Rapport (Institutionen för energi och teknik, SLU)

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https://hdl.handle.net/20.500.12703/6613

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    UV-based advanced oxidation process for nutrient stabilisation and organic micropollutant degradation in source-separated human urine
    (Department of Energy and Technology, Swedish University of Agricultural Sciences, 2023) Demissie, Natnael
    Urine dehydration is one of the technological approach to recover nutrients in concentrated form from source separated urine. When drying fresh urine, nitrogen loss occurs due to hydrolysis of urea into ammonia unless methods to inactivate urease enzyme are employed. In addition, concerns arise when using urine-derived fertiliser due to the potential presence of organic micropollutants (pharmaceuticals). This thesis evaluated ultraviolet (UV) treatment as an alternative chemical-free nutrient stabilisation (urease inactivation) and organic micropollutant (OMP) degradation technology. Urease inactivation and OMP degradation in water and in urine (synthetic urine, real urine from human subjects) were studied in a photoreactor equipped with a low-pressure mercury UV lamp emitting light predominantly at 185 and 254 nm. Exposure of real urine to 80 min of UV irradiation resulted in more than 90% degradation of 18 out of 75 OMPs and 1-90% degradation of the remaining OMPs. Enzymatic activity fell below the detection limit for real urine exposed to 71 min of UV irradiation. However, electrical energy demand for reducing enzymatic activity below the detection limit in real fresh urine was 52-fold higher than for inactivation in synthetic fresh urine (without urea), while electrical energy demand was more than 10-fold higher for 90% OMP degradation in real fresh urine than in water. The inactivation and OMP degradation observed were probably due to direct photolysis and photo-oxidation. Presence of organic substances in real urine was the likely reason for less efficient inactivation of urease and OMP degradation, as such substances can competitively absorb incoming UV light and scavenge the free radicals formed during UV treatment. Although 20% urea was lost after UV treatment, there was no decrease in total nitrogen. In summary, UV treatment can stabilise urea-N and degrade OMPs in fresh urine and has potential for integration into urine diversion sanitation systems.
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    Neurocognitive modelling of human decision making
    (Department of Energy and Technology, Swedish University of Agricultural Sciences, 2019) Nazir, Azadeh Hassannejad
    A central issue related to climate change and the path to a low carbon society is how we can change our attitudes and associated behavioral patterns. This type of decisions is concerned with how complex systems can be dealt with, conceptually, psychologically, as well as socially. In order to transform our society, we need to consider the relationship between brain, mind and behavior. One of the approaches to address this problem is to design computational models that can be used for simulations and scenario building. This thesis concerns the development and application of a neurocomputational model of the decision making process of an individual at experiential and social levels, considering both emotional and rational aspects. It is an attempt to bridge the gaps between micro (neuronal), meso (brain areas) and macro (cognition/behavior) levels with a focus on the mesoscale neurodynamics of cortical structures. The model is intended to link neural structures, functions, and includes effects of internal and environmental factors. The thesis is divided into two parts, corresponding to the two kinds of decision making: 1) experience-based and 2) social-based decision making. At an individual level, a final decision is the result of an integration of rational and emotional processes. The neural structures involved in cognition valuate the potential options regarding internal attitudes and rules, as well as external contexts. Decision values are based on neural properties of activity patterns associated with different actions. The option with the highest value is selected for in the decision making process. Human behavior is guided not only by subjective values and attitudes, but also by the perceived behavior of others. Learning from/about others through observation shapes our thoughts and behavioral patterns. The second part of the thesis deals with this social adaptive characteristic of an individual, where the dynamic changes of her behaviors are connected with trust. Traces of social influences on an individual’s decisions and social expectations (e.g. trust) have been observed in the rational and emotional brain structures and their functions. While the neurocomputational model is based on anatomical and physiological data of the modeled brain structures, no real world data have been available for model validation. Yet, simulation results mimic EEG and fMRI readouts, which could be compared with experimental/clinical data, when available. Future work intends to provide such data, but currently the modeling can only provide insights in the neurodynamic interactions between brain areas involved in decision making.
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    A New Approach in Profile Analysis with High-Dimensional Data Using Scores
    (Swedish University of Agricultural Sciences, Department of Energy and Technology, 2020) Cengiz, Cigdem
    In profile analysis, there exist three tests: test of parallelism, test of levels and test of flatness. In this thesis, these tests have been studied. Firstly, a classical setting, where the sample size is greater than the dimension of the parameter space, is considered.  The hypotheses have been established and likelihood ratio tests have been derived. The distributions of these test statistics have been given. In the latter stage, all tests have been derived in a high-dimensional setting, where the number of parameters exceeds the number of sample size. Such settings have become more common due to the advances in computer technologies in the last decades. In high-dimensional data analysis, several issues arise with the dimensionality and different techniques have been developed to deal with these issues.  We propose a dimension reduction method using scores that was first proposed by Läuter et al. (1996). To be able to find the specific distributions of the test statistics of profile analysis in this context, the properties of spherical distributions are utilized.
