Abnormal Situation Management Research Articles

A review of Faisal Khan's contribution to process monitoring

AbstractProcess monitoring is pivotal in process system engineering for abnormal situation management and ensuring process safety. This paper presents a review of Professor Khan's works on process monitoring. It examines (i) the number of publications, (ii) the type of publications, (iii) key sources, (iv) focused areas and their evolvement, and (v) the research impact by Professor Khan in process monitoring. The results suggest that journals are the primary sources he has used to disseminate research results. Over the years, his research focus evolved from detection to root cause diagnosis, fault propagation pathway analysis, and failure prognosis. Professor Khan has immensely impacted his peers, evidenced by his theoretical contributions, a higher number of recognitions by other researchers, and diversified workforce development.

Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects

In the chemical process, abnormal situations are precursor events of incidents and accidents. Abnormal situation management (ASM) can effectively identify abnormalities and prevent them from evolving into incidents or accidents, ensuring the safe and smooth operation of chemical plants. In recent years, ASM has attracted extensive attention from the process industry and from academia, and a lot of research work has been conducted. However, the intelligence level of ASM in actual chemical plants is still relatively low, and industrial applications still face many difficulties and challenges. This review first summarizes the concepts and involved in the contents of ASM. Then, the latest research progress in various aspects of ASM is systematically reviewed. Finally, the challenges and future research directions of ASM are analyzed based on the perspective of industrial application. This review aims to provide the most cutting-edge reference for follow-up research on ASM, and to promote the intelligent development and practical industrial application of ASM in the chemical process.

ProTopormer: Toward Understandable Fault Diagnosis Combining Process Topology for Chemical Processes

In modern chemical processes, fault detection and diagnosis (FDD) is a key part of abnormal situation management (ASM). As researchers continue to improve the fault diagnosis performance of different models, the emphasis on model interpretability and explainability studies has increased in recent years. In this paper, a novel model, ProTopormer, was proposed for fault diagnosis of chemical processes. Self-attention mechanism and process topology knowledge were fully combined to achieve high model performance and good interpretability. Experiments on Tennessee Eastman process showed that the model achieved a high diagnosis rate and a low false alarm rate. Attention weights were visualized and quantitatively analyzed to identify key variables for fault diagnosis, which showed strong explanations for the root causes of different faults.

Process safety analysis using operational data and Bayesian network

AbstractFault detection and diagnosis (FDD) methods have recently experienced significant advances. These methods provide valuable information from an abnormal situation management perspective. However, traditional FDD methods do not consider system failure analysis, which is required from the process safety perspective. This work seeks to overcome this barrier and presents a methodology to assess process system failure probability based on process operational data and system knowledge. The methodology is built using the principal component analysis (PCA) and a Bayesian network (BN). The PCA is used for FDD, while the BN determines the probability of system failure once a fault is detected. This portion of the network is based on the logical relationship of the data, operational thresholds, and system failure conditions. The proposed methodology is tested and verified on a level‐controlled tank system and the real‐life failure scenario of the 2010 Tesoro heat exchanger explosion. The results suggest that the proposed methodology helps to assess process system failure probability based on process operating conditions. The current work is expected to give a stimulus on digital process safety education.

Influence of the Clinical Nursing Pathway on Nursing Outcomes and Complications of Cervical Carcinoma Patients Undergoing Chemotherapy via PICC.

Background Cervical Carcinoma (CC) is the second most common cause of death in women, with most patients being diagnosed at an advanced stage. The conventional treatment for CC, with a long chemotherapy treatment cycle, is less than satisfactory and will cause serious damage to the patient's blood vessels. Objective To analyze the impact of the clinical nursing pathway (CNP) on the incidence of complications and adverse prognosis in patients undergoing chemotherapy for CC via peripherally inserted central catheters (PICC). Materials and Methods This study enrolled 157 CC patients who underwent PICC chemotherapy in the Shaanxi Provincial Cancer Hospital between March 2017 and April 2020 and assigned them between the two groups according to different nursing interventions. Ninety-three patients treated with CNP intervention were included in the research group (RG), and sixty-four cases treated with the routine nursing intervention were included in the control group (CG). The self-care ability and intervention satisfaction of patients were assessed using the self-care ability scale and the intervention satisfaction questionnaire, respectively, both developed by our hospital. The complication rate was observed in both cohorts, and the adverse prognosis of patients was statistically analyzed. Finally, an assessment was made on the patients' quality of life (QOL) using the quality of life questionnaire core 30 (QLQ-C30). Results Higher scores of self-management information, catheter nursing ability, self-care compliance, and abnormal situation management were determined in RG after the nursing intervention. RG also outperformed CG in the overall incidence rates of complications and poor prognosis. Moreover, RG presented statistically higher nursing satisfaction and QLQ-C30 scores than CG after the nursing intervention. Conclusion CNP has a significant nursing effect on patients with CC treated with PICC chemotherapy, which can not only reduce the incidence of postchemotherapy complications but also improve patient prognosis, satisfaction, and life quality, with the value for clinical promotion.

