Fuzzy System Dynamics Risk Analysis (FuSDRA) of Autonomous Underwater Vehicle Operations in the Antarctic

Loss of research and operational equipment in Antarctica: Balancing scientific advances with environmental impact

Antarctica has been subject to widespread, long-term and on-going human activity since the establishment of permanent research stations became common in the 1950s. Equipment may become intentionally or inadvertently lost in Antarctic marine and terrestrial environments as a result of scientific research and associated support activities, but this has been poorly quantified to date. Here we report the quantity and nature of equipment lost by the UK's national operator in Antarctica, the British Antarctic Survey (BAS). Over the 15-year study period (2005-2019), 125 incidents of loss were reported, with c. 23 tonnes of equipment lost of which 18% by mass was considered hazardous. The geographical distribution of lost equipment was widespread across the BAS operational footprint. However, impacts are considered low compared to those associated with research station infrastructure establishment and operation. To reduce environmental impact overall, we recommend that, where possible, better use is made of existing research station capacity to facilitate field research, thereby reducing the need for construction of new infrastructure and the generation of associated impacts. Furthermore, to facilitate reporting on the state of the Antarctic environment, we recommend that national Antarctic programmes reinvigorate efforts to comply with Antarctic Treaty System requirements to actively record the locations of past activities and make available details of lost equipment. In a wider context, analogous reporting is also encouraged in other pristine areas subject to new research activities, including in other remote Earth environments and on extra-terrestrial bodies.

An integrated environmental risk assessment framework for coal-fired power plants: A fuzzy logic approach

This study quantifies the environmental risk of a coal-fired thermal power plant during operation by using environmental monitoring data, site surveys, and documented evidence. The following criteria are assessed: emissions (CO, SO₂ , NO_x , PM₁₀ ), impact on aquatic ecosystem (fish protection at cooling water intake and cooling water discharge temperature), and waste management (fly ash and bottom ash). Fuzzy sets were defined for each criterion, taking environmental regulatory context as an expert judgment. A survey was conducted with multiple stakeholders to determine the relative importance of risk factors. The survey results showed that the most concerned risks are SO₂ and NO_x emissions. The proposed method estimates the risk of each environmental criterion separately and then accumulates them into an environmental risk index (ERI). Accordingly, we assessed the Catalagzi coal-fired power plant, which has been in operation on the Black Sea coast in northwestern Turkey. For this case study, the ERI resulted in a value of 0.78 (on a scale of 0-1), showing high environmental risk to the facility. Moreover, the applicability of the proposed framework was tested in several existing coal-fired power plants using simultaneous measurements. All studied coal-fired power plants in Turkey have unacceptable pollutants (PM₁₀ , SO₂ , and NO_x ) concentration levels indicating high health risk potential. The application of the integrated environmental risk assessment framework showed that new environmental regulations are needed in Turkey to specify more strict emission limits and to monitor CO₂ , fine particulate matter emissions, cooling water discharge, and fish protection at cooling water intake.

Investigating the causal relationships among failure modes, effects and causes: a system dynamics approach

Purpose

This paper aims to develop a system dynamics (SD) model to identify causal relationships among the elements of failure modes and effects analysis (FMEA), i.e. failure modes, effects and causes.

Design/methodology/approach

A causal loop diagram (CLD) has been developed based on the results obtained from interdependencies and correlations analysis among the FMEA elements through applying the integrated approach of FMEA-quality function deployment (QFD) developed by Shaker et al. (2019). The proposed model was examined in a steel manufacturing company to identify and model the causes and effects relationships among failure modes, effects and causes of a roller-transmission system.

Findings

Findings indicated interactions among the most significant failure modes, effects and causes. Moreover, corrective actions defined to eliminate or relieve critical failure causes. Consequently, production costs decreased, and the production rate increased due to eliminated/decreased failure modes.

Practical implications

The application of CLD illustrates causal relationships among FMEA elements in a more effective way and results in a more precise recognition of the root causes of the potential failure modes and their easy elimination/decrease. Therefore, applying the proposed approach leads to a better analysis of the interactions among FMEA elements, decreased system's failure rate and increased system availability.

Originality/value

The literature review indicated a few studies on the application of SD methodology in the maintenance area, and no study was performed on the causal interactions among FMEA elements through an FMEA-QFD based SD approach. Although the interactions of these elements are significant and helpful in risks ranking, researchers fail to investigate them sufficiently.

An integrated approach for modeling uncertainty in human health risk assessment

A human health risk assessment (HHRA) will not remain simple and straightforward when it involves multiple uncertain input variables. Uncertainties in HHRA result from the unavailability and subjectivity of input variables. Though several studies have performed HHRA, the quantification of uncertainty in HHRA under a situation of data scarcity and the simultaneous application of random and non-random input variables have rarely been reported. The present study proposes an integrated hybrid health risk modeling framework involving the concurrent treatment of random and non-random input variables and estimating the uncertainties linked to the input variables in HHRA. The proposed framework presents the flexibility to classify the input variables into fuzzy and probabilistic categories, based on their data availability and provenience nature. The framework is demonstrated over the Turbhe sanitary landfill in Navi Mumbai, India, where the fate and transport of heavy metals in leachate are investigated through LandSim modeling. The present study considers the LandSim-simulated heavy metal concentration and body weight as a random variable and water intake, exposure duration, frequency, bioavailability, and average time as fuzzy variables. Further, the uncertainties in the non-carcinogenic human health risk have been quantified using Monte Carlo simulations, followed by a comprehensive multivariate sensitivity analysis of the proposed framework. High health risk at Turbhe is estimated for the male and female population. This study presents the first effort to quantify the non-carcinogenic human health risks from leachate-contaminated groundwater considering the health risk input variables as non-deterministic. The proposed framework is generic and applicable to any landfill site and will remain unaltered when integrated health risk assessment and uncertainty assessment are performed for the landfill.

A Hybrid Fuzzy System Dynamics Approach for Risk Analysis of AUV Operations

The maturing of autonomous technology has fostered a rapid expansion in the use of Autonomous Underwater Vehicles (AUVs). To prevent the loss of AUVs during deployments, existing risk analysis approaches tend to focus on technicalities, historical data and experts’ opinion for probability quantification. However, data may not always be available and the complex interrelationships between risk factors are often neglected due to uncertainties. To overcome these shortfalls, a hybrid fuzzy system dynamics risk analysis (FuSDRA) is proposed. The approach utilises the strengths while overcoming limitations of both system dynamics and fuzzy set theory. Presented as a three-step iterative framework, the approach was applied on a case study to examine the impact of crew operating experience on the risk of AUV loss. Results showed not only that initial experience of the team affects the risk of loss, but any loss of experience in earlier stages of the AUV program have a lesser impact as compared to later stages. A series of risk control policies were recommended based on the results. The case study demonstrated how the FuSDRA approach can be applied to inform human resource and risk management strategies, or broader application within the AUV domain and other complex technological systems.