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  • Disputas: 2018-12-13 10:00 B135, Kalmar
    Ahlgren, Fredrik
    Linnéuniversitetet, Fakulteten för teknik (FTK), Sjöfartshögskolan (SJÖ).
    Reducing ships' fuel consumption and emissions by learning from data2018Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    In the context of reducing both greenhouse gases and hazardous emissions, the shipping sector faces a major challenge as it is currently responsible for 11% of the transport sector’s anthropogenic greenhouse gas emissions. Even as emissions reductions are needed, the demand for the transport sector rises exponentially every year. This thesis aims to investigate the potential to use ships’ existing internal energy systems more efficiently. The thesis focusses on making existing ships in real operating conditions more efficient based logged machinery data. This dissertation presents results that can make ship more energy efficient by utilising waste heat recovery and machine learning tools. A significant part of this thesis is based on data from a cruise ship in the Baltic Sea, and an extensive analysis of the ship’s internal energy system was made from over a year’s worth of data. The analysis included an exergy analysis, which also considers the usability of each energy flow. In three studies, the feasibility of using the waste heat from the engines was investigated, and the results indicate that significant measures can be undertaken with organic Rankine cycle devices. The organic Rankine cycle was simulated with data from the ship operations and optimised for off-design conditions, both regarding system design and organic fluid selection. The analysis demonstrates that there are considerable differences between the real operation of a ship and what it was initially designed for. In addition, a large two-stroke marine diesel was integrated into a simulation with an organic Rankine cycle, resulting in an energy efficiency improvement of 5%. This thesis also presents new methods of employing machine learning to predict energy consumption. Machine learning algorithms are readily available and free to use, and by using only a small subset of data points from the engines and existing fuel flow meters, the fuel consumption could be predicted with good accuracy. These results demonstrate a potential to improve operational efficiency without installing additional fuel meters. The thesis presents results concerning how data from ships can be used to further analyse and improve their efficiency, by using both add-on technologies for waste heat recovery and machine learning applications.

  • Disputas: 2018-12-14 13:15 Weber, Växjö
    Johansson, Annelie
    Linnéuniversitetet, Fakulteten för konst och humaniora (FKH), Institutionen för svenska språket (SV).
    Lärares bedömningsspråk: Språkhandlingar, bedömning och språklig utformning i grundskolans skriftliga omdömen2018Doktoravhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    This thesis studies elementary school teachers’ language use in written assessments of students, and problematises the relation between teachers’ experiences of written assessment and the institutional and social expectations of professional language use.

    The study is based on three types of material: questionnaires answered by 39 teachers, interviews with 3 school principals and approximately 2,200 assessments of students, written according to three different templates. The assessments are analysed using methods derived from stylistics and textanalysis. The analyses of the texts focuses on speech acts, the content of the assessment and linguistic choices. The teachers’ testimonials in the questionnaires and the principals’ perspectives provide contextual information necessary for the understanding of the various forms of language used in the assessments.

    The theories applied are critical discourse analysis (Fairclough, e.g., 1992), Roberts & Sarangi’s model of language usage in professional practices (1999,2003), Bernstein’s theory of discourses in pedagogical practices (1990, 1996) and Gerrevall’s (2008) theory of assessment practices.

    The results indicate that the teachers’ language use positions them on a scale from formal to informal, and reflect different conditions for writing assessments. The templates for assessments, the statements of teachers and principals illustrate a wide range of institutional, collegial and personal language use. Teachers use six macro speech acts in their assessments: they inform, evaluate, summarise, guide, express feelings and attitudes, and seek dialogue. The content of the assessments focuses on the students’ achievements, processes, personal qualities and behaviour, but also on psychosocial evaluation, which promotes self-esteem and enhances selfregulation ability. In the assessments, teachers often reproduce parts of previously used texts, switch between formal, institutional, and bureaucratic language and context-bound, informal, and dialogical language use. The results show four typical roles that a teacher can assume: the reporter, the processor, the educator, and the coach. Teachers embrace these roles, and switch between them when navigating between institutional and social expectations of form, function and focus of the assessments, which can partly be explained by the influence of New Public Management on teachers’ documentation practices.