Traditional measures used in the composting industry for ammonia and odor emissions control are those involving collection and treatment such as thermal oxidation, adsorption, wet scrubbing and biofiltration. However, these methods do not address the source of the odor generation problem. The primary objective of this thesis research was to develop preventive means to minimize ammonia and odor emissions, and maximize nitrogen conservation to increase the agronomic value of compost. Laboratory-scale experiments were performed to examine the effectiveness of various technologies to minimize these emissions during the active phase of composting. These techniques included precipitating ammonium into struvite in composting matrix before it release to outside environment; the use of chemical and biological additives in the form of yeast, zeolite and alum; and the manipulation of key operational parameters during the composting process. The fact that struvite crystals were formed in manure composting media, as verified by both XRD and SEM-EDS analyses, represents novel findings from this study. This technique was able to reduce ammonia emission by 40-84%, while nitrogen content in the finished compost was increased by 37-105%. The application of yeast and zeolite with dosages of 5-10% enhanced the thermal performance of composting and the degree of degradation, and ammonia emission was reduced by up to 50%. Alum was found to be the most effective additive for both ammonia and odor emission control; ammonia emission decreased by 45-90% depending on the dosage, and odor emission assessed via an dynamic dilution olfactometer was reduced by 44% with dosages above 2.5%. This study reaffirmed that aeration is the most influential factor to odor emission. An optimal airflow rate for odor control would be 0.6 L/min.kg dry matter with an intermittent aeration system. Quantitative relationships between odor emission and key operational parameters were determined, which would enable “best management practices” to be devised and implemented for composting. An empirical odor predictive model was developed to provide a simple and direct means for simulation of composting odor emissions. The effects of operating conditions were incorporated into the model with multiplicative algorithm and linearization approximation approach. The model was validated with experimental observations.

Phytoremediation is a relatively new remediation technology that may be useful in removing organic and inorganic pollutants from soils. Much research has focused on this type of remediation in the past few years due to its potential as an efficient and cost effective technology.<p>The purpose of this project was to extensively monitor phytoremediation of diesel-contaminated field soils in the laboratory under simulated field conditions. The main objectives were: to examine petroleum hydrocarbon (PHC) transfer and degradation processes involved in phytoremediation of contaminated field soils; to compare phytoremediation of contaminated field soils with intrinsic bioremediation; and, to develop a rationally-based model that could be used as a starting point for a quantitative prediction of the rate of PHC removal.<p>To realize these objectives a series of laboratory scale experiments were designed and carried out. The experiments reproduced pole planting of hybrid poplars into diesel contaminated field soils from a former bulk fuel station. The experiments were conducted in a closed and controlled environment over a 215-230 day period with numerous aspects of the system being monitored including volatilization of PHC from the tree and soil, and microbial activity of the soil.<p>Monitoring data indicated that microbial degradation of the contaminant was by far the most influential monitored degradation pathway, accounting for 96.3 to 98.7% of the mass removed for soils containing poplars. The monitoring data also indicated a significant difference in the mass of contaminant removed from the soil for soils containing poplars compared to those without. The total estimated mass of contaminant removed varied between 8.3 and 27.7% of the initial mass for soils containing poplars and between 6.0 and 6.1% of the initial mass for soils without poplars. Lastly, using the monitoring data and the below ground biomass of the poplars from each of the experimental test cells, a rationally-based model was developed to be used as a starting point for quantitative prediction of the rate of PHC removal.

