Energy potential and alternative usages of biogas and sludge from UASB reactors: case study of the Laboreaux wastewater treatment plant (Itabira) / Potencial energético e alternativas para o aproveitamento do biogás e lodo de reatores UASB: Estudo de caso Estação de tratamento de efluentes Laboreaux (Itabira)

Este trabalho estuda o potencial de aproveitamento energético dos subprodutos biogás e lodo gerados na estação de tratamento de efluentes (ETE) Laboreaux em Itabira (MG), composta de reatores UASB, filtros biológicos percoladores e unidade de desaguamento do lodo por filtro prensa. Os subprodutos biogás e lodo foram caracterizados em termos quantitativos (produção) e qualitativos (composição e poder calorífico) durante 12 meses de monitoramento. Foram estudados dois cenários de aproveitamento energético dos subprodutos: (i) uso prioritário do biogás para a secagem térmica do lodo e o excedente de biogás para geração de eletricidade em motor de combustão interna; e (ii) uso prioritário do biogás visando à geração de eletricidade e ao aproveitamento do calor dos gases de exaustão para a secagem térmica de lodo. Para a análise desses cenários, utilizou-se o software CHEMCAD® a fim de determinar as condições de queima do biogás em câmara de combustão e em motor de combustão interna, assim como na determinação dos balanços de massa e energia. O estudo analisou o potencial de aproveitamento dos subprodutos do tratamento como fonte de energia renovável para uso na própria ETE e para fornecimento a terceiros. No cenário 1, a geração de eletricidade é menor (atendendo 22,2% da demanda de energia da ETE), mas a secagem térmica possibilita maior redução no volume final de lodo a ser disposto ou a eliminação completa de disposição final se o lodo seco final (com 10% de umidade) for utilizado como combustível por terceiros. No cenário 2, a geração de eletricidade é capaz de suprir 57,6% da demanda de energia da ETE, todavia o calor contido nos gases de exaustão não é suficiente para a secagem de todo o lodo desaguado, configurando uma menor redução na quantidade de lodo a ser disposto (13,5 ou 24,9% de redução em massa, conforme a alternativa de remoção de umidade selecionada)

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Aproveitamento energético de biogás gerado em reatores UASB tratando esgoto doméstico

