title: Process integration between individual plants at a large dairy factory by the application of heat recovery loops and transient stream analysis

authors: Atkins, Martin John

subjects: Waste heat recovery

abstract: Indirect heat transfer between individual plants at a large industrial site, such as at a large dairy factory, can be accomplished using a Heat Recovery Loop (HRL) operating at relatively low temperatures (20–50 °C). Direct heat transfer between plants is problematic because of the production schedule and frequent process start-ups and shutdowns that occur due to the need for regular cleaning. The dynamic operation of an industrial HRL at a large dairy factory is presented and briefly discussed. A model HRL based on a set of variable industrial stream data is used to examine the effect of heat storage capacity and HRL temperature range on the amount of heat recovery. Both HRL temperature range and a small heat storage volume can significantly improve the amount of heat recovery. The interface level in the storage tank is also modelled for different volumes and the resulting system is very dynamic and therefore good control needs to be incorporated into the HRL.

text: Process integration between individual plants at a large dairy factory by the application of heat recovery loops and transient stream analysis Indirect heat transfer between individual plants at a large industrial site, such as at a large dairy factory, can be accomplished using a Heat Recovery Loop (HRL) operating at relatively low temperatures (20–50 °C). Direct heat transfer between plants is problematic because of the production schedule and frequent process start-ups and shutdowns that occur due to the need for regular cleaning. The dynamic operation of an industrial HRL at a large dairy factory is presented and briefly discussed. A model HRL based on a set of variable industrial stream data is used to examine the effect of heat storage capacity and HRL temperature range on the amount of heat recovery. Both HRL temperature range and a small heat storage volume can significantly improve the amount of heat recovery. The interface level in the storage tank is also modelled for different volumes and the resulting system is very dynamic and therefore good control needs to be incorporated into the HRL.

year: 2012

title: Integrating Cyc and Wikipedia: Folksonomy meets rigorously defined common-sense

authors: Medelyan, Olena

subjects: ontologies

abstract: Integration of ontologies begins with establishing mappings between their concept entries. We map categories from the largest manually-built ontology, Cyc, onto Wikipedia articles describing corresponding concepts. Our method draws both on Wikipedia’s rich but chaotic hyperlink structure and Cyc’s carefully defined taxonomic and common-sense knowledge. On 9,333 manual alignments by one person, we achieve an F-measure of 90%; on 100 alignments by six human subjects the average agreement of the method with the subject is close to their agreement with each other. We cover 62.8% of Cyc categories relating to common-sense knowledge and discuss what further information might be added to Cyc given this substantial new alignment.

text: Integrating Cyc and Wikipedia: Folksonomy meets rigorously defined common-sense Integration of ontologies begins with establishing mappings between their concept entries. We map categories from the largest manually-built ontology, Cyc, onto Wikipedia articles describing corresponding concepts. Our method draws both on Wikipedia’s rich but chaotic hyperlink structure and Cyc’s carefully defined taxonomic and common-sense knowledge. On 9,333 manual alignments by one person, we achieve an F-measure of 90%; on 100 alignments by six human subjects the average agreement of the method with the subject is close to their agreement with each other. We cover 62.8% of Cyc categories relating to common-sense knowledge and discuss what further information might be added to Cyc given this substantial new alignment.

year: 2008

title: Ecological Survey of Tauranga Harbour

authors: Ellis, Joanne I.

