Os lixões urbanos são práticas antigas e constantes nas cidades brasileiras, nas quais os resíduos sólidos são depositados em locais inadequados e sem qualquer tratamento, o que vem a ocasionar impactos para a população, a saúde pública e o meio ecológico. Neste trabalho teve- se como objetivo elaborar um diagnóstico qualitativo da degradação ambiental na área do lixão de Pombal-PB. A metodologia teve por base a realização de visitas de campo, entrevistas aos gestores do município, catadores da área do lixão e moradores no entorno da área em estudo. Fez-se a identificação dos impactos ambientais utilizando-se os métodos Ad Hoc e Check Lists, e proposição de medidas voltadas à recuperação da área. De acordo com os resultados, observou-se que os principais impactos diagnosticados foram: a contaminação do solo, dos recursos hídricos, do ar atmosférico; o aumento dos processos erosivos; redução ou perda total da fauna e flora; riscos aos catadores e impacto na saúde pública. Os fatores mais afetados foram o antrópico, o solo, a fauna, a flora e a paisagem. Propôs-se a biorremediação e o reflorestamento para a recuperação da área, cujo uso final indicado foi área de preservação.
Hydrological models (HMs) can be applied for different purposes, and a key step is model calibration using objective functions (OF) to quantify the agreement between observed and calculated discharges. Fully understanding the OF is important to properly take advantage of model calibration and interpret the results. This study evaluates 36 OF proposed in the literature, considering two watersheds of different hydrological regimes. Daily simulated streamflow time-series, using a distributed hydrological model (MGB-IPH), and ten daily streamflow synthetic time-series, generated from the observed and calculated streamflows, were used in the analysis of each watershed. These synthetic data were used to evaluate how does each metric evaluate hypothetical cases that present isolated very well known error behaviors. Despite of all NSE-derived (Nash-Sutcliffe efficiency) metrics that use the square of the residuals in their formulation have shown higher sensitivity to errors in high flows, the ones that use daily and monthly averages of flow rates in absolute terms were more stringent than the others to assess HMs performance. Low flow errors were better evaluated by metrics that use the flow logarithm. The constant presence of zero flow rates deteriorate them significantly, with the exception of the metrics TRMSE (Transformed root mean square error) did not demonstrate this problem. An observed limitation of the formulations of some metrics was that the errors of overestimation or underestimation are compensated. Our results reassert that each metric should be interpreted specifically thinking about the aspects it has been proposed for, and simultaneously taking into account a set of metrics would lead to a broader evaluation of HM ability (e.g. multiobjective model evaluation). We recommend that the use of synthetic time series as those proposed in this work could be useful as an auxiliary step towards better understanding the evaluation of a calibrated hydrological model for each study case, taking into account model capabilities and observed hydrologic regime characteristics.
RESUMO O estudo da descarga de sólidos de uma determinada bacia hidrográfica é importante para que se possam tomar decisões corretas quanto ao planejamento de gestão dos recursos hídricos. O objetivo do presente trabalho foi determinar curvas-chave que representam cargas de sedimentos em suspensão no Rio Piranhas. O estudo foi realizado na sub-bacia hidrográfica do Rio Piranhas. Foram realizadas 15 campanhas de medições hidrossedimentométricas, no período de novembro de 2012 a maio de 2013, envolvendo as medições de concentração de sedimentos em suspensão e de descargas líquida e sólida. Foram plotadas curvas-chave de sedimentos em suspensão para os períodos seco e chuvoso, as quais apresentaram bons coeficientes de determinação.
Objectives/Scope The drop on the daily rates for the Drilling Rigs in the recent years has pushed Drilling Contractors in the industry for innovative solutions. Industry 4.0 is bringing many features and technologies to overcome these challenges and help the companies to meet this new scenario. This paper will present how a partnership between Ocyan, an ultra-deep-water Drilling Contractor and RIO Analytics, an A.I. technology company that develops solutions for failure prediction of industrial assets, is using artificial intelligence and Data Analytics to manage and control drill pipes operation and prevent failures, correlating different sources of information. Drill pipe is one of the most critical equipment on a deepwater Drilling Rig and Drill pipes incidents are one of the biggest causes of nonproductive time and unplanned costs in the drilling industry. In most cases, the lack of information about the drill pipes, such as historical and operational efforts related to their individual use make it very hard to investigate an incident that occurred, and consequently, to predict a pipe failure. Also, some operational limits (such as make-up torque and elevator capacity) that are driven by dimensional inspection results are often not used correctly for operational planning, leading to unnecessary risks. Methods, Procedures, Process To be able to apply failure prediction algorithms and correlate operational and historical information for each individual drill pipe, a web-based software was developed building a valuable database and management system, allowing users to easily navigate for drill pipes information, generate reports, and simulate operational scenarios by providing operation planned tally (list of drill pipes). Warnings are generated as the results for the simulations indicating any risk for operations. Critical situations are made available to the rig crew, immediately transmitted to the Ocyan's Decision Support Center (CSD) and management team onshore, while less critical alerts are recorded in the system for further investigation. Software integrates with different inspection reports formats and automatically updates critical information on drill pipe's database, allowing also to identify invalid or wrong information on these reports, upon inspection criteria used. Results, Observations, Conclusions With the implementation of this predictive maintenance solution, companies aim to increase Operational and Process Safety, avoid NPT and reduce maintenance cost regarding the Drill Pipes. Novel/Additive Information Based on the integration with real-time data from rig sensors and identification of active operational tally, it has been possible to automatically control drilled meters and rotating hours for each drill pipe, which triggers inspection requirements, generating automated work orders for the CMMS. Also, an algorithm was developed to calculate real-time damage in each drill pipe during operation, considering the most significant parameters (such as torque, tension, drilling depth, wear, pressure, dog leg severity, jarring, etc.), using it to provide valuable information for failure prediction.
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