4 research outputs found
Process Performance Analysis in Large-Scale Systems Integrating Different Sources of Information
Process auditing using historical data can identify causes for poor performance and reveal opportunities to improve process operation. To date, the data used has been limited to process measurements; however other sources hold complementary information about the process behavior. This paper proposes a new approach to root-cause diagnosis, which also takes advantage of the information in utility, mechanical and electrical data, alarms and diagrams. Its benefit is demonstrated in an industrial case study, by tackling an important challenge in root-cause analysis: large-scale systems. This paper also defines specifications for a semi-automated tool to implement the proposed approach. © 2012 IFAC
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Understanding the Application and Benefits of Learning-based Methods in Nuclear Science and Engineering
Known as the fourth industrial revolution, digitization is an ongoing trend in all fields, in which various industries are integrating information technologies to support and improve their businesses. Nuclear technology industries have also increased their interest in data-driven methods by leveraging the potential of pattern recognition to identify anomalies and to take actions more rapidly, in areas such as health and monitoring, radiation detection, and optimization. By acknowledging the practicality and popularity of these methods, it is imperative to understand the benefits and barriers of implementing such methodologies to create better research plans and identify project risks and opportunities. This dissertation discusses different technologies and their integration and challenges within the nuclear industry. It is recognized that concepts of complexity and emergent behavior, as well as the importance of such properties as a distinctive aspect of nuclear and radiological engineering problems in which holistic approaches are crucial to innovation. Overall, the development and application of learning-based methods can be promising in the nuclear industry and many related tasks as long as expert knowledge is considered in the desired application to ensure a robust application of such methods. Cross-discipline studies and the creation of benchmarks are highly suggested for future practices
On extending process monitoring and diagnosis to the electrical and mechanical utilities: an advanced signal analysis approach
This thesis is concerned with extending process monitoring and diagnosis to electrical and mechanical utilities. The motivation is that the reliability, safety and energy efficiency of industrial processes increasingly depend on the condition of the electrical supply and the electrical and mechanical equipment in the process.
To enable the integration of electrical and mechanical measurements in the analysis of process disturbances, this thesis develops four new signal analysis methods for transient disturbances, and for measurements with different sampling rates. Transient disturbances are considered because the electrical utility is mostly affected by events of a transient nature. Different sampling rates are considered because process measurements are commonly sampled at intervals in the order of seconds, while electrical and mechanical measurements are commonly sampled with millisecond intervals.
Three of the methods detect transient disturbances. Each method progressively improves or extends the applicability of the previous method. Specifically, the first detection method does univariate analysis, the second method extends the analysis to a multivariate data set, and the third method extends the multivariate analysis to measurements with different sampling rates.
The fourth method developed removes the transient disturbances from the time series of oscillatory measurements. The motivation is that the analysis of oscillatory disturbances can be affected by transient disturbances.
The methods were developed and tested on experimental and industrial data sets obtained during industrial placements with ABB Corporate Research Center, Kraków, Poland and ABB Oil, Gas and Petrochemicals, Oslo, Norway.
The concluding chapters of the thesis discuss the merits and limitations of each method, and present three directions for future research. The ideas should contribute further to the extension of process monitoring and diagnosis to the electrical and mechanical utilities. The ideas are exemplified on the case studies and shown to be promising directions for future research.Open Acces
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Site-level Resource Efficiency Analysis
To achieve agreed targets for reducing global carbon emissions, industry must become more resource-efficient. To this end, two viable strategies exist: energy efficiency and material efficiency. Despite their inherent interdependence, industry continues to treat these two strategies as isolated pursuits, providing in the process only a partial insight into the potential of resource efficiency. To resolve this disconnect, this thesis attempts to develop and apply tools that help integrate industrial energy and material efficiency analyses. Three areas of research are explored.
The first is concerned with a fundamental component of industrial performance: efficiency benchmarks. No agreed-upon metric exists to measure the efficiency with which the sector trans- forms both energy and materials – that is, how resource-efficient they are. This thesis applies exergy – a well-established method to consolidate energy and materials into a single metric – to a case study of the global steel industry in 2010. Results show that this exergy-based metric provides a suitable proxy to capture the interactions between energy and materials. By comparing energy and material efficiency options on an equal footing, this metric encourages the recovery of material by-products – an intervention excluded from traditional energy efficiency metrics.
To realise resource efficiency opportunities, individual industry firms must be able to identify them at actionable time-frames and scopes. Doing this hinges on understanding resources flows through entire systems, the most detailed knowledge of which resides in control data. No academic study was found to exploit control data to construct an integrated picture of resources that is representative of real operations. In the second research area, control data is extracted to track the resource flows and efficiency of a basic oxygen steel-making plant from TataSteel. This second case study highlights the plant’s material efficiency options during operations. It does so by building close-to-real-time Sankey diagrams of resource flows (measured in units of exergy) for the entire plant and its constituent processes.
Without the support of effective policies the new exergy approach is unlikely to be widely adopted in industry. By collating evidence from interviews and policy documents, the third area explores why the European Union’s industrial energy and emissions policies do not incentivise material efficiency. Results suggest several contributing factors, including: the inadequacy of monitored indicators; an imposed policy lock-in; and the lack of a designated industry lobby and high-level political buy-in. Policy interventions are then proposed to help integrate material efficiency into energy and climate agendas. The European Union’s limited agency stresses the need for Member States and industry to drive the move to a low-carbon industry in the short-term.Emerson Electric sponsored my PhD. Emerson Electric is an American multinational corporation headquartered in Ferguson, Missouri, United States