abstract: This report summarises the results of biological and physical data collected from a broad scale intertidal survey of Tauranga Harbour conducted between December 2011 and February 2012. The survey was designed to understand more fully the role of various anthropogenic stressors on the ecology of the harbour. The research was conducted as part of the Manaaki Taha Moana (MTM) programme. The wider research project aims to restore and enhance coastal ecosystems and their services of importance to iwi/hapū, by working with iwi to improve knowledge of these ecosystems and the degradation processes that affect them.
In this report we assess the health of macrofaunal benthic communities (bottom-dwelling animals) as well as trends in sediments, nutrients and contaminants. The results indicate that the sites identified as most impacted were generally located in the upper reaches of estuaries in some of the locations least exposed to wind, waves and currents. In addition, the biological community composition characterising sites with different sediment textures, nutrient and contaminant loadings were found to vary. Sediments within Tauranga Harbour were predominantly sandy with the percentage of mud within a similar range as measured for other New Zealand estuaries. The exceptions included Te Puna Estuary and Apata Estuary, which experience higher rates of sedimentation.
Heavy metal contamination in sediments is often highly correlated with the percentage of mud content due to the adherence of chemicals to fine sediments and/or organic content. It is, therefore, not surprising that heavy metal concentrations were also highest in the depositional inner areas of the harbour, such as Te Puna Estuary. The heavy metal contaminant levels within Tauranga were well below relevant guideline thresholds and lower than concentrations measured in many other estuaries in New Zealand and overseas. Although the three metals recorded were found to be highly correlated, zinc levels tended to be closer to guideline thresholds for possible biological effects.
Sediment nutrient concentrations in the harbour tended to decline with distance from the inner harbour and associated rivers. Te Puna Estuary showed comparatively high nitrogen and phosphorus loadings. Comparison of sediment nutrient concentrations with other New Zealand estuaries indicates that the Tauranga Harbour sits within a range typical for slightly to moderately enriched estuaries. Although total phosphorus was low compared with other estuaries, total N:P ratios suggest Tauranga Harbour is still limited by nitrogen.
We developed a BHM using statistical ordination techniques to identify key stressors affecting the ‘health’ of macrofaunal communities. Sediments, nutrients and heavy metals were identified as key ‘stressors’, i.e. variables affecting the ecology of the harbour. Therefore, three multivariate models were developed based on the variability in community composition using canonical analysis of principal coordinates (CAP). The ecological assemblages generally reflected gradients of stress or pollution very well. However, the CAP models for sedimentation and contamination performed best. In general, the multivariate models were found to be more sensitive to changing ecological health than simple univariate measures (abundance, species diversity, evenness and Shannon-Wiener diversity). This finding has also been reported in the literature where univariate measures based on abundance and diversity were only able to detect significant differences between the most and least disturbed sites, but were not able to differentiate between smaller relative changes in ecological health. Hence univariate measures were less sensitive to smaller degradative changes in community composition. For Tauranga Harbour, ordination models based on community composition appear to be a more sensitive measure of ‘health’ along an ecological gradient and should enable long term degradative change from multiple disturbances to be assessed. This BHM approach can be used as a management or monitoring tool where sites are repeatedly sampled over time and tracked to determine whether the communities are moving towards a more healthy or unhealthy state.
The key species at ‘healthy’ and ‘impacted’ sites as determined from the CAP models were also identified. Species at ‘impacted’ sites can be considered to be tolerant to the stressor (i.e. sediment, nutrients or contaminants), while species with high abundances at only ‘healthy’ sites are sensitive to increasing stressors. We developed species response models for 20 taxa. Although the type of response differed by taxa and stressor, variation in the abundance of most of the taxa modelled was most likely to be better predicted by sedimentation. Unimodal responses were almost always observed in response to nutrients, while declines or skewed unimodal responses were most often observed in response to sedimentation and metals.
The results from this study are consistent with models of macrofaunal species occurrence with respect to sediment mud content developed across a range of New Zealand estuaries by Thrush et al. (2003). Within this report we extend this analysis by also developing models of macrofaunal species occurrence with respect to nutrient and contaminants loadings. Ultimately such statistical models provide a tool to forecast the distribution and abundance of species associated with habitat changes in sediments, nutrients and metals.
In conclusion, Tauranga Harbour is a predominantly sandy harbour with slight to moderate enrichment and low levels of heavy metal contaminants. Sites identified as most impacted by elevated sediments, heavy metal contaminants and nutrients were generally located in the upper reaches of estuaries in some of the least exposed locations. To some extent, this reflects the natural progression of an estuary from land to sea; however, the rates of accumulation of sediments and nutrients have been accelerated as a result of anthropogenic land-based activities. Sediments and contaminants were found to explain the largest variance in benthic communities. Species response models suggest that taxa were either sensitive to elevated sediments, nutrients loading or contamination at all levels, or sensitive to these stressors beyond a critical point.

