title: Heat transfer enhancement for site level indirect heat recovery systems using nanofluids as the intermediate fluid

authors: Tarighaleslami, Amir Hossein

subjects: nanoparticle

abstract: In this paper, implementation of nanofluids as a Heat Transfer Enhancement technique in Process Integration has been studied. A step by step flowchart is introduced and as a case study the effect of replacing water with various nanofluids as the heat transfer media in an industrial Heat Recovery Loop (HRL) has been modelled. Nanofluids are prepared by distributing a nanoparticle through a base fluid such as water, ethylene glycol or oils. Suspended nanoparticles slightly affect the thermal and physical properties of the base fluid. Primarily nanoparticles are added to improve the fluid’s heat transfer characteristics by increasing its Reynolds number and thermal conductivity. HRL system in a large dairy factory in New Zealand has been studied as case study. Results show that by applying various HRL design methods and a nanofluid as an intermediate fluid, an increase in heat recovery is possible without the need for extra heat exchanger area and infrastructure. 1.5 vol.% CuO/water nanofluid has been chosen as an intermediate fluid and by using a constant temperature storage control strategy, heat recovery from liquid–liquid heat exchangers increases between 5% and 9%. The air-side heat transfer coefficient limits the impact of using a nanofluid for the air–liquid exchangers. In other cases, the total available duty from the process stream, such as a condenser, significantly nullifies the heat transfer benefit of using a nano- fluid in a retrofit situation. Alternative to increasing heat recovery, results show that applying a nanofluid in the HRL design phase enables heat exchanger area to decrease significantly for liquid–liquid matches. Results show that the increase in pressure drop and friction factor effects in such a system is negligible

text: Heat transfer enhancement for site level indirect heat recovery systems using nanofluids as the intermediate fluid In this paper, implementation of nanofluids as a Heat Transfer Enhancement technique in Process Integration has been studied. A step by step flowchart is introduced and as a case study the effect of replacing water with various nanofluids as the heat transfer media in an industrial Heat Recovery Loop (HRL) has been modelled. Nanofluids are prepared by distributing a nanoparticle through a base fluid such as water, ethylene glycol or oils. Suspended nanoparticles slightly affect the thermal and physical properties of the base fluid. Primarily nanoparticles are added to improve the fluid’s heat transfer characteristics by increasing its Reynolds number and thermal conductivity. HRL system in a large dairy factory in New Zealand has been studied as case study. Results show that by applying various HRL design methods and a nanofluid as an intermediate fluid, an increase in heat recovery is possible without the need for extra heat exchanger area and infrastructure. 1.5 vol.% CuO/water nanofluid has been chosen as an intermediate fluid and by using a constant temperature storage control strategy, heat recovery from liquid–liquid heat exchangers increases between 5% and 9%. The air-side heat transfer coefficient limits the impact of using a nanofluid for the air–liquid exchangers. In other cases, the total available duty from the process stream, such as a condenser, significantly nullifies the heat transfer benefit of using a nano- fluid in a retrofit situation. Alternative to increasing heat recovery, results show that applying a nanofluid in the HRL design phase enables heat exchanger area to decrease significantly for liquid–liquid matches. Results show that the increase in pressure drop and friction factor effects in such a system is negligible

year: 2016

title: Sampling permutations for Shapley value estimation

authors: Mitchell, Rory

subjects: interpretability

abstract: Game-theoretic attribution techniques based on Shapley values are used to interpret blackbox machine learning models, but their exact calculation is generally NP-hard, requiring approximation methods for non-trivial models. As the computation of Shapley values can be expressed as a summation over a set of permutations, a common approach is to sample a subset of these permutations for approximation. Unfortunately, standard Monte Carlo sampling methods can exhibit slow convergence, and more sophisticated quasi-Monte Carlo methods have not yet been applied to the space of permutations. To address this, we investigate new approaches based on two classes of approximation methods and compare them empirically. First, we demonstrate quadrature techniques in a RKHS containing functions of permutations, using the Mallows kernel in combination with kernel herding and sequential Bayesian quadrature. The RKHS perspective also leads to quasi-Monte Carlo type error bounds, with a tractable discrepancy measure defined on permutations. Second, we exploit connections between the hypersphere S d−2 and permutations to create practical algorithms for generating permutation samples with good properties. Experiments show the above techniques provide significant improvements for Shapley value estimates over existing methods, converging to a smaller RMSE in the same number of model evaluations

text: Sampling permutations for Shapley value estimation Game-theoretic attribution techniques based on Shapley values are used to interpret blackbox machine learning models, but their exact calculation is generally NP-hard, requiring approximation methods for non-trivial models. As the computation of Shapley values can be expressed as a summation over a set of permutations, a common approach is to sample a subset of these permutations for approximation. Unfortunately, standard Monte Carlo sampling methods can exhibit slow convergence, and more sophisticated quasi-Monte Carlo methods have not yet been applied to the space of permutations. To address this, we investigate new approaches based on two classes of approximation methods and compare them empirically. First, we demonstrate quadrature techniques in a RKHS containing functions of permutations, using the Mallows kernel in combination with kernel herding and sequential Bayesian quadrature. The RKHS perspective also leads to quasi-Monte Carlo type error bounds, with a tractable discrepancy measure defined on permutations. Second, we exploit connections between the hypersphere S d−2 and permutations to create practical algorithms for generating permutation samples with good properties. Experiments show the above techniques provide significant improvements for Shapley value estimates over existing methods, converging to a smaller RMSE in the same number of model evaluations

year: 2022

title: Mental health inpatient services: Improving our understanding of the needs of Maori when acutely unwell

authors: Wharewera-Mika, Julie

subjects: Maori

abstract: There are many possible explanations for the pattern of Maori overrepresentation in mental health acute services. This research project focuses on how services can optimally meet the needs of Maori to improve outcomes. This doctoral research in progress is about claiming space for Maori to have a voice in identifying factors that contribute to recovery and Whanau Ora, and offering recommendations to improve the effectiveness of existing services to better meet the needs of Maori Tangata Whaiora and Whanau.

text: Mental health inpatient services: Improving our understanding of the needs of Maori when acutely unwell There are many possible explanations for the pattern of Maori overrepresentation in mental health acute services. This research project focuses on how services can optimally meet the needs of Maori to improve outcomes. This doctoral research in progress is about claiming space for Maori to have a voice in identifying factors that contribute to recovery and Whanau Ora, and offering recommendations to improve the effectiveness of existing services to better meet the needs of Maori Tangata Whaiora and Whanau.

year: 2008