Decision support for process operators: Task loading in the days of big data

Modern chemical processes rely on distributed control systems to make the repetitive and routine adjustments to maintain steady operation. Operators are still required to “supervise the (system) supervisor” and intervene when variables exceed pre-programmed parameters to avert major incidents. Research in human-computer interaction and advanced process control has often focused on data-driven methods for fault detection as distinct from operator effectiveness. In this paper, we explore the application of a novel data-driven fault-detection technique to enhance operator decision support. During a simulated abnormal event, three users attempted to diagnose the root cause of a process upset using a traditional or standard interface, then with the addition of causal maps, in a A-B-A single-subject design. The causal maps were derived using a hierarchical method that could be applied to a wide range of chemical processes as an online, adaptive augmentation for abnormal situation management. Using a think-aloud technique, the three participants developed high quality insights into the process without negatively impacting the overall task load. These preliminary findings challenge prevailing wisdom in process control interface design, which often focuses on de-cluttering displays at the cost of information resolution.

Metrics for objectively assessing operator training using eye gaze patterns

Process plant operators rely on their knowledge of process cause-and-effect relationships during abnormal situation management. Novice operators develop such process knowledge during training. Hence, holistic assessment of operators’ training is essential to ensure process safety. Currently, during training, operators’ process understanding is evaluated using criteria such as successful completion, task based measures, and operator actions that ignore their cognitive behavior. In this work, we propose an eye-tracking-based approach that uses the operator’s attention allocation during different pre-specified training scenarios along with process data, alarm information, and operator actions. Our approach is based on the precept that an operator would focus their attention on those variables on the human-machine interface that they believe have a direct causal relationship to the situation at hand. Also, expert operators seek time-based information for proactive monitoring. Accordingly, to quantify the progress of a novice operator’s learning, we develop two metrics — association metric and salience metric — using correspondence analysis of operators' eye gaze. To evaluate the applicability of the metrics, we conducted experiments with ten participants who performed 486 tasks. Statistical studies reveal that the proposed metrics can quantify operators’ learning and thus can be used to objectively evaluate training effectiveness.

A process fault diagnosis method using multi‐time scale dynamic feature extraction based on convolutional neural network

AbstractUnlike many other techniques used in process control, which are widely applied in practice and play significant roles, abnormal situation management (ASM) still relies heavily on human experience, not least because the problem of fault detection and diagnosis (FDD) has not been well addressed. In this paper, a process fault diagnosis method using multi‐time scale dynamic feature extraction based on convolutional neural network (CNN) consisting of similarity measurement, variable ranking, and multi‐time scale dynamic feature extraction is proposed. The CNN‐based model containing the fixed multiple sampling (FMS) layer can extract dynamic characteristics of process data at different time scales. The benchmark Tennessee Eastman (TE) process is used to verify the performance of the proposed method.

Abnormal Situation Management: Detection of Fouling and Longitudinal Bypass in Heat Exchangers

Abnormal Situation Management: Detection of Fouling and Longitudinal Bypass in Heat Exchangers

Quantifying situation awareness of control room operators using eye-gaze behavior

In an attempt to improve process safety, today’s plants deploy sophisticated automation and control strategies. Despite these, accidents continue to occur. Statistics indicate that human error is the predominant contributor to accidents today. Traditionally, human error is only considered during process hazard analysis. However, this discounts the role of operators in abnormal situation management. Recently, with the goal to develop proactive strategies to prevent human error, we utilized eye tracking to understand the situation awareness of control room operators. Our previous studies reveal the existence of specific eye gaze patterns that reveal operators’ cognitive processes. This paper further develops this cognitive engineering based approach and proposes novel quantitative measures of operators’ situation awareness. The proposed measures are based on eye gaze dynamics and have been evaluated using experimental studies. Results demonstrate that the proposed measures reliably identify the situation awareness of the participants during various phases of abnormal situation management.

