Current practice and challenges towards handling uncertainty for effective outcomes in maintenance

The combination of viable heuristic attributes with statistical measurements presents significant challenges in industrial maintenance for complex assets under through-life service contracts. Techniques to obtain and process heuristic attributes raise numerous uncertainties which often go undefined and unmitigated. A holistic view of these uncertainties may improve decision-making capabilities and reduce maintenance costs and turnaround time. It is therefore necessary to identify and rank factors that influence uncertainties originating from challenges in the above context. This, along with an identification of who contributes to such challenges and current practice to handle them, sets the focus for this study. The influence of 32 categorised factors on uncertainty is assessed through a questionnaire completed by nine experienced maintenance managers from a leading defence company. The pedigree approach is applied to score validity of respondents’ answers according to their experience and job role to normalise scores. Results are discussed in interviews with respondents along with current practice in and ways to improve uncertainty assessment. Scores are weighted through the Analytical Hierarchy Process (AHP) in order to identify the most influential factors on uncertainty in maintenance. The analysis revealed that these include: intellectual property rights (IPR), maintainer performance, quality of information, resistance to change, stakeholder communication and technology integration. These are verified with 40 practitioners from various industrial backgrounds. From the interviews, it is deemed that a holistic view of heuristic and statistical attributes ultimately allows for more accomplished decision-making but requires trade-offs between quality and cost over the asset’s life cycle

An uncertainty quantification and aggregation framework for system performance assessment in industrial maintenance

The exponential increase in technological complexity of modern engineering systems necessitates rigorous and accurate maintenance planning to determine optimum equipment availability and turnaround time whilst allowing for overruns and unforeseen costs. Quality and availability of quantitative data, as well as qualitative expert opinion and experience expose uncertainties that can result in under or over estimation of the above factors. Uncertainty quantification in complex engineering systems should consider inter-connected components and associated processes from a combination of quantitative and qualitative (compound) perspectives. This paper presents a framework to quantify and aggregate compound uncertainties and to be assessed against a predetermined acceptable level of uncertainty. This will provide maintenance planners with a confident, comprehensive view of parameters surrounding the above factors to improve decision making capabilities. The framework was validated by assessing individual and compound uncertainties in a bespoke heat exchanger test rig comprised of subsystem modules interact in a non-linear manner, as well as subjective opinions and actions of operators. The results demonstrate the framework’s ability to effectively quantify these factors with an indication of their impact on the system. Future work will include further validation with more complex case studies and development of methods to forecast the quantified uncertainty through the in-service phase of an asset’s life cycl

Uncertainty-based decision-making in fire safety: Analyzing the alternatives

Large accidents throughout the 20th century marked the development of safety fields in engineering, devoted to better identify hazards, understand risks and properly manage them. As these fields evolved rather quickly and moved from a compliance to a risk-based approach, a significant delay in this transition was experienced in fire safety engineering (FSE). Devastating fires well into the 21st century and the restrictive nature of prescriptive codes signaled the need to transition towards a performance-based one. A performance-based approach provides flexibility and capitalizes on learning from accidental events and engineering disciplines such as process safety and FSE. This work provides an overview of the main alternatives to account for uncertainty in safety studies within the context of FSE, including traditional probabilistic analyses and emerging approaches such as strength of knowledge. A simple example is used to illustrate the impact of the uncertainty analysis on the results of a simple fire safety assessment. A structured evaluation is performed on each alternative to assess its ease of implementation and communication. The outcome is a compendium of advantages and disadvantages of the alternatives that constitute a toolbox for fire safety engineers to configure and use within their fire risk assessments. Process safety engineers are expected to gain an understanding of the similar and important challenges of FSE, being it directly relevant for process risk management and fire risk management in administrative buildings