Non-indigenous invasive species are a major threat to native species diversity and ecosystem function and have been called the single worst threat of natural disaster of this century. Eragrostis lehmanniana Nees (Lehmann lovegrass), a tufted perennial bunchgrass native to southern Africa, is one such problematic species in Arizona, USA. This dissertation research is a mix of predictive modeling and field experiments designed to inform management decisions based on greater understanding of this nonnative species, with emphasis on the potential for spread and the impacts of removal.The modeling studies in this dissertation aimed to predict the potential distribution of E. lehmanniana in the southwestern United States under current and potential future climate conditions. The first portion of study addressed a common assumption in predictive modeling of nonnative species: data from the species' native range are necessary to accurately predict the potential distribution in the invaded range. The second portion of this study predicted the distribution of E. lehmanniana under 28 different climate change scenarios. Results showed the distribution of E. lehmanniana progressively shrinking in the southeastern and northwestern portions of the state and increasing in the northeastern portion of the state with increasing temperatures and precipitation. Key shifts occurred under scenarios with increases in summer and winter precipitation of 30% or more, and increases in summer maximum and winter minimum temperatures of at least 2oC.The field experiment served as a pre-eradication assessment for E. lehmanniana and indicates how semi-desert grassland communities in southeastern Arizona may respond to the removal of this species. This study suggested that plant community response to removal of an introduced species is mediated by precipitation variability (timing and amount), local site history, and edaphic conditions. The response observed on a site previously farmed for decades was to subsequently become dominated by other nonnative annual species. However, the two other sites with histories of livestock grazing responded more predictably to the removal, with an increase in annual ruderal species (2 to 10 times the amount of annual cover recorded on control plots).

Traditional measures used in the composting industry for ammonia and odor emissions control are those involving collection and treatment such as thermal oxidation, adsorption, wet scrubbing and biofiltration. However, these methods do not address the source of the odor generation problem. The primary objective of this thesis research was to develop preventive means to minimize ammonia and odor emissions, and maximize nitrogen conservation to increase the agronomic value of compost. Laboratory-scale experiments were performed to examine the effectiveness of various technologies to minimize these emissions during the active phase of composting. These techniques included precipitating ammonium into struvite in composting matrix before it release to outside environment; the use of chemical and biological additives in the form of yeast, zeolite and alum; and the manipulation of key operational parameters during the composting process. The fact that struvite crystals were formed in manure composting media, as verified by both XRD and SEM-EDS analyses, represents novel findings from this study. This technique was able to reduce ammonia emission by 40-84%, while nitrogen content in the finished compost was increased by 37-105%. The application of yeast and zeolite with dosages of 5-10% enhanced the thermal performance of composting and the degree of degradation, and ammonia emission was reduced by up to 50%. Alum was found to be the most effective additive for both ammonia and odor emission control; ammonia emission decreased by 45-90% depending on the dosage, and odor emission assessed via an dynamic dilution olfactometer was reduced by 44% with dosages above 2.5%. This study reaffirmed that aeration is the most influential factor to odor emission. An optimal airflow rate for odor control would be 0.6 L/min.kg dry matter with an intermittent aeration system. Quantitative relationships between odor emission and key operational parameters were determined, which would enable “best management practices” to be devised and implemented for composting. An empirical odor predictive model was developed to provide a simple and direct means for simulation of composting odor emissions. The effects of operating conditions were incorporated into the model with multiplicative algorithm and linearization approximation approach. The model was validated with experimental observations.

Traditional measures used in the composting industry for ammonia and odor emissions control are those involving collection and treatment such as thermal oxidation, adsorption, wet scrubbing and biofiltration. However, these methods do not address the source of the odor generation problem. The primary objective of this thesis research was to develop preventive means to minimize ammonia and odor emissions, and maximize nitrogen conservation to increase the agronomic value of compost. Laboratory-scale experiments were performed to examine the effectiveness of various technologies to minimize these emissions during the active phase of composting. These techniques included precipitating ammonium into struvite in composting matrix before it release to outside environment; the use of chemical and biological additives in the form of yeast, zeolite and alum; and the manipulation of key operational parameters during the composting process. The fact that struvite crystals were formed in manure composting media, as verified by both XRD and SEM-EDS analyses, represents novel findings from this study. This technique was able to reduce ammonia emission by 40-84%, while nitrogen content in the finished compost was increased by 37-105%. The application of yeast and zeolite with dosages of 5-10% enhanced the thermal performance of composting and the degree of degradation, and ammonia emission was reduced by up to 50%. Alum was found to be the most effective additive for both ammonia and odor emission control; ammonia emission decreased by 45-90% depending on the dosage, and odor emission assessed via an dynamic dilution olfactometer was reduced by 44% with dosages above 2.5%. This study reaffirmed that aeration is the most influential factor to odor emission. An optimal airflow rate for odor control would be 0.6 L/min.kg dry matter with an intermittent aeration system. Quantitative relationships between odor emission and key operational parameters were determined, which would enable “best management practices” to be devised and implemented for composting. An empirical odor predictive model was developed to provide a simple and direct means for simulation of composting odor emissions. The effects of operating conditions were incorporated into the model with multiplicative algorithm and linearization approximation approach. The model was validated with experimental observations. / Applied Science, Faculty of / Chemical and Biological Engineering, Department of / Graduate

No description available.