Integrated Data (i-Data), Mining and Utilization Approach for Effective Flare Management Strategies

Upset emissions occur during plant startup, shutdown, maintenance, malfunction, and flaring incidents. A wide range of these upsets cannot be managed by standalone control systems; plant personnel intervention is necessary sometimes. The methods needed to assist plant personnel to control and prevent abnormal process operations are gathered under abnormal situation management. Abnormal operations that lead to flare have significant economic, environmental, and safety impacts. Flaring is necessary for managing process upsets, however, it leads to the emission of greenhouse gases (GHG) and volatile organic compounds (VOCs), causing negative social impacts and local transient air pollution. In addition, excessive flaring results in energy and raw material losses. These are valuable commodities that must be sustained. Therefore, flare minimization during normal and abnormal operational situations has great environmental, industrial, and societal benefits. It is not possible to quantify the impacts without und...

Automata-based operating procedure for abnormal situation management in batch processes

“Abnormal situation management” (ASM) in general refers to the various tasks required for online fault diagnosis and also hazard mitigation. Although quite a few ASM-related studies have already been carried out in the past, none of them addressed the wide range of issues consistently and rigorously with the same modeling tool. An automata-based strategy is therefore proposed in this work to synthesize all operating procedures needed for diagnostic tests and also other emergency response operations in the batch processes. The proposed model building techniques are suitable not only for characterizing all components in any given process, but also for representing the operation targets of all ASM tasks. Finally, notice that every resulting procedure can be readily expressed with an implementable sequential function chart (SFC).

Abnormal situation management: Challenges and opportunities in the big data era

Abstract Although modern chemical processes are highly automatic, abnormal situation management (ASM) still heavily relies on human operators. Process fault detection and diagnosis (FDD) are one of the most important issues of ASM but few FDD systems have been satisfactorily applied in real chemical processes since the concept of FDD was proposed about 40 years ago. In this paper, developments of chemical process FDD are briefly reviewed. The reason why FDD has not been widely implemented in the chemical process industry is discussed. One of the insights gained is that some basic problems in FDD such as how to define faults and how many faults to diagnose have not even been addressed well while researchers tirelessly try to invent new methods to diagnose fault. A new framework is proposed based on the big data in a cloud computing environment of a big chemical corporation for addressing the challenging issues in ASM.

Integration of Energy and Wastewater Treatment Alternatives with Process Facilities To Manage Industrial Flares during Normal and Abnormal Operations: Multiobjective Extendible Optimization Framework

This work reports an extendible multiobjective optimization framework to find the optimal configuration of energy utilization and wastewater treatment facility of the process. It incorporates two sustainable energy integration alternative tools, i.e., a cogeneration (COGEN) unit and thermal membrane distillation (TMD), as available add-ons to the process during normal operation and for abnormal situation management. The objective of the framework is to reduce the environmental footprint of abnormal flares by enumerating and assessing possible process configurations in order to manage flares from uncertain sources and to utilize unused energy resources for wastewater treatment. The core of this optimization framework is developed using a genetic algorithm and its objective function is aimed at minimizing the total annualized cost which accounts for the fixed and operating costs of the system, the value of produced coproducts (i.e., power, wastewater treatment savings, income from permeate), and taxes/credits associated with greenhouse gases. An ethylene process plant is used to demonstrate the applicability of the developed framework. The results of different alternative configurations demonstrate the economic, energetic, and/or environmental trade-offs of integrating TMD and the COGEN unit with the process plant both for flare mitigation and during normal operation. It was seen that the total annualized cost (TAC) dropped around 35% and the payback period reduced from 7.01 to 4.61 years when an integrated process plant (ethylene plant), utility unit (COGEN), and wastewater treatment facility (TMD) was considered instead of separate divisions. Moreover, utility savings were achieved up to 8% and annual incomes from coproducts were increased around 20% for the integrated ethylene plant, COGEN, and TMD unit. Besides, prolific recycling opportunities of unused flare streams and treated wastewater were identified to make some valuable products from waste streams.

