Computing resilience of process plants under Na-Tech events: Methodology and application to sesmic loading scenarios

https://doi.org/10.1016/j.ress.2019.106685Get rights and content

Highlights

  • A novel procedure for resilience calculation of process plants under Na-Tech events is proposed.

  • The methodology is strongly based on actual physical structure of the process and PERT/CPM technique, and it provides a direct estimation of capacity loss and the recovery function.

  • The model can be applied both in deterministic and probabilistic manner and is generalizable to any kind of Na-Tech hazard.

  • The applicability of the methodology has been shown in computating the seismic resilience of a nitric acid plant.

Abstract

Resilience is a performance measure representing both the capability of a system to survive a disruptive event and the ability of rapidly restoring the operational status recovering the initial capacity. However, literature is lacking about methods allowing to compute resilience of process plants from a technical point of view. In fact, in the process industry the scarce literature about resilience mainly focused on organizational issues. In order to contribute to fill this gap a methodology has been developed to estimate resilience in case of Na-Tech events for process plants. The methodology provides a direct estimation of capacity loss after the disruptive event, and the time trend of recovery as well as the related economic loss. The model can be applied both in deterministic and probabilistic manner and is generalizable to any kind of Na-Tech hazard. However, in this paper specific reference is made to seismic hazard. In order to show the capabilities of the methodology a case study is also described referring to a Nitric Acid plant. Results show the predictive capabilities of this approach and the usefulness as a decision making tool for facility planners and emergency managers in the process industry.

Introduction

Resilience is a concept which has attracted a great deal of interest from both public managers, industrial actors and the research community for over a decade. Although resilience is a wide ranging concept, which has been applied in many diverse contexts ranging from psychology, to metallurgy to organizational research, from the technical and managerial perspective it can be generally considered as a performance measure combining both the robustness of a system, intended as the ability to survive unexpected events which are able to disrupt its operations, and the capability of rapidly restoring system capacity after the disruptive event has occurred.

However, research aimed at assessing systems resilience in the last decade focused more on the civil infrastructures and building sector, as compared to the industrial one, and especially on networked systems, such as roads and transportation networks, supply chains, or service distribution infrastructures like gas and electricity distribution grids and telecommunication networks. Research on resilience of process plants and industrial facilities in general has been, instead, comparatively scarce, and mainly focused on organizational problems rather than aimed at providing a quantitative estimation model. Nevertheless, industrial facilities, and process plants in particular, that involve huge capital investments, may be a source of major accidents, thus representing a risk to nearby communities, contribute significantly to economic development of nations, and their failure can have a disruptive potential for global supply chains. Therefore, being able to assess plant resilience in case of disruptive events is inherently relevant, as local government, emergency responders, and business owners are interested in assessing the capability of the facility to withstand disruptive events and to rapidly return to normal operation. Moreover, knowledge of plant resilience allows to better plan protective and preventive measures, and is a building block to assess resilience of industrial supply chains.

Among the disruptive events which industrial plants face, natural hazards, like floods, hurricanes, tsunamis, earthquakes etc.., represent significant threats owing to their destructive potential. In industrial plants such natural hazard can give rise to Natural-Technological (Na-Tech) accidents which can have devastating consequences including equipment damage, release of dangerous substances, disruption of services and infrastructures, fatalities and environmental pollution with huge economic losses [[1], [2]–3]. Research on seismic hazards in the process industry is a relevant topic and has been reviewed recently [4,[5], [4]]. Several models for quantitative risk assessment have been also made available [[6], [7], [8], [9], [10], [11]–12]. Nevertheless, the above models focus on estimating equipment damage and losses consequent to the occurrence of an earthquake, but neglect the analysis of the capacity recovery process and plant resilience in general.

Resilience is a wide scope concept and performance measure, blending both the capability of a system to survive and the ability of rapidly recovering from high impact and low frequency disruptive events. This can be achieved by a mix of robust system design practices, damage prevention measures, organizational redesign as well as rapid recovery and survivability enhancing activities. Given its omnicomprehensive reach and applicability a single agreed definition of resilience has not yet been established. Therefore, several alternative definitions are discussed in the literature [[13], [14]–15].

Generic overviews of the resilience concept in business and engineering contexts are provided by Hollnagel et al. [16] and Sheffi [17], while a general qualitative modeling framework has been also suggested by Rigaud and Guarnieri [18] and by Lundberg and Johansson [19]. Given the multiplicity of possible resilience definitions a wide range of quantitative performance measures and specific resilience metrics have been also proposed in the literature [14,[20], [21], [22], [23]–24].

From the management point of view resilience has been extensively studied under the organizational perspective [25,26], as well as in the framework of enterprise management [[27], [28], [29], [30]–31], including the economic impact on the social community [32] and the single business [33]. Input-output models have been also used to investigate the economic impact of natural or man-made hazards in industrial complexes and business sectors. However, the fields which attracted the major attention of researchers are those of civil and infrastructures engineering, networked infrastructures and supply chains.

In the field of civil engineering and built and transportation infrastructures, attention has been also focused on modeling resilience related to natural hazards. In particular, Broccardo et al. [[34], [35], [36]–37] review resilience modeling of built infrastructures under natural disasters conditions, and especially earthquakes [38], [39], [40], [41], [42]. Resilience of community structures in networks has been modeled by Ramirez-Marquez et al. [43]. Economic loss following natural disaster in urban communities and at regional level is also investigated by Vargas and Ehlen [44,45], while Barabadi and Ayele [46] attempt to use statistical analysis of historical data to estimate infrastructure recovery rates and time and Barker and Baroud [47] apply proportional hazard models to infrastructure recovery.

