) Methods

The subcritical water hydrolysis (SWH) process has proven to be an effective method for sugar recovery from bakery waste. However, the natural principle of the process involving high pressure and temperature has made it a hazardous operation. Hence, failure mode and effect analysis (FMEA) has been applied to identify the potential failure modes in subcritical water hydrolysis (SWH) systems. The Risk Priority Number (RPN) approach was used to rate each potential problem during the SWH process. The reactor unit was found to have the highest potential for failure by RPN value of 800 with the 'failure effect analysis' on the potential reactor cap to explode due to the very high pressure inside the reactor that developed during SWH. The failure consequences may lead to injury or even death. As a result of the FMEA assessment approach and several corrective action plans, the failure risks of SWH can be reduced and managed effectively.

Reducing Operational Expenses of LoRaWAN-Based Internet of Remote Things Applications.

LoRaWAN has become the most widely used low-power wide-area network technology to implement monitoring solutions based on the Internet of remote things (IoRT) paradigm. Typically, these solutions interconnect remote sensing areas and data processing infrastructure located in urban centers. The operation expenses of these solutions depend mainly on the traffic sent through the network backhaul, i.e., the link that connects the remote sensing area and the urban area where the data are usually processed and stored. This service is provided by telecommunication companies and represents the main operation cost of IoRT solutions. These expenses usually limit the affordability of IoRT-based systems in developing countries, and also in scenarios where the operational cost is an issue to address. This paper presents an extension to the LoRaWAN protocol, named Node-Aware-LoRaWAN (NA-LoRaWAN), that reduces the traffic in the backhaul, thus decreasing the operational expenses of IoRT-based systems. In order to evaluate the performance of NA-LoRaWAN, it was compared to a regular LoRaWAN implementation. Depending on the network scenario, the proposed extension reduced the traffic through the backhaul in the range of 12–34%. This extension opens several opportunities to use IoRT solutions in application domains with a low operational budget, e.g., precision agriculture, environmental monitoring and natural hazards’ early detection.

Pre-collapse motion of the February 2021 Chamoli rock–ice avalanche, Indian Himalaya

Abstract. Landslides are a major geohazard that cause thousands of fatalities every year. Despite their importance, identifying unstable slopes and forecasting collapses remains a major challenge. In this study, we use the 7 February 2021 Chamoli rock–ice avalanche as a data-rich example to investigate the potential of remotely sensed datasets for the assessment of slope stability. We investigate imagery over the 3 decades preceding collapse and assess the precursory signs exhibited by this slope prior to the catastrophic collapse. We evaluate monthly slope motion from 2015 to 2021 through feature tracking of high-resolution optical satellite imagery. We then combine these data with a time series of pre- and post-event digital elevation models (DEMs), which we use to evaluate elevation change over the same area. Both datasets show that the 26.9×106 m3 collapse block moved over 10 m horizontally and vertically in the 5 years preceding collapse, with particularly rapid motion occurring in the summers of 2017 and 2018. We propose that the collapse results from a combination of snow loading in a deep headwall crack and permafrost degradation in the heavily jointed bedrock. Despite observing a clear precursory signal, we find that the timing of the Chamoli rock–ice avalanche could likely not have been forecast from satellite data alone. Our results highlight the potential of remotely sensed imagery for assessing landslide hazard in remote areas, but that challenges remain for operational hazard monitoring.