The Plant Simulator as viable means to prevent and manage risk through competencies management: Experiment results

Making decisions and managing competences in complex systems is a challenging task to accomplish. Specifically, the process industry is known for its complexity and sensitivity to critical procedures. Recent disasters like the “Deepwater Horizon” (2010, 11 fatalities), BP Texas City (2005, 15 fatalities), and AZF Toulouse (2001, 29 fatalities), clearly showed the risk to which we are all exposed. The increasing complexity of processes, due to the simultaneous escalation of automation, optimisation and intensification processes (followed to face globalisation challenges), are moving the attention to the management of abnormal situations, which are even more complex in nature and frequent. This increasing complexity, coupled with the fact that abnormal situations may lead to irreversible losses, is imposing the adoption of adequate approaches and tools that allow for better learning and properly managing abnormal situations. The paper presents a simulation-enabled, experiment-based approach that can be used to prevent and manage risk through competencies management. More specifically, the paper presents the results of the first experiment campaign performed in a Plant Simulator (PS), the first known in the process industry domain, and shows the efficacy of using Immersive Virtual Environments (IVE) both to make decisions and to train teams (not just single operators).The experiment results presented in the paper show the effectiveness of IVE to increase the competencies and train operators and managers. In addition, they explain how conveniently the data collected by means of the PS can be used for making daily decisions to better prevent and manage risks.

Fault propagation behavior study and root cause reasoning with dynamic Bayesian network based framework

The Bhopal disaster was a gas leak incident in India, considered the world's worst industrial disaster happened around process facilities. Nowadays the process facilities in petrochemical industries have becoming increasingly large and automatic. There are many risk factors with complex relationships among them. Unfortunately, some operators have poor access to abnormal situation management experience due to the lack of knowledge. However these interdependencies are seldom accounted for in current risk and safety analyses, which also belonged to the main factor causing Bhopal tragedy. Fault propagation behavior of process system is studied in this paper, and a dynamic Bayesian network based framework for root cause reasoning is proposed to deal with abnormal situation. It will help operators to fully understand the relationships among all the risk factors, identify the causes that lead to the abnormal situations, and consider all available safety measures to cope with the situation. Examples from a case study for process facilities are included to illustrate the effectiveness of the proposed approach. It also provides a method to help us do things better in the future and to make sure that another such terrible accident never happens again.

Increasing the Effective Span of Control: Advanced Graphics for Proactive, Trend-Based Monitoring

OCCUPATIONAL APPLICATIONS Legacy status display formats limit the number of processes that human operators can effectively and proactively monitor. Through a design approach targeting the needs of human attention, a novel trend–icon hybrid status display format (Trendicon; patent pending; Fisher Rosemount Systems, Inc., Round Rock, TX) was developed for proactive monitoring. An experiment contrasting Trendicons with digital values and trend graphs revealed that after minimal training, Trendicons supported earlier problem detections and more frequent proactive detections. Further, Trendicons led to a roughly fivefold increase in process indicators that participants could effectively oversee. Practical implications include earlier problem detection, improved system operation, and an increased operator span of control. Participants’ incorrect initial intuitions about display effectiveness highlight potential pitfalls of flexible, configurable displays. Trendicons are being developed for process and military control displays. The attention management design approach taken here is applicable to the design of supervisory status views for display-mediated work domains.TECHNICAL ABSTRACT Background: Process control operators rely heavily on their visual displays to monitor and stay abreast of processes and situations, anticipate future status, and proactively detect and resolve emerging problems. However, fielded status displays support only a subset of these tasks and can engender reactive rather than proactive monitoring because of inadequate attention management. Reactive monitoring is a significant barrier to effective operations and increasing operator span of control. Purpose: The benefits of an attention management design approach are demonstrated through the development and evaluation of a novel trend–icon hybrid (Trendicon) display format to support proactive monitoring. Methods: A within-subjects experiment contrasted the effectiveness of three status indicator formats—digital values, trend graphs, and Trendicons—in supporting proactive detection of emerging deviations. Span of control was assessed by varying the number of concurrently monitored status indicators (4, 12, and 24). Participants’ intuitions and preferences for the indicator formats were assessed as a proxy for display configuration choices. Results: Monitoring was largely reactive for digital values and trend graphs. In contrast, Trendicons promoted earlier problem detections and more frequent proactive detections, leading to a nearly fivefold increase in effective span of control. However, participants initially underestimated the utility of Trendicons. Conclusions: By explicitly designing attention management into new representations of process status, a dramatic increase in effective span of control was achieved over legacy formats. Better allocation of limited attentional resources during monitoring helped shift the nature of monitoring from reactive to proactive. Miscalibrated performance intuitions and preferences demonstrate that designers must consider both performance and user beliefs about display effectiveness. Development of Trendicons is ongoing in process and military control display applications. Designing for and addressing the underlying cognitive challenges of display-mediated supervisory control can result in more effective system operation, safer management of abnormal situations, and increased productivity.