Environmental risk assessment of enhanced oil recovery solutions

PhD thesis in Risk management and societal safetyThe overall objective of the research presented in this thesis is to contribute new knowledge about the environmental risk related to shortlisted products and processes developed at the National Improved Oil Recovery (IOR) Centre of Norway and about how to assess such risk. According to the World Energy Outlook report presented by the International Energy Agency in 2021, oil and natural gas will continue to be important contributors to the energy mix over the next 20 years. Implementing enhanced oil recovery (EOR) solutions is important to maintain oil production from existing fields, as it is becoming increasingly difficult to discover new oil and gas reserves. An EOR screening study conducted across 53 reservoirs in 27 of the largest fields on the Norwegian Continental Shelf (NCS) found significant potential for additional oil recovery through EOR solutions. The (IOR) Centre of Norway has been developing new products and processes as part of EOR solutions to improve oil recovery on the NCS. Using these products and processes offshore poses an environmental risk to the marine environment and atmosphere, which needs to be assessed and managed. This thesis explores existing environmental risk assessment (ERA) approaches for offshore oil production and identifies knowledge gaps related to assessing the environmental risk of EOR solutions. The knowledge gaps are filled by using laboratory studies to generate new data, using this data in models to generate key insights, and by developing new methods for ERA of EOR solutions and proposing improvements to existing methods. The research conducted in this thesis has resulted in five scientific papers that are summarized below. Paper I presents a literature review on ERA guidelines relevant to offshore oil production. A review of the primary sources of environmental impacts and key environmental stressors resulting from offshore oil and gas production is also conducted. The main sources of environmental impacts from offshore oil production include operational discharges of produced water (PW), drilling waste to the marine environment, and air emissions from energy production using fossil fuels. The literature review indicates that current ERA practices may form a basis for ERA of EOR solutions; however, there are also knowledge gaps related to the ERA of new products and processes planned to be used as a part of EOR solutions. Based on the review, a generalized ERA framework for PW and drilling waste into the sea and for air emissions is proposed in Paper I. Several products and processes are developed at the IOR Centre to quantify and increase oil recovery as a part of EOR solutions. Using these new products and processes results in their back-production with PW, which is typically discharged into the marine environment. As a result, the main focus of this thesis is on the ERA of PW discharges caused by the implementation of EOR solutions. Quantifying residual oil saturation is important for the successful implementation of EOR solutions. The IOR Centre has proposed a group of seven chemicals (tracers) for potential use in quantifying residual oil saturation in oil reservoirs. Using these tracers in offshore oil fields results in their operational discharges (e.g., with PW) into the marine environment. Once released into the sea, marine organisms may become exposed to the tracers, thereby posing an environmental risk to the ecosystem. Paper II first reports on laboratory experiments conducted to measure the biodegradability and toxicity of seven tracer compounds. A hypothetical case of using tracer compounds on the NCS is then assumed. Discharge of PW containing tracers, along with other production chemicals from the Brage field (used as a proxy case), is simulated using the dynamic risk and effects assessment model (DREAM), which estimates the contribution to the environmental impact factor (EIF) values from each tracer. In addition, the seven tracer compounds are ranked from low to high in terms of their environmental impact. This ranking of the tracers can be used to shortlist the tracer(s) with minimum environmental impact for offshore applications. Polymer flooding is a process in which high molecular weight synthetic polymers are injected into an oil reservoir to increase oil recovery. Injected polymers are usually back-produced with the PW, which is typically discharged into the sea. These synthetic polymers have slow microbial degradation rates under aerobic conditions, unless the molecular weight is reduced to less than 3 kilodaltons. Photocatalytic depolymerization rates for several different synthetic EOR polymers have been measured as a part of another project at the IOR Centre. In Paper III, a novel method is proposed to estimate the residual lifetime of synthetic polymers in the marine environment. Residual lifetime is the amount of time the discharged synthetic polymer takes to reach a molecular weight, below which it becomes biodegradable in the sea. The proposed method uses the DREAM model to estimate the concentration distribution of polymers in the sea. Subsequently, the concentration distribution is linked with the depolymerization rate equations to estimate the residual lifetime of synthetic polymers in the sea. The applicability of this developed procedure is demonstrated by estimating the residual lifetime of synthetic polymers discharged from single and multiple oil fields on the NCS. Paper IV assesses the exposure and effects of discharging synthetic EOR polymers into the sea. Two main approaches are used: The first is based on estimating the EIF values of discharging PW-containing polymers using near-field simulations (where the discharge point is placed within a 50*50-kilometer grid). The estimated contribution to EIF values from synthetic polymers suggests negligible environmental impact when no assessment factor (AF) is used and low/moderate impact when an AF of 50 is used. The AF is a simple way to account for uncertainty in the assessment. The second approach, based on far-field simulations (where the discharge point is placed within a 1200*1800-kilometer grid), is primarily studied to assess polymer build-up in the sea, as synthetic EOR polymers show resistance to microbial degradability. In one of the farfield simulations, polymers are repeatedly released annually over a 10-year period from seven arbitrarily chosen oil fields on the NCS. The highest concentration values (based on the 75 percentiles) during the first and tenth years of discharge are used in a regression analysis against the amount of polymer discharged each year. The regression analysis indicates that polymers will not build up within the simulation area at the expected amounts of polymers discharged each year. Moreover, there is a considerable margin of safety between the highest concentration values calculated by the model and the concentration at which harmful effects in aquatic species are predicted. Paper V focuses on the use of species sensitivity distributions (SSDs) in ERA. An SSD is used to determine the threshold effect levels of stressors, below which unacceptable effects on a group of species are not expected. A literature review is performed to understand how risk is currently defined and how uncertainties are addressed when using SSDs in ERA. It is found that current ways of handling uncertainties while using SSDs are not based on unified guidance provided by the field of risk science. In Paper V, a risk-oriented framework is proposed that addresses uncertainties in a systematic manner while using SSDs. The proposed framework addresses uncertainties due to both lack of knowledge and variability. Furthermore, a scheme for assessing bias in theoretical and practical assumptions underlying SSDs is included in the framework. Lastly, a qualitative method is proposed to characterize the strength of knowledge underlying the SSDs

