Controle avançado aplicado a colunas de destilação / Advanced control applied to distillation columns

O objetivo desse trabalho é apresentar uma proposta de operação e controle de uma coluna de destilação em uma usina sucroalcooleira que combina a aplicação de ferramentas de sintonia de malhas de controle, de controle avançado de processos e de sistemas de otimização, além do monitoramento do processo em tempo real. Com essa metodologia obtém-se uma diminuição significativa da variabilidade do processo, além da otimização da produção de etanol. O resultado mostrou que a combinação dessas ferramentas de automação contribuiu para uma redução de 91,2% na variabilidade do processo, um aumento de 9,2% na capacidade de produção de etanol e uma redução significativa das intervenções dos operadores no processo

Controle avançado aplicado a colunas de destilação / Advanced control applied to distillation columns

O objetivo desse trabalho é apresentar uma proposta de operação e controle de uma coluna de destilação em uma usina sucroalcooleira que combina a aplicação de ferramentas de sintonia de malhas de controle, de controle avançado de processos e de sistemas de otimização, além do monitoramento do processo em tempo real. Com essa metodologia obtém-se uma diminuição significativa da variabilidade do processo, além da otimização da produção de etanol. O resultado mostrou que a combinação dessas ferramentas de automação contribuiu para uma redução de 91,2% na variabilidade do processo, um aumento de 9,2% na capacidade de produção de etanol e uma redução significativa das intervenções dos operadores no processo.

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

Improved grazing activity of dairy heifers in shaded tropical grasslands

ABSTRACT: Trees in the production systems can effectively reduce hot weather-induced stress in the Brazilian Midwest. High temperatures cause changes in animals daily routine, and trees into pastures can promote benefits. The aim of this research was to evaluate the behavior of dairy heifers in silvopastoral systems in the state of Mato Grosso, Brazil. A herd of 24 crossbreed heifers (3/4 and 7/8 Holstein/Zebu), 350kg average weight, was evaluated over three seasons. Piatã grass was managed under three shade levels: full-sun, moderate-shade, and intensive-shade provided by 10 to 12m high Eucalyptus trees. Behavior data were collected every 15 minutes from 8:30h to 16h. Shade availability significantly impacted heifer behavior, mainly affecting grazing frequency and time during the hottest hours. Grazing behavior was affected by shade levels during the different seasons. Heifers showed preferred grazing times. Heifers in the intensive-shade system visited shady areas during the hottest hours throughout the seasons. Heifers in the full sun-system avoided grazing during the warmer times, ceasing feeding activities. Our results from the Brazilian Midwest showed that shade availability causes breed heifers to change their daily routine