text: Ecological Survey of Tauranga Harbour This report summarises the results of biological and physical data collected from a broad scale intertidal survey of Tauranga Harbour conducted between December 2011 and February 2012. The survey was designed to understand more fully the role of various anthropogenic stressors on the ecology of the harbour. The research was conducted as part of the Manaaki Taha Moana (MTM) programme. The wider research project aims to restore and enhance coastal ecosystems and their services of importance to iwi/hapū, by working with iwi to improve knowledge of these ecosystems and the degradation processes that affect them.
In this report we assess the health of macrofaunal benthic communities (bottom-dwelling animals) as well as trends in sediments, nutrients and contaminants. The results indicate that the sites identified as most impacted were generally located in the upper reaches of estuaries in some of the locations least exposed to wind, waves and currents. In addition, the biological community composition characterising sites with different sediment textures, nutrient and contaminant loadings were found to vary. Sediments within Tauranga Harbour were predominantly sandy with the percentage of mud within a similar range as measured for other New Zealand estuaries. The exceptions included Te Puna Estuary and Apata Estuary, which experience higher rates of sedimentation.
Heavy metal contamination in sediments is often highly correlated with the percentage of mud content due to the adherence of chemicals to fine sediments and/or organic content. It is, therefore, not surprising that heavy metal concentrations were also highest in the depositional inner areas of the harbour, such as Te Puna Estuary. The heavy metal contaminant levels within Tauranga were well below relevant guideline thresholds and lower than concentrations measured in many other estuaries in New Zealand and overseas. Although the three metals recorded were found to be highly correlated, zinc levels tended to be closer to guideline thresholds for possible biological effects.
Sediment nutrient concentrations in the harbour tended to decline with distance from the inner harbour and associated rivers. Te Puna Estuary showed comparatively high nitrogen and phosphorus loadings. Comparison of sediment nutrient concentrations with other New Zealand estuaries indicates that the Tauranga Harbour sits within a range typical for slightly to moderately enriched estuaries. Although total phosphorus was low compared with other estuaries, total N:P ratios suggest Tauranga Harbour is still limited by nitrogen.
We developed a BHM using statistical ordination techniques to identify key stressors affecting the ‘health’ of macrofaunal communities. Sediments, nutrients and heavy metals were identified as key ‘stressors’, i.e. variables affecting the ecology of the harbour. Therefore, three multivariate models were developed based on the variability in community composition using canonical analysis of principal coordinates (CAP). The ecological assemblages generally reflected gradients of stress or pollution very well. However, the CAP models for sedimentation and contamination performed best. In general, the multivariate models were found to be more sensitive to changing ecological health than simple univariate measures (abundance, species diversity, evenness and Shannon-Wiener diversity). This finding has also been reported in the literature where univariate measures based on abundance and diversity were only able to detect significant differences between the most and least disturbed sites, but were not able to differentiate between smaller relative changes in ecological health. Hence univariate measures were less sensitive to smaller degradative changes in community composition. For Tauranga Harbour, ordination models based on community composition appear to be a more sensitive measure of ‘health’ along an ecological gradient and should enable long term degradative change from multiple disturbances to be assessed. This BHM approach can be used as a management or monitoring tool where sites are repeatedly sampled over time and tracked to determine whether the communities are moving towards a more healthy or unhealthy state.
The key species at ‘healthy’ and ‘impacted’ sites as determined from the CAP models were also identified. Species at ‘impacted’ sites can be considered to be tolerant to the stressor (i.e. sediment, nutrients or contaminants), while species with high abundances at only ‘healthy’ sites are sensitive to increasing stressors. We developed species response models for 20 taxa. Although the type of response differed by taxa and stressor, variation in the abundance of most of the taxa modelled was most likely to be better predicted by sedimentation. Unimodal responses were almost always observed in response to nutrients, while declines or skewed unimodal responses were most often observed in response to sedimentation and metals.
The results from this study are consistent with models of macrofaunal species occurrence with respect to sediment mud content developed across a range of New Zealand estuaries by Thrush et al. (2003). Within this report we extend this analysis by also developing models of macrofaunal species occurrence with respect to nutrient and contaminants loadings. Ultimately such statistical models provide a tool to forecast the distribution and abundance of species associated with habitat changes in sediments, nutrients and metals.
In conclusion, Tauranga Harbour is a predominantly sandy harbour with slight to moderate enrichment and low levels of heavy metal contaminants. Sites identified as most impacted by elevated sediments, heavy metal contaminants and nutrients were generally located in the upper reaches of estuaries in some of the least exposed locations. To some extent, this reflects the natural progression of an estuary from land to sea; however, the rates of accumulation of sediments and nutrients have been accelerated as a result of anthropogenic land-based activities. Sediments and contaminants were found to explain the largest variance in benthic communities. Species response models suggest that taxa were either sensitive to elevated sediments, nutrients loading or contamination at all levels, or sensitive to these stressors beyond a critical point.

year: 2017