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  1. Free, publicly-accessible full text available August 1, 2023
  2. Egolfopoulos, Fokion (Ed.)
    This research focused on the size and overall porosity (pore volume) of carbonaceous chars, originating from high-heating rates and high-temperature pyrolysis and/or combustion of biomass. Emphasis was given to torrefied biomass chars. First, the porosity of char residues of single biomass particles of known mass was determined, based on an assumed value of skeletal density and by comparing experimentally observed temperature-time histories with numerical predictions of their burnout times. The average char porosities (effective porosities) of several raw and torrefied biomass particles were calculated to be in the range of 92–95%. Thereafter, these deduced porosity values were input again tomore »the model to calculate the size of chars of other biomass particle precursors, whose initial size and mass were not known. Such biomass particles were sieve-classified to different nominal size ranges. This time, besides the porosity, representative time-temperature profiles of biomass particles in the aforementioned size ranges were also input to the model. Biomass particles are highly irregular with large aspect ratios and, in many cases, they melt and spherodize under high heating rates and elevated temperatures. Knowledge of the initial size of the chars, upon extinction of the volatile flames, is needed for modeling their heterogeneous combustion phase. For this purpose, numerical predictions were in general agreement with measurements of char size obtained from both scanning electron microscopy of captured chars and real-time high-speed, high- magnification cinematographic observations of their combustion. Results showed that the generated chars of the examined biomass types were highly porous with large cavities. The average initial dimension of the chars, upon rapid pyrolysis, was in the range of 50–60% the mid-value of the mesh size of the sieves used to size-classify their highly irregular parent biomass particles.« less
    Free, publicly-accessible full text available August 1, 2023
  3. Abstract The COVID-19 pandemic has reduced travel but led to an increase in household food and energy consumption. Previous studies have explored the changes in household consumption of food and energy during the pandemic; however, the economy-wide environmental implications of these changes have not been investigated. This study addresses the knowledge gap by estimating the life cycle environmental impacts of U.S. households during the pandemic using a hybrid life cycle assessment. The results revealed that the reduction in travel outweighed the increase in household energy consumption, leading to a nationwide decrease in life cycle greenhouse gas emissions (−255 Mton COmore »2 eq), energy use (−4.46 EJ), smog formation (−9.17 Mton O 3 eq), minerals and metal use (−16.1 Mton), commercial wastes (−8.31 Mton), and acidification (−226 kton SO 2 eq). However, U.S. households had more life cycle freshwater withdrawals (+8.6 Gton) and slightly higher eutrophication (+0.2%), ozone depletion (+0.7%), and freshwater ecotoxicity (+2.1%) caused by increased household energy and food consumption. This study also demonstrated the environmental trade-offs between decreased food services and increased food consumption at home, resulting in diverse trends for food-related life cycle environmental impacts.« less
    Free, publicly-accessible full text available February 25, 2023
  4. Co-evolving sequences are ubiquitous in a variety of applications, where different sequences are often inherently inter-connected with each other. We refer to such sequences, together with their inherent connections modeled as a structured network, as network of co-evolving sequences (NoCES). Typical NoCES applications in- clude road traffic monitoring, company revenue prediction, motion capture, etc. To date, it remains a daunting challenge to accurately model NoCES due to the coupling between network structure and sequences. In this paper, we propose to modeling NoCES with the aim of simultaneously capturing both the dynamics and the inter- play between network structure and sequences.more »Specifically, we propose a joint learning framework to alternatively update the network representations and sequence representations as the se- quences evolve over time. A unique feature of our framework lies in that it can deal with the case when there are co-evolving sequences on both network nodes and edges. Experimental evaluations on four real datasets demonstrate that the proposed approach (1) out- performs the existing competitors in terms of prediction accuracy, and (2) scales linearly w.r.t. the sequence length and the network size.« less
    Free, publicly-accessible full text available April 1, 2023
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