Introduction: Trauma is the main cause of death in working-age adults in the UK. Multiple organ failure (MOF) is associated with a high proportion of late trauma deaths, and MOF survivors have poor long-term outcomes. Early prediction of patients at risk of MOF would assist treatment decisions and allow targeted interventions. Methods: A cohort of major trauma patients requiring intensive care unit (ICU) treatment at the John Radcliffe Hospital was identified. Data were obtained from the two national databases of the Trauma Audit Research Network and the Intensive Care National Audit and Research Centre, and from a local ICU database with hourly data recording. Literature review and questionnaire analysis of trauma clinicians identified candidate predictors of MOF, grouped into patient, injury, physiological, laboratory and management variables. MOF scoring systems were reviewed to determine the most appropriate for use in trauma patients. Prediction models of post-trauma MOF were developed using logistic regression at a range of times from 0 to 48 hours after injury. Models were internally validated using bootstrapping. Results: 517 adult trauma patients were identified from 2003-2011. Overall mortality was 14.9%, with 491 patients surviving more than 48 hours, and therefore being at risk of MOF development. For these 491 patients, MOF incidence depended on the definition, and ranged from 23% (Denver score) to 58% (SOFA score). MOF was associated with mortality, time to ICU admission, and length of ICU and hospital stay. MOF could be predicted with an accuracy of up to 81.3% at 2 hours post-injury, and 84.2% at 12 hours post-injury using small numbers of clinical variables. Age, head injury, abdominal injury, maximum heart rate and the need for vasopressors were strong predictors of all definitions of MOF. Conclusions: Post-trauma MOF can be predicted early after injury using combinations of clinical variables. Further validation of the identified variables on external populations would allow development of a clinical score to assist clinicians in trauma management.

Three-Dimensional (3D) integration of circuits based on die and wafer stacking using through-silicon-via is a critical technology in enabling "more-than-Moore", i.e. functional integration of devices beyond pure scaling ("more Moore"). In particular, the scaling from multi-core to many-core architecture is an excellent candidate for such integration. 3D systems design follows is a challenging and a complex design process involving integration of heterogeneous technologies. It is also expensive to prototype because the 3D industrial ecosystem is not yet complete and ready for low-cost mass production. Networks-on-Chip (NoCs) efficiently facilitates the communication of massively integrated cores on 3D many-core architecture. In this thesis scalability and performance issues of NoCs are explored in terms of architecture, organization and functionality of many-core systems. First, we evaluate on-chip network performance in massively integrated many-core architecture when network size grows. We propose link and channel models to analyze the network traffic and hence the performance. We develop a NoC simulation framework to evaluate the performance of a deflection routing network as the architecture scales up to 1000 cores. We propose and perform comparative analysis of 3D processor-memory model configurations in scalable many-core architectures. Second, we investigate how the deflection routing NoCs can be designed to maximize the benefit of the fast TSVs through clock pumping techniques. We propose multi-rate models for inter-layer communication. We quantify the performance benefit through cycle-accurate simulations for various configurations of 3D architectures. Finally, the complexity of massively integrated many-core architecture by itself brings a multitude of design challenges such as high-cost of prototyping, increasing complexity of the technology, irregularity of the communication network, and lack of reliable simulation models. We formulate a zero-load average distance model that accurately predicts the performance of deflection routing networks in the absence of data flow by capturing the average distance of a packet with spatial and temporal probability distributions of traffic. The thesis research goals are to explore the design space of vertical integration for many-core applications, and to provide solutions to 3D technology challenges through architectural innovations. We believe the research findings presented in the thesis work contribute in addressing few of the many challenges to the field of combined research in many-core architectural design and 3D integration technology. / <p>QC 20151221</p>