Exportado OPUSMade available in DSpace on 2019-08-12T16:43:15Z (GMT). No. of bitstreams: 1 tese_n_57___l_via_lobato.pdf: 6484204 bytes, checksum: 57a84b67518da71e9fb4d346cf10609f (MD5) Previous issue date: 19A pesquisa teve como objetivo avaliar as principais alternativas para valorização energética do biogás produzido em reatores UASB tratando esgoto doméstico. O foco principal esteve relacionado à cogeração de energia e calor para secagem térmica e desinfecção do lodo excedente de reatores UASB. Um modelo matemático foi desenvolvido com o intuito de se estimar, com maior precisão, a parcela de DQO recuperada como metano no biogás e que, efetivamente, representasse o potencial de recuperação de energia em reatores UASB tratando esgoto doméstico. O modelo buscou incluir todas as rotas de conversão e perdas no reator, incluindo a parcela de DQO utilizada para redução de sulfato e as perdas de metano nos gases residuais e dissolvido no efluente. Resultados da produção de biogás em reatores UASB, de pequena e larga escala, foram usados para validar o modelo. Os resultados mostraram que o modelo permitiu uma estimativa mais realista da produção de biogás e de seu potencial energético. A comparação entre as principais alternativas para a valorização energética do biogás produzido em reatores UASB tratando esgoto doméstico indicou que a combustão direta com recuperação de energia térmica é a mais simples e com uma melhor relação custo/benefício. A cogeração de energia e calor parece ser viável para quase todas as plantas em larga escala, especialmente, no caso dos motores de combustão interna. Uma pequena unidade de cogeração de energia e calor foi testada no Centro de Pesquisa e Treinamento em Saneamento UFMG/COPASA - CePTS, localizado na Estação de Tratamento de Esgoto Arrudas, em Belo Horizonte, Minas Gerais, Brasil. A unidade é composta por um motor de combustão interna (MCI) adaptado para funcionar a biogás, um protótipo de secador térmico e outros periféricos (compressor, tanque de armazenamento do biogás, soprador de ar etc.). O calor dos gases de exaustão do MCI era direcionado para o protótipo do secador térmico com o objetivo de realizar a secagem do lodo e sua desinfecção. Os resultados mostraram que o aparato experimental é autossuficiente em termos de energia elétrica, produzindo ainda um excedente, disponível para outros usos. Os testes de secagem e desinfecção do lodo duraram sete horas, permitindo um aumento dos teores de sólidos de lodo de 4 para 8% (50% de redução do volume de lodo). Apesar da secagem de lodo não ter sido possível (apenas o adensamento foi alcançado), o processo de desinfecção mostrou ser muito eficaz, mensurado pela completa inativação de ovos de helmintos.The research aimed at evaluating the main alternatives for energy recovery from biogas produced in upflow anaerobic sludge blanket (UASB) reactors treating domestic wastewater. The primary focus was related to cogeneration of power and heat for thermal drying and disinfection of the excess sludge. A comprehensive mathematical model was developed in order to estimate that more precisely the fraction of chemical oxygen demand (COD) recovered as methane in the biogas and which, effectively, represented the potential for energy recovery in upflow UASB reactors treating domestic wastewater. The model sought to include all routes of conversion and losses in the reactor, including the portion of COD used for the reduction of sulfates and the loss of methane in the residual gas and dissolved in the effluent. Results from the production of biogas in small- and large-scale UASB reactors were used to validate the model. The results showed that the model allowed a more realistic estimate of biogas production and of its energy potential. Comparison of the main alternatives for energy recovery from biogas produced in UASB reactors treating domestic wastewater indicated that direct combustion with thermal energy recovery is the simplest one and with a better cost/benefit ratio. Cogeneration of power and heat seems to be feasible for nearly all large-scale plants, especially in the case of internal combustion engines. A small unit of cogeneration of energy and heat was tested at the Centre for Research and Training on Sanitation UFMG/COPASA CePTS, located at the Arrudas Sewage Treatment Plant, in Belo Horizonte, Minas Gerais, Brazil. The unit consisted of an engine power generator adapted to run on biogas, a thermal dryer prototype and other peripherals (compressor, biogas storage tank, air blower etc.). The heat from engine power generator exhaust gases was directed towards the thermal dryer prototype to dry the sludge and disinfect it. The results showed that the experimental apparatus is self-sufficient in electricity, even producing a surplus, available for other uses. The tests of drying and disinfection of sludge lasted 7 h, leading to an increase in solids content from 4 to 8% (50% reduction in sludge volume). Although the drying of sludge was not possible (only thickening was achieved), the disinfection process proved very effective, enabling the complete inactivation of helminth eggs

Neotropical freshwater fisheries : A dataset of occurrence and abundance of freshwater fishes in the Neotropics

The Neotropical region hosts 4225 freshwater fish species, ranking first among the world's most diverse regions for freshwater fishes. Our NEOTROPICAL FRESHWATER FISHES data set is the first to produce a large-scale Neotropical freshwater fish inventory, covering the entire Neotropical region from Mexico and the Caribbean in the north to the southern limits in Argentina, Paraguay, Chile, and Uruguay. We compiled 185,787 distribution records, with unique georeferenced coordinates, for the 4225 species, represented by occurrence and abundance data. The number of species for the most numerous orders are as follows: Characiformes (1289), Siluriformes (1384), Cichliformes (354), Cyprinodontiformes (245), and Gymnotiformes (135). The most recorded species was the characid Astyanax fasciatus (4696 records). We registered 116,802 distribution records for native species, compared to 1802 distribution records for nonnative species. The main aim of the NEOTROPICAL FRESHWATER FISHES data set was to make these occurrence and abundance data accessible for international researchers to develop ecological and macroecological studies, from local to regional scales, with focal fish species, families, or orders. We anticipate that the NEOTROPICAL FRESHWATER FISHES data set will be valuable for studies on a wide range of ecological processes, such as trophic cascades, fishery pressure, the effects of habitat loss and fragmentation, and the impacts of species invasion and climate change. There are no copyright restrictions on the data, and please cite this data paper when using the data in publications