Performance evaluation of a water/silicone oil two‐phase partitioning bioreactor using Rhodococcus erythropolis T902.1 to remove volatile organic compounds from gaseous effluents

BACKGROUND: In the framework of biological processes used for waste gas treatment, the impact of the inoculum size on the start‐up performance needs to be better evaluated. Moreover, only a few studies have investigated the behaviour of elimination capacity and biomass viability in a two‐phase partitioning bioreactor (TPPB) used for waste gas treatment. Lastly, the impact of ethanol as a co‐substrate remains misunderstood. RESULTS: Firstly, no benefit of inoculation with a high cellular density (>1.5 g L^?1) was observed in terms of start‐up performance. Secondly, the TPPB was monitored for 38 days to characterise its behaviour under several operational conditions. The removal efficiency remained above 63% for an inlet concentration of 7 g isopropylbenzene (IPB) m^?3 and at some time points reached 92% during an intermittent loading phase (10 h day^?1), corresponding to a mean elimination capacity of 4 × 10^?3 g L^?1 min^?1 (240 g m^?3 h^?1) for a mean IPB inlet load of 6.19 × 10^?3 g L^?1 min^?1 (390 g m^?3 h^?1). Under continuous IPB loading, the performance of the TPPB declined, but the period of biomass acclimatisation to this operational condition was shorter than 5 days. The biomass grew to approximately 10 g L^?1 but the cellular viability changed greatly during the experiment, suggesting an endorespiration phenomenon in the bioreactor. It was also shown that simultaneous degradation of IPB and ethanol occurred, suggesting that ethanol improves the biodegradation process without causing oxygen depletion. CONCLUSION: A water/silicone oil TPPB with ethanol as co‐substrate allowed the removal of a high inlet load of IPB during an experiment lasting 38 days. Copyright © 2008 Society of Chemical Industry     

Learning long-term dependencies with gradient descent is difficult

Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies present in the input/output sequences span long intervals. We show why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases. These results expose a trade-off between efficient learning by gradient descent and latching on information for long periods. Based on an understanding of this problem, alternatives to standard gradient descent are considered.     

Photoelectrocatalytic humic acid degradation kinetics and effect of pH,applied potential and inorganic ions

This study addresses the removal of humic acid (HA) dissolved in an aqueous medium by a photoelectrocatalytic process. UV₂₅₄ removal and the degradation of color (Vis₄₀₀) followed pseudo‐first order kinetics. Rate constants were 1.1 × 10^?1 min^?1, 8.3 × 10^?2 min^?1 and 2.49 × 10^?2 min^?1 (R² > 0.97) for UV₂₅₄ degradation and 1.7 × 10^?1 min^?1, 6.5 × 10^?2 min^?1 and 2.0 × 10^?2 min^?1 for color removal from 5 mg dm^?3, 10 mg dm^?3 and 25 mg dm^?3 HA respectively. Following a 2 h irradiation time, 96% of the color, 98% of the humic acid and 85% of the total organic carbon (TOC) was removed from an initial 25 mg dm^?3 HA solution in the photoanode cell. Photocatalytic removal on the same photoanode was also studied in order to compare the two methods of degradation. Results showed that the photoelectrocatalytic method was much more effective than the photocatalytic method especially at high pH values and with respect to UV₂₅₄ removal. The effect of other important reaction variables, eg pH, external potential and electrolyte concentration, on the photoelectrocatalytic HA degradation was also studied. Copyright © 2003 Society of Chemical Industry     

The MITRE E3 family of models structure and results

A family of energy/economic/environmental (E³) models is presented as a mechanism for analysing national policy issues. The family consists of discrete models which are designed to be run in an integrated manner. The outputs of certain models provide the inputs to the next. This structure allows the analyst to readily incorporate an understanding of regional factors such as local energy prices, concerns over air quality, water availability, or attitudes towards construction of new energy facilities, into national assessments of energy policies. This paper reviews the analytic framework within which energy policy issues are currently addressed. The initial family of E³ models is described with the emphasis on the data linkages and feedback which are provided when these models are run sequentially. The ongoing MITRE research programme with the E³ family of models is presented and plans and opportunities for future work are outlined.     