Networked and critical infrastructures, such as electricity grids or other utilities distribution networks, as well as computer networks also attracted a great deal of academic research. In this realm, apart from vulnerability to natural hazards, a major concern are cascading effects and external attacks either in stand-alone or interdependent networks [[48], [49], [50], [51], [52], [53]–54]. This research also is aimed at resilience based network design [55] and identifying components importance in networks [56].

Networked systems include supply chains, which is another research field rising increasing interest, strictly related to networks and transportation systems resilience although with specific features [[57], [58], [59]–60].

Resilience research applied to industrial plants, instead, has been comparatively scarce even if such facilities are quite complex in itself, represent high capital investments and relevant cash flow streams for either the owner and other stakeholder, and a failure can have long duration effect which can propagate along the supply chains. In this sector, research has been mainly oriented towards the organizational and operational issues including human factors [[61], [62], [63]–64] and more recently to applications in safety analysis and risk assessment of process plants [[65], [66], [67], [68]–69]. Ryzdak and coworkers [70,71] also attempted to model resilience using the system dynamics paradigm. Nevertheless, resilience modeling approaches derived from the networked infrastructures sector and from the civil and seismic engineering domain, may not be directly transferable to the field of industrial facilities, where resilience depends on fragility of process equipment and process flow structure. Ganesan and Elamvazuthi [72] develop a method for preliminary design process plants in order to optimize reliability, resilience and cost but using simplified process modeling and neglecting natural hazards. Tan et al. [73] develop a simplified model for an industrial plant based on process flow, but mainly from an economic perspective, and neglect disruptions from natural hazards. Zio and Ferrario [74], Ferrario and Zio [75,76] investigate with detailed modeling safety-critical facilities, such as nuclear power plants subject to natural hazards. Nevertheless, in such kind of application product flow is not relevant, so that the approach cannot be directly applied to process plants as the emphasis is placed on the logical interactions of cascading effects on safety components and the affected processes. Mebarki et al. [77] deal with process plants but focus specifically on resilience of single process equipment under natural hazards thus neglecting the overall process flows. Finally, research is scarce too in the domain of manufacturing plants, although some general purpose modeling approaches have been suggested [70,71,78].

From the above discussion it appears that additional modeling effort, specifically targeted to process and manufacturing plants is still needed in order to obtain realistic resilience estimation of such facilities taking into account their internal structures, the process flows and, in case of natural hazards, the inherent fragility of equipment.

In order to contribute to a solution of this research problem, in this paper an approach and associated software tool for the computation of process plant resilience, based on the actual process flows and installed equipment, is developed building on the preliminary work by Caputo and Paolacci [79]. The method has a general formulation and can be applied to any kind of disruption causing physical damage to plant equipment. It will be described with specific reference to earthquakes, even if it can be extended to any kind of Na-Tech event. Moreover, it can be applied both in a deterministic and probabilistic manner as described in the following. The method has the following notable features: it directly computes initial capacity loss after the disruption occurs; it computes the time trend of capacity recover and total recovery time; it estimates the total economic loss imputable to business interruption and plant reconstruction. In doing so, this model, while filling the above cited gap in the literature, may prove valuable as a decision making support tool for facility planning and emergency management in process industries.

This paper is organized in the following manner. The proposed algorithm for resilience computation is described assuming both deterministic and probabilistic scenarios and a generic disrupting event, with specific extensions to seismic risk. A case study application is successively described in order to show model capabilities with reference to a Nitric Acid Plant. Finally, model discussion and perspectives for future research conclude the paper.

Section snippets

Model description

The model for resilience assessment is amenable to both a deterministic and probabilistic analysis. Deterministic resilience analysis implies that the user is interested in analyzing a specific damage scenario, meaning that the set of plant equipment damaged by the natural event, i.e. assigned to an out of service state, is predefined by the user. Any different damage scenario of interest can be separately analyzed. Probabilistic analysis implies that either the initial damage scenario is

Application to Na-Tech Events: earthquake loading scenario

In this section the proposed methodology is applied to a specific case study, the nitric acid plant described in Ray and Johnston [97], properly modified to include more complex conditions. After a brief description of the plant, the most frequent seismic damage scenarios are evaluated by using the methodology proposed by [8]. Finally, the resilience calculation and the business interruption evaluation for the analyzed example are described and commented, showing the full potentiality of method.

Conclusions

In this paper a novel methodology to estimate process plants resilience is proposed. The method, which is based on actual plant architecture, provides, for given damage scenarios, an estimate of the economic losses and business interruption costs, along with the time length of the capacity recovery phase. The proposed approach can incorporate either deterministic or probabilistic damage scenarios, representing a useful making decision tool for plant owners, facility planners and emergency

Acknowledgment

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721816.

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      Citation Excerpt :

      Several countries and regions adopted specific regulations to address Na-tech events, such as Seveso Directives III [15], California Accidental Release Prevention [16] and OECD Guiding Principles for Chemical Accident Prevention, Preparedness and Response [13]. At present, studies on Na-tech events mainly focus on historical accident statistical analysis [5–7], vulnerability assessment [8,17,18], risk analysis [2,19,20], accident prevention and mitigation [19,21], etc. Advances in lessons learned from past Na-tech events have led to the development of many useful approaches that specifically address hurricane-related Na-tech events, lightning-related Na-tech events, earthquake-related Na-tech events and floods-related Na-tech events [2,13,20].

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