Compound uncertainty quantification and aggregation (CUQA) for reliability assessment in industrial maintenance

The mounting increase in the technological complexity of modern engineering systems requires compound uncertainty quantification, from a quantitative and qualitative perspective. This paper presents a Compound Uncertainty Quantification and Aggregation (CUQA) framework to determine compound outputs along with a determination of the greatest uncertainty contribution via global sensitivity analysis. This was validated in two case studies: a bespoke heat exchanger test rig and a simulated turbofan engine. The results demonstrated the effective measurement of compound uncertainty and the individual impact on system reliability. Further work will derive methods to predict uncertainty in-service and the incorporation of the framework with more complex case studies

Open-source modelling infrastructure: Building decarbonization capacity in Canada

Actions that transform our energy system are the cornerstone of decarbonizing our economy but have been hindered by the ineffective interface between researchers and decision-makers in Canada. This paper begins by arguing for a more holistic perspective on energy system decarbonization modelling and exploring how insights can aid evidence-based decision making. We then respond with the development of a modelling platform that includes three core pillars: (1) a toolbox of models that together represent the integrated energy system, (2) a dataset containing the inputs required to populate those models, and (3) a visualization suite to analyze and communicate their outputs. The Spine Toolbox is leveraged to process these three components in an efficient workflow. Taken together, the platform promotes the usability of model results by fostering consistency, transparency, and timeliness. Furthermore, the epistemic limitations of energy systems modelling and implications for platform and model design, and engaging extended peer communities, are discussed. Our hope is that this platform can be a foundational resource that facilitates collaboration between energy system and decarbonization researchers, modelling teams and decision-makers, ultimately enabling the effective application of evidence-based policy

Advanced uncertainty quantification with dynamic prediction techniques under limited data for industrial maintenance applications.

Zhao, Yifan - Associate supervisorEngineering systems are expected to function effectively whilst maintaining reliability in service. These systems consist of various equipment units, many of which are maintained on a corrective or time-based basis. Challenges to plan maintenance accounting for turnaround times, equipment availability and resulting costs manifest varying degrees of uncertainty stemming from multiple quantitative and qualitative (compound) sources throughout the in-service life. Under or over-estimating this uncertainty can lead to increased failure rates or, more often, unnecessary maintenance being carried out. As well as the quality availability of data, uncertainty is driven by the influence of expert experience or assumptions and environmental operating conditions. Accommodating for uncertainty requires the determination of key contributors, their influence on interconnected units and how this might change over time. This research aims to develop a modelling approach to quantify, aggregate and forecast uncertainty given by a combination of historic equipment data and heuristic estimates for in-service engineering systems. Research gaps and challenges are identified through a systematic literature review and supported by a series of surveys and interviews with industrial practitioners. These are addressed by the development of two frameworks: (1) quantify and aggregate compound uncertainty, and (2) predict uncertainty under limited data. The two frameworks are brought together to produce the Multistep Compound Dynamic Uncertainty Quantification (MCDUQ) app, developed in MATLAB. Results demonstrate effective measurement of compound uncertainties and their impact on system reliability, along with robust predictions under limited data with an immersive visualisation of dynamic uncertainty. The embedded frameworks are each validated through implementation in two case studies. The app is verified with industrial experts through a series of interviews and virtual demonstrations.PhD in Manufacturin