Pyramid collaborative filtering technique for an intelligent autonomous guide agent

This article presents an autonomous guide agent that can observe a community of learners on the web, interpret the learners' inputs, and then assess their sharing. The goal of this agent is to find a reliable helper (tutor or other learner) to assist a learner in solving his task. Despite the growing number of Internet users, the ability to find helpers is still a challenging and important problem. Although helpers could have much useful information about courses to be taught, many learners fail to understand their presentations. For that, the agent must be able to deal autonomously with the following challenges: Do helpers have information that the learners need? Will helpers present information that learners can understand? And can we guarantee that these helpers will collaborate effectively with learners? We have developed a new filtering framework, called a pyramid collaborative filtering model, to whittle the number of helpers down to just one. We have proposed four levels for the pyramid. Moving from one level to another depends on three filtering techniques: domain model filtering, user model filtering, and credibility model filtering. A new technique is filtering according to helpers' credibilities. Our experiments show that this method greatly improves filtering effectiveness. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1065–1082, 2007.     

Kinetic modelling of the catalytic conversion of synthesis gas

A kinetic study for the one-step conversion of synthesis gas to gasoline on a ZnO–Cr₂O₃–ZSM-5 catalyst is described. On this catalyst, three reactions are involved in the overall transformation of synthesis gas: the methanol synthesis, the conversion of methanol to hydrocarbons and the water–gas shift reaction. Under the operating conditions selected for the study, it was found that the water–gas shift was at equilibrium and the methanol was completely converted to hydrocarbons. Consequently, it was postulated that the kinetics of the limiting reaction step, the methanol synthesis on the ZnO–Cr₂O₃ component, was the one that controls the overall reaction rate. Three kinetic model equations describing the rate of synthesis gas conversion on the bifunctional catalyst, were considered to fit the data of the experimental runs performed in a Berty well-mixed reactor. Those equations were derived under very special conditions where the methanol decomposition term could be neglected. It was also observed that in the kinetic equations a term involving the fugacity of CO₂ was required to predict the rate properly. The catalyst deactivation was also taken into account in the analysis.     

Delineating Boundaries for Imprecise Regions

In geographic information retrieval, queries often name geographic regions that do not have a well-defined boundary, such as “Southern France.” We provide two algorithmic approaches to the problem of computing reasonable boundaries of such regions based on data points that have evidence indicating that they lie either inside or outside the region. Our problem formulation leads to a number of subproblems related to red-blue point separation and minimum-perimeter polygons, many of which we solve algorithmically. We give experimental results from our implementation and a comparison of the two approaches. This research is supported by the EU-IST Project No. IST-2001-35047 (SPIRIT) and by grant WO 758/4-2 of the German Research Foundation (DFG).     

A strategic classification support system for brownfield redevelopment

Brownfield redevelopment (BR) is an ongoing issue for governments, communities, and consultants around the world. It is also an increasingly popular research topic in several academic fields. Strategic decision support that is now available for BR is surveyed and assessed. Then a dominance-based rough-set approach is developed and used to classify cities facing BR issues according to the level of two characteristics, BR effectiveness and BR future needs. The data for the classification are based on the widely available results of a survey of US cities. The unique features of the method are its reduced requirement for preference information, its ability to handle missing information effectively, and the easily understood linguistic decision rules that it generates, based on a training classification provided by experts. The resulting classification should be a valuable aid to cities and governments as they plan their BR projects and budgets.     

The SHARC framework for data quality in Web archiving

Web archives preserve the history of born-digital content and offer great potential for sociologists, business analysts, and legal experts on intellectual property and compliance issues. Data quality is crucial for these purposes. Ideally, crawlers should gather coherent captures of entire Web sites, but the politeness etiquette and completeness requirement mandate very slow, long-duration crawling while Web sites undergo changes. This paper presents the SHARC framework for assessing the data quality in Web archives and for tuning capturing strategies toward better quality with given resources. We define data quality measures, characterize their properties, and develop a suite of quality-conscious scheduling strategies for archive crawling. Our framework includes single-visit and visit?Crevisit crawls. Single-visit crawls download every page of a site exactly once in an order that aims to minimize the ??blur?? in capturing the site. Visit?Crevisit strategies revisit pages after their initial downloads to check for intermediate changes. The revisiting order aims to maximize the ??coherence?? of the site capture(number pages that did not change during the capture). The quality notions of blur and coherence are formalized in the paper. Blur is a stochastic notion that reflects the expected number of page changes that a time-travel access to a site capture would accidentally see, instead of the ideal view of a instantaneously captured, ??sharp?? site. Coherence is a deterministic quality measure that counts the number of unchanged and thus coherently captured pages in a site snapshot. Strategies that aim to either minimize blur or maximize coherence are based on prior knowledge of or predictions for the change rates of individual pages. Our framework includes fairly accurate classifiers for change predictions. All strategies are fully implemented in a testbed and shown to be effective by experiments with both synthetically generated sites and a periodic crawl series for different Web sites.