The recovery and recycling of the spent chemicals from the kraft pulping process are economically and environmentally essential in an integrated kraft pulp and paper mill. The recovery process can be optimised by firing high-solids black liquor in the recovery boiler. Unfortunately, due to a corresponding increase in the liquor viscosity, in many mills, black liquor is fired at reduced solids concentration to avoid possible rheological problems. Online measurement, monitoring and control of the liquor viscosity are deemed essential for the recovery boiler optimization. However, in most mills, including those in New Zealand, black liquor viscosity is not routinely measured. Four batches of black liquors having solids concentrations ranging between 47 % and 70 % and different residual alkali (RA) contents were obtained from Carter Holt Harvey Pulp and Paper (CHHP&P), Kinleith mill, New Zealand. Weak black liquor samples were obtained by diluting the concentrated samples with deionised water. The viscosities of the samples at solids concentrations ranging from 0 to 70 % were measured using open-cup rotational viscometers at temperatures ranging from 0 to 115 oC and shear rates between 10 and 2000 s-1. The effect of post-pulping process, liquor heat treatment (LHT) on the liquors’ viscosities was investigated in an autoclave at a temperature >=180 oC for at least 15 mins. The samples exhibit both Newtonian and non-Newtonian behaviours depending on temperature and solids concentration; the onsets of these behaviours are liquor-dependent. In conformity with the literature data, at high solids concentrations (> 50 %) and low temperatures, they exhibit shear-thinning behaviour with or without thixotropy but the shear-thinning/thixotropic characteristics disappear at high temperatures (>= 80 oC). Generally, when the apparent viscosities of the liquors are <= ~1000 cP, the liquors show a Newtonian or a near-Newtonian behaviour. These findings demonstrate that New Zealand black liquors can be safely treated as Newtonian fluids under industrial conditions. Further observations show that at low solids concentrations (< 50 %), viscosity is fairly independent of the RA content; however at solids concentrations > 50 %, viscosity decreases with increasing RA content of the liquor. This shows that the RA content of black liquor can be manipulated to control the viscosity of high-solids black liquors. The LHT process had negligible effect on the low-solids liquor viscosity but led to a significant and permanent reduction of the high-solids liquor viscosity by a factor of at least 6. Therefore, the incorporation of a LHT process into an existing kraft recovery process can help to obtain the benefits of high-solids liquor firing without a concern for the attending rheological problems. A variety of the existing and proposed viscosity models using the traditional regression modelling tools and an artificial neural network (ANN) paradigm were obtained under different constraints. Hitherto, the existing models rely on the traditional regression tools and they were mostly applicable to limited ranges of process conditions. On the one hand, composition-dependent models were obtained as a direct function of solids concentration and temperature, or solids concentration, temperature and shear rate; the relationships between these variables and the liquor viscosity are straight forward. The ANN-based models developed in this work were found to be superior to the traditional models in terms of accuracy, generalization capability and their applicability to a wide range of process conditions. If the parameters of the resulting ANN models can be successfully correlated with the liquor composition, the models would be suitable for online application. Unfortunately, black liquor viscosity depends on its composition in a complex manner; the direct correlation of its model parameters with the liquor composition is not yet a straight forward issue. On the other hand, for the first time in the Australasia, the limitations of the composition-dependent models were addressed using centrifugal pump performance parameters, which are easy to measure online. A variety of centrifugal pump-based models were developed based on the estimated data obtained via the Hydraulic Institute viscosity correction method. This is opposed to the traditional approaches, which depend largely on actual experimental data that could be difficult and expensive to obtain. The resulting age-independent centrifugal pump-based model was implemented online as a black liquor viscosity soft sensor at the number 5 recovery boiler at the CHHP&P, Kinleith mill, New Zealand where its performance was evaluated. The results confirm its ability to effectively account for variations in the liquor composition. Furthermore, it was able to give robust viscosity estimates in the presence of the changing pump’s operating point. Therefore, it is concluded that this study opens a new and an effective way for kraft black liquor viscosity sensor development.

No description available.