Abnormal situation management: Challenges and opportunities in the big data era
Graphical abstract
Introduction
Process control has made a major contribution to the production efficiency, product quality, process safety and environmental protection in the chemical process industry over the past decades. Although it makes chemical processes highly automatic nowadays, the automation of abnormal situation management (ASM) has not been realized yet. According to Nimmo’s estimation, abnormal situations brought an economic loss of 20 billion US dollars per year to petroleum and chemistry industries in USA (Nimmo, 1995). In UK, abnormal situations cause 27 billion US dollars per year (Venkatasubramanian, 2005). That is one of the main reasons why human operators are still needed in chemical plants. Industry 4.0 is regarded as the fourth industrial revolution. It was defined by Weyer and Schmitt (2015) as a synonym for the transformation of today’s factories into smart factories which are intended to address and overcome the current challenges of shorter product lifecycles, highly customized products, enhanced work safety and stiff global competition. According to the 5C architecture of the Cyber-Physical Systems (CPS) in a smart factory, cognition is a critical level or component because it creates a thorough knowledge about the monitored system (Lee et al., 2015). Making timely, reliable and automatic decision supports to the operators about the abnormal situations in the chemical processes should be an indispensable cognition function in smart factories.
ASM does not only mean early warning of abnormal situations, but also timely diagnosis of their causal origins and providing decision supports to operators to take actions to bring the process back to normal (Venkatasubramanian et al., 2003a). Adhitya et al. (2014) designed a generic experimental scheme to quantify the benefits of an early warning system which predicts the time of occurrence of critical alarms before they are actually triggered (Adhitya et al., 2014). Their study results showed that early warning was effective in improving diagnosis lag and subjectively found to be helpful by the experiment participants, but it did not improve diagnosis accuracy. Therefore, fault diagnosis is one of the most important components in ASM. It could be found out that some of the severe chemical accidents in the past were due to failure of timely fault diagnosis.
On March 23, 2005, an explosion occurred at the isomerization unit of the BP Texas City refinery, killed 15 people, injured 180 and caused an economic loss of about 1.8 billion US dollars including the income losses during the reconstruction from March 2005 to March 2006 (Manca and Brambilla, 2012). The accident occurred during the startup of the raffinate splitter column where the incoming raffinate feed was separated into light and heavy components. Before the accident, operators were feeding the splitter tower with raffinate. As the tower level raised, the high alarm of tower level was triggered to warn the operators but was ignored by them because a high tower level was good for operations in the downstream according to their operating experience. Due to a fault in the level transmitter, the tower level kept raising but the level reading was declining on the contrary. According to the accident investigation, the tower level finally reached 156 feet but the level transmitter only showed a reading of 7.90 feet. Therefore the operators still maintained the feed even after the tower was filled up. The raffinate filled up the tower and then flew to the blow down drum, but the high level alarm of the drum did not work. Finally the raffinate flew out of the plant, formed vapour cloud, and was ignited by a nearby vehicle, causing a catastrophic explosion. If the equipment faults and operation mistakes had been diagnosed timely and correctly, the accident could have been avoided. Therefore Saleh et al. (2014) argued that safety-diagnosability principle is an essential ingredient for improving operators’ situation awareness (Saleh et al., 2014).
On November 13, 2005, an explosion occurred at the Jilin chemical plant, killing 8, injured 60 and polluted the Songhua River with about 100 tons of pollutants containing benzene and nitrobenzene (Zhang et al., 2010). The accident was caused by a wrong order of operations. Before the accident, the operators tried to stop the feed to the vacuum distillation tower but they did not stop the heating. When they resumed the feed, they started the heating steam before resuming the feed by mistake. When the cold nitrobenzene feed entered the heat exchanger, it was heated dramatically by the high temperature of the heat exchanger. The nitrobenzene then evaporated sharply and caused the flanges in the feeding pipe loosened. Afterwards the air was drawn in by the vacuum of the tower, and an explosive mixture was formed to lead to the explosion. No control interlocks were designed to prevent the misoperations or respond to the abnormal temperature rise which was as high as 60 °C before the explosion. There were two high temperature alarms before the explosion, which lasted for 80 min and 10 min respectively. But neither of them was recognized by the operators because alarm flooding was a common issue during the plant startup (Zhu et al., 2013).
The above two severe accidents showed that even though the process control systems have made modern chemical processes highly automatic accidents may still occur due to the mistakes of operators and malfunctions of equipment or instruments. Unfortunately, we have to acknowledge that there is no equipment or instrument that never fails and there is no human being that never makes any mistake. About 40 years ago, Prof. Himmelblau (1978) published the first book on chemical process fault detection and diagnosis (FDD) (Himmelblau, 1978). However, his dream to have a reliable early warning and diagnosis system that can widely be applied to different chemical processes has not been realized yet up to now. The situations in the development of the other automatic control systems are quite different. For example, the concept of programmable logic controller (PLC) was proposed by General Motors in 1968. One year later, Digital Equipment Corporation in the USA successfully released the first PLC. It took Honeywell only 5 years to launch the first distributed control system (DCS) TDC 2000 in 1975. In 1980, Cutler and Ramaker proposed the dynamic matrix control. In 1981, Garcia and Morari proposed the internal model control (IMC). Within 10 years, many advanced process control (APC) systems were successfully developed based on these concepts and widely applied in the refineries and petrochemical companies. However, process FDD is a 40 year dream that has not come true.
