Systems that we deploy in subsea natural environments face many of the same challenges as space-based systems. This means that they are complex and often embedded as systems of systems. Norwegian firms excel in this industry and are increasingly applying systems engineering processes to ensure the success of their projects. However, these processes are defined as product development activities that do not accommodate the full product lifecycle. This paper investigates the project execution processes of a Norwegian subsea supplier and compares them to the systems engineering process baseline provided by ISO/IEC 15288:2008 for further analysis. Insights from this analysis are used to propose modifications to the current practices by closing the loop with a unified lifecycle model that bridges departmental divides. The research was performed as part of the master's thesis by the first author. Reflections on the usefulness of the standard as guidance are also offered.
Abstract. Subsea equipment must continuously evolve to meet the challenging conditions of the deep underwater environments. The optimization of coolers is a continuous process needed to remain competitive in this technology domain. This paper reports on the results of a project to study concept design optimization for the anti-surge cooler intended for a subsea compression station. The project followed the Aker Solutions project execution model, which begins with the creation of a concept of operations used to establish measures of performance, in addition to a valid requirements capture. The systems approach ensured that stakeholder needs were met, while identifying and refining parameters for the design. The refinement process, which resulted in a handful of design concepts, was evaluated through application of the AHP (Analytical Hierarchy Process) Decision Modeling Tool. Through this tool, the researcher arrived to a design concept recommendation, validated by stakeholders and sensitivity analysis.
Abstract. Requirement elicitation is a challenge in the Subsea Oil extraction industry due to the short project award and execution times that do not allow the complete compilation of a requirement database from the contract documents and appendixes. In this paper, a semantic approach to the automation of relevant inferences involved in the Requirement Analysis (RA) stages is proposed. An ontological model is here proposed to supporting the enrichment of the involved texts along a number of semantic assumptions, aiming to provide a rich explanatory description of the targeted phenomena These general types are used to enable the semantic annotation of texts and sentences within the documentation, i.e. conceptual information useful to characterize design choices (as in specification documents), requirements (design documents) or product descriptions (catalogues). A Machine learning approach is then discussed as a robust and effective solution to annotated texts according to the ontological model.
None of the recent Floating Production Storage and Offloading Unit (FPSO) projects was delivered on time or on budget. This paper, part of a PhD in Systems Engineering at the Norwegian University of Science and Technology (NTNU), endeavors to make visible some Root Causes by comparing the design process in two industries enabling the upstream oil energy value chain, Subsea oil Production Systems (SPS) and shipbuilding. The comparison uses Systems Engineering concepts and techniques to illustrate the current practices and their reliance on different domain specific conventions. Particular attention is given to possible commonalities and synergies in the design process to enable a more integrated approach to the industry needs. The purpose is to enable seamless requirement transfer between the user of the vessel and the shipbuilder. The comparison of the two design processes is leveraging on the 25 years subsea oil production experience of the writer and the guidelines established by professor Stein Ove Erikstad at the Department of Marine Technology at NTNU in his course "Introduction to Marine Systems Design Models and Methods".
VEAS is the largest WWTP in Norway, where inflow is collected through a combined sewer system, i.e., storm water runoff is combined in a common conduit with wastewater from homes, businesses, and industry and delivered to the plant. From a process perspective this already high degree of variability is further compounded by return flows from the plant itself. The VEAS plant is fully located in cavern and is operated 24/7. Cavern location requires low footprint and consequently high surface load. The VEAS process features a "single-shot" sedimentation and has a record-low water retention time of 3 hours. This highly efficient configuration is sensitive to variation in the inflow water parameters and internal plant recirculation flows, 25 measured parameters have been identified as impacting the effectiveness of the sedimentation process. Due to the high non-linearity of the parameters influence, even extensive use of classic non-linear statistical analysis has failed to clearly identify the main performance drivers of the process.In this paper we investigate the use of Kernel-based and Neural methods for the learning of the optimal control parameters in the context of industrial plants. The main objective is to define an automatic way to identify and tune the most relevant parameters of the plant (e.g., dosage of chemicals, sump level setting) to minimize the final water turbidity. The adopted machine learning framework enables the automatic analysis of the evolution of the plant behavior over time, i.e. exploits sensors readings stored for a long time period (one year), to develop a predictive model of the future behavior of the system. VEAS is the largest Norwegian Waste Water treatment Plant (WWTP) and its operation is essential for maintaining the Oslo fjord water at the requested quality level. Vestfjorden Avløpsselskap, VEAS, is fully owned by a consortium of municipalities (Aker, Baerum and Oslo).The plant and the administration services are located on the coast at Bjerkås in the municipality of Asker. The plant features a wide spectrum of equipment and advanced processes. Wastewater from more than 650'000 inhabitants in Oslo, Asker and Baerum is conveyed through the VEAS main tunnel Engineering
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