A high demand of oil products on daily basis requires oil processing plants to work with maximum efficiency. Oil, water and gas separation in a three-phase separator is one of the first operations that are performed after crude oil is extracted from an oil well. Failure of the components of the separator introduces the potential hazard of flammable materials being released into the environment. This can escalate to a fire or explosion. Such failures can also cause downtime for the oil processing plant since the separation process is essential to oil production. Fault detection and diagnostics techniques used in the oil and gas industry are typically threshold based alarm techniques. Observing the sensor readings solely allows only a late detection of faults on the separator which is a big deficiency of such a technique, since it causes the oil and gas processing plants to shut down.A fault detection and diagnostics methodology for three-phase separators based on Bayesian Belief Networks (BBN) is presented in this paper. The BBN models the propagation of oil, water and gas through the different sections of the separator and the interactions between component failure modes and process variables, such as level or flow monitored by sensors installed on the separator. The paper will report on the results of the study, when the BBNs are used to detect single and multiple failures, using sensor readings from a simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the separator. 相似文献
The oxygenated species, massively produced in the energy production plants based on combustion processes, constitute one of the most numerous categories of hazardous air pollutants. Therefore, development of real time diagnostic tools are needed in order to study their formation during combustion processes and to reveal their presence both in the exhaust and in the atmosphere. In this work, oxygenated compounds were identified inside fuel-rich premixed ethylene/air flames by means of ultraviolet fluorescence spectroscopy with the support of qualitative chemical analysis of the sampled combustion gases.
Strong band progression, typical of aldehydic functionality, were recognized in fluorescence spectra (λexc=355 nm) measured in the early oxidation region of premixed flames varying the equivalence ratio from 3.0 up 21.6. Downstream of the oxidation region, spectroscopic signatures of pyrolytic species were found to prevail on those peculiar of oxygenated compound. The position and the extension of the two main flame zones were found to depend on the flame conditions (C/O ratio) due to the effect of the C/O ratio on the temperature history along the flame axis. This correlation was interpreted on the basis of the measured axial temperature profiles. 相似文献
The presence of toxic substances in wastewaters and outdoor bodies of water is an important ecotoxicological issue. The aim of this review is to illustrate how duckweeds, which are small, simply constructed, floating aquatic plants, are well suited to addressing this concern. The ability of duckweeds to grow rapidly on nutrient-rich water and to facilitate the removal of many substances from aqueous solution comprises the potential of these macrophytes for the remediation of wastewater and polluted aqueous reservoirs, while producing usable biomass containing the unwanted substances having been taken up. Their ease of cultivation under controlled and even sterile conditions makes duckweeds excellent test organisms for determining the toxicity of water contaminants, and duckweeds are important as model aquatic plants in the assessment of ecotoxicity. Duckweeds are also valuable for establishing biomarkers for the toxic effects of water contaminants on aquatic higher plants, but the current usefulness of duckweed biomarkers for identifying toxicants is limited. The recent sequencing of a duckweed genome holds the promise of combining the determination of water contaminant toxicity with toxicant diagnostics by means of gene expression profiling via DNA microarrays. 相似文献
The expansion of the industrial economy and the increase of population in Northeast Asian countries have caused much interestin climate monitoring related to global warming. However, new techniques and better platforms for the measurement of globalwarming and regional databases are still old-fashioned and arenot being developed sufficiently. With respect to this agenda,since 1993, at the request of the World Meteorological Organization (WMO), to monitor functions of global warming, theKorea Meteorological Administration (KMA) has set up a Global Atmospheric Watch (GAW) Station on the western coast of Korea(Anmyun-do) and has been actively monitoring global warming overNortheast Asia. In addition, atmospheric carbon dioxide (CO2) has been measured for a similar KMA global warmingprogram at Kosan, Cheju Island since 1990. Aerosol and radiationhave also been measured at both sites as well as in Seoul. Theobservations have been analyzed using diagnostics of climate change in Northeast Asia and also have been internationally compared. Results indicate that greenhouse gases are in good statistic agreement with the NOAA/Climate Monitoring and Diagnostics Laboratory (CMDL) long-term trends of monthly meanconcentrations and seasonal cycles. Atmospheric particulatematter has also been analyzed for particular Asian types interms of optical depth, number concentration and size distribution. 相似文献
This paper presents a complete Bayesian methodology for analyzing spatial data, one which employs proper priors and features diagnostic methods in the Bayesian spatial setting. The spatial covariance structure is modeled using a rich class of covariance functions for Gaussian random fields. A general class of priors for trend, scale, and structural covariance parameters is considered. In particular, we obtain analytic results that allow easy computation of the predictive distribution for an arbitrary prior on the parameters of the covariance function using importance sampling. The computations, as well as model diagnostics and sensitivity analysis, are illustrated with a set of precipitation data. 相似文献