In fact, countless work has been done the area of FDD. In the 1980s, the IFAC Technical Committee on Fault Detection, Supervision and Safety for Technical Progresses (SAFEPROCESS) was founded. The IFAC Symposium on Fault Detection, Supervision and Safety for Technical Progresses is held every 3 years, aiming at strengthening contacts between academia and industry in the area of fault diagnosis, process supervision and safety monitoring. In 1994, the ASM Consortium was organized by Honeywell Company to address the chemical process industry concerns about the high cost caused by incidents and/or accidents, such as unplanned shutdowns, fires, explosions, emissions, etc. The ASM Consortium currently consists of companies including SASOL, BP, ExxonMobil, Honeywell, Shell, UOP and Human Centered Solutions, and universities including Mary Kay O’Connor Process Safety Center, University of California, Los Angeles, Pennsylvania State University, Nanyang Technology University and University of Alberta. The National Institute of Standards and Technology (NIST) funded the ASM Consortium with 8.5 million US dollars and the membership companies invested 8.5 million US dollars to support related research activities within the consortium. From 2008 to 2010, the ASM Consortium published three guidelines for (1) Effective Operator Display Design, (2) Effective Alarm Management Practices and (3) Effective Procedural Practices (Bullemer et al., 2011) respectively. Based on the first guidelines for effective operator display design, the graphical user interfaces of DCS systems have been changed to improve the operators’ ability to handle process abnormal situations that generate alarm floods (Laberg et al., 2014). Several alarm management systems such as Honeywell’s DynAmo, Yokogawa’s CAMS, PAS’s PlantState Suite, National University of Singapore’s IAMS (Srinivasan et al., 2004) and Tsinghua University’s iTAM were commercialized and the alarms flooding situations in chemical processes have been significantly mitigated by deploying these system. The suppression of nuisance alarms could help the operators take correct and quick action in response to serious alarms (Meel and Seider, 2008) and therefore prevent accidents.
Even though process safety and abnormal situation management have been enhanced worldwide during the past twenty years, there is no clearly visible overall decline in major accident events observed in either the USA or EU (Pitblado, 2011). While some companies may push safety culture and process safety management as the key drivers for enhancement of accident prevention, techniques such as real-time process fault detection and diagnosis will however be another viable technical key to avoiding major accidents.
Section snippets
Challenges and opportunities of chemical process fault diagnosis
The process FDD methods were categorized into three types: the qualitative model-based methods, the quantitative model-based methods, and the process history based methods (Venkatasubramanian et al., 2003a, Venkatasubramanian et al., 2003b, Venkatasubramanian et al., 2003c). Due to the increasing size and complexity of modern chemical processes and the prolife-rating quantity of available historical data, the process history based methods have shown great advantages in applicability over the
A new chemical process FDD framework
It is quite frustrating that process FDD technologies have not been as mature as other process control technologies such as model predictive control or advanced process control after 40 years of research and development. In our opinion, the main barrier that hinders the industrial application of FDD techniques is lack of historical fault samples. In Table 3 we listed the number of fault samples used to build process FDD models by some of the reported FDD technologies. It can be found a large
Conclusion
The concept of process Fault detection and diagnosis (FDD) was coined about 40 years ago. Its real industrial applications are still scarce. It is not because it is not a highly demanded technology nowadays when other process control technologies have widely been deployed in chemical plants. In contrast, it is still a dreamful and highly demanded technology that needs to be researched and developed in future. Many disasters such as the BP Texas city refinery explosion accident and the Jilin
Acknowledgements
The authors gratefully acknowledge support from the National High-Tech R&D Program of China (863 Program) (No. 2013AA040702) and the National Natural Science Foundation of China (No. 61433001).
References (86)
- et al.
Quantifying the effectiveness of an alarm management system through human factors studies
Comput. Chem. Eng.
(2014) - et al.
Common procedural execution failure modes during abnormal situations
J. Loss Prev. Process Ind.
(2011) - et al.
A plant-wide industrial process control problem
Comput. Chem. Eng.
(1993) Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee—Eastman process
Appl. Soft Comput.
(2011)- et al.
Optimal variable selection for effective statistical process monitoring
Comput. Chem. Eng.
(2014) - et al.
An introduction to alarm analysis and design
- et al.
A framework for optimal design of alarm systems
- et al.
Predictive monitoring for abnormal situation management
J. Process Control
(2001) HAZOP-past and future
Reliab. Eng. Syst. Saf.
(1997)- et al.
Graphical tools for routine assessment of industrial alarm systems
Comput. Chem. Eng.
(2012)