Addressing alarm flood situations in the process industries through alarm summary display design and alarm response strategy

Despite efforts to improve alarm systems, alarm flooding remains a significant problem in the process industries. Alarm summary displays for managing alarm floods do not fully support operator needs when responding to plant upsets. This Abnormal Situation Management Consortium (asmconsortium.org) funded study tested two alarm summary display designs in a simulated process control environment using twenty-four certified operators. The first display represented the traditional list-based alarm summary display typically used in control rooms. The second display was a new alarm tracker summary display, which showed alarms in a time series represented by icons and a short alarm description. Results of the simulated evaluation showed that when operators used a formal alarm response strategy that focused the new alarm tracker summary display by equipment area, they responded to more process events overall and had fewer false responses compared to when operators used the traditional list-based alarm summary. Relevance to industryNew alarm summary displays can combine the benefits of list-based displays with time series presentation of alarm information. Process operators can be trained on formal alarm response strategies and should be given ample time to familiarize themselves with new displays as part of an effective deployment strategy.

Real-time application of multivariate statistical methods for early event detection in an industrial slurry stripper

Multivariate data analysis (MDA) is a well-established technique for abnormal situation management and early event detection (EED). This paper presents the development and on-line deployment of a Principle Component Analysis (PCA) model based EED system for an industrial-scale slurry stripper processing a solid state particle suspension. The developed solution was designed to detect plugging or blockage of the stripping column trays earlier than it is possible using traditional monitoring techniques and to avoid process disruption and production losses. The paper describes the project steps from data selection and preparation to the online implementation and utilization by operators and plant personnel. It was developed within a close collaboration between university and industry.

Abnormal-Situation Management in Offshore Operations

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 166584, ’Abnormal-Situation Management and Its Relevance to Process Safety for Offshore Operations,’ by Dal Vernon Reising and Peter Bullemer, Human Centered Solutions, and Bruce Colgate (retired), prepared for the 2013 SPE Offshore Europe Oil and Gas Conference and Exhibition, Aberdeen, 3-6 September. The paper has not been peer reviewed. The Abnormal Situation Management (ASM) Consortium performed a root-cause analysis on 32 incident reports gathered from public documents and member companies. The analysis identified common failure categories and manifestations in these incidents. Consequently, the consortium completed three case studies on potential deployment solutions: one supporting better shift-handover communication, a second supporting better alarm-flood situations, and a third supporting better situation awareness by use of overview displays using qualitative gauges. Introduction The ASM Consortium has been working for almost 20 years with an emphasis on human-factor engineering to address process-safety and operational challenges for downstream hydrocarbon-processing and petrochemicals companies. These consortium members have jointly invested in research and development to create knowledge, tools, and products designed to prevent, detect, and mitigate abnormal situations that affect process safety in the control/operations environment. The consortium has placed a continual emphasis on the humans that use automation and technology to operate the production processes, focusing extensively on human factors, human reliability, and process safety. Recently, the consortium has expanded its focus to upstream enterprises, with many of the operating company members having extensive oil and gas businesses. What Are Abnormal Situations? The ASM Consortium has defined abnormal situations as “undesired plant disturbances or incidents with which the control system is not able to cope, requiring a human to intervene to supplement the actions of the control system.” An abnormal situation could be a simple upset condition quickly recognized and rectified by operator action, or an abnormal situation could escalate to a critical process-safety incident, where a safety system must be engaged for an emergency shutdown and evacuation is required. The objective of ASM is to bring the process back to normal before safety-shutdown-control systems or other safety-protection systems are engaged. This definition is specifically used to distinguish between normal, abnormal, and emergency situations from the perspective of console operations. Often, operator error is blamed for causing incidents, and companies rush to increase training as a panacea. The work of the consortium has shown that incidents are caused by a multitude of factors, and solutions to address incidents need to consider the human operator’s role, the technology involved, and the system design, as well as the environment.