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    Time and spatial dependent climate impact of grass cultivation and grass-based biogas system
    (Sveriges lantbruksuniversitet, Institutionen för energi och teknik, 2020) Nilsson, Johan
    One strategy to limit global warming is to phase out fossil products and replace them with bio-based alternatives. This is often referred to as transitioning from a fossil economy to a bioeconomy. In this transition, it is important to know the environmental impact of bio-based products, since it can be greater than that of the fossil products they replace. Life Cycle Assessment (LCA) is a suitable methodology for studying the impact of bio-based products, since it encompasses the whole life cycle of the product. However, LCA rarely considers spatial and temporal variations in impacts. It also rarely includes soil processes such as soil carbon balance and only roughly estimates nitrous oxide (N2O) emissions from soil.In this thesis, LCA was combined with the agro-ecosystem model DNDC to include these soil processes and their variations over time and space. The combined method was used to assess climate impact and eutrophication in grass production at five sites in central and southern Sweden and the climate impact and energy balance in grass-based biogas production in Uppsala municipality, Sweden. Analysis of grass cultivation with two fertilisation rates (140 and 200 kg N ha-1) at different Swedish sites revealed that the higher rate gave a lower climate impact per Mg harvested biomass, but that site properties were more important than fertilisation intensity in determining the climate impact.Analysis of grass for biogas production, which was assumed to be cultivated on fallow land, was conducted for more than 1000 regional sites with different properties in Uppsala municipality and the whole life cycle was included (cradle to grave). The results showed large variations in impact between different sites, depending on weather conditions, soil properties, transport distances etc. The greenhouse gas fluxes from grass cultivation with the greatest climate impact were soil N2O emissions and emissions from fertiliser manufacture, which contributed to global warming, and changes in soil carbon balance, which generally had a climate mitigating effect. Overall, grass cultivation increased soil carbon stocks, but this effect was highly site- and time-dependent. The grass-based biogas production system reduced the climate impact significantly compared with the reference fallow-diesel-mineral fertiliser system.The method developed in this thesis, where LCA was combined with agro-ecosystem modelling, could be used to assess the environmental impact of agricultural systems in other regions. The results could then also be used to assist policymakers in optimising agricultural land use planning for food, feed and fuel production.
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    Assessment of statistical analysis of Swedish cultivar testing: a cross-validation study for model selection
    (Department of Energy and Technology, Swedish University of Agricultural Sciences, 2019) Buntaran, Harimurti
    The Swedish official cultivar testing conducts multienvironmental trials (MET) to makerecommendations of cultivars that are well adapted to farmers’ regional conditions. Inthe MET, a large number of cultivars are tested in several geographical regions. Thetested cultivars perform differently in varying soil types and climates, a phenomenonknown as genotype×environment interactions. The MET data structure is often large andhighly imbalanced, which causes computational problems when applying some statisticalmethods. Several issues, such as prediction of crop variety performance and efficientcomputation of measure of cultivar stability are urgent to be tackled by developingcomprehensive and robust statistical methods. This study aims to address these issuesand provide a gold standard for MET analysis in Swedish official cultivar testing. In this study, we investigated several linear mixed models by using cross-validation(CV). We proposed to use random cultivar effects, known as best linear unbiasedprediction (BLUP) method to replace the current fixed cultivar effects, known as bestlinear unbiased estimation (BLUE). In theory, BLUP provides more accurate rankingsand predictions than BLUE. The current-practice analysis strategy, i.e., two-stageunweighted strategy, was also compared to several strategies such as single-stagestrategy and two-stage weighted strategies that comprise some weighting methods. In theCV, mean squared error of differences (MSEP) was used to assess the performance ofestimation of cultivar effects by BLUP and BLUE to select a model that provides bestprediction accuracy. A new inter-zone stability measure was also proposed to tacklecomputational burden and provide additional useful information regarding cultivarstability across zones and years. The MSEP revealed that BLUP outperformed the current-practice method, BLUE,and so improved the accuracy of zone-based prediction. Also, the single-stage and twostage weighted strategies outperformed the current strategy. The proposed stabilitymeasure offered a less computational resource, and provided more flexible stabilitymeasure for practical purpose.