Rapid gravity filtration operational performance assessment and diagnosis for preventative maintenance from on-line data

Rapid gravity filters, the final particulate barrier in many water treatment systems, are typically monitored using on-line turbidity, flow and head loss instrumentation. Current metrics for assessing filtration performance from on-line turbidity data were critically assessed and observed not to effectively and consistently summarise the important properties of a turbidity distribution and the associated water quality risk. In the absence of a consistent risk function for turbidity in treated water, using on-line turbidity as an indicative rather than a quantitative variable appears to be more practical. Best practice suggests that filtered water turbidity should be maintained below 0.1 NTU, at higher turbidity we can be less confident of an effective particle and pathogen barrier. Based on this simple distinction filtration performance has been described in terms of reliability and resilience by characterising the likelihood, frequency and duration of turbidity spikes greater than 0.1 NTU. This view of filtration performance is then used to frame operational diagnosis of unsatisfactory performance in terms of a machine learning classification problem. Through calculation of operationally relevant predictor variables and application of the Classification and Regression Tree (CART) algorithm the conditions associated with the greatest risk of poor filtration performance can be effectively modelled and communicated in operational terms. This provides a method for an evidence based decision support which can be used to efficiently manage individual pathogen barriers in a multi-barrier system

THM and HAA formation from NOM in raw and treated surface waters

The disinfection by-product (DBP) formation potential (FP) of natural organic matter (NOM) in surface water sources has been studied with reference to the key water quality determinants (WQDs) of UV absorption (UV254), colour, and dissolved organic carbon (DOC) concentration. The data set used encompassed raw and treated water sampled over a 30-month period from 30 water treatment works (WTWs) across Scotland, all employing conventional clarification. Both trihalomethane (THM) and haloacetic acid (HAA) FPs were considered. In addition to the standard bulk WQDs, the DOC content was fractionated and analysed for the hydrophobic (HPO) and hydrophilic (HPI) fractions. Results were quantified in terms of the yield (dDBPFP/dWQD) and the linear regression coefficient R2 of the yield trend. The NOM in the raw waters was found to comprise 30–84% (average 66%) of the more reactive HPO material, with this proportion falling to 18–63% (average 50%) in the treated water. Results suggested UV254 to be as good an indicator of DBPFP as DOC or HPO for the raw waters, with R2 values ranging from 0.79 to 0.82 for THMs and from 0.71 to 0.73 for HAAs for these three determinants. For treated waters the corresponding values were significantly lower at 0.52–0.67 and 0.46–0.47 respectively, reflecting the lower HPO concentration and thus UV254 absorption and commensurately reduced precision due to the limit of detection of the analytical instrument. It is concluded that fractionation offers little benefit in attempting to discern or predict chlorinated carbonaceous DBP yield for the waters across the geographical region studied. UV254 offered an adequate estimate of DBPFP based on a mean yield of ∼2600 and ∼2800 μg per cm−1 absorbance for THMFP for the raw and treated waters respectively and ∼3800 and2900 μg cm−1 for HAAFP, albeit with reduced precision for the treated waters

Profiling large-scale lazy functional programs

The LOLITA natural language processing system is an example of one of the ever increasing number of large-scale systems written entirely in a functional programming language. The system consists of over 50,000 lines of Haskell code and is able to perform a number of tasks such as semantic and pragmatic analysis of text, context scanning and query analysis. Such a system is more useful if the results are calculated in real-time, therefore the efficiency of such a system is paramount. For the past three years we have used profiling tools supplied with the Haskell compilers GHC and HBC to analyse and reason about our programming solutions and have achieved good results; however, our experience has shown that the profiling life-cycle is often too long to make a detailed analysis of a large system possible, and the profiling results are often misleading. A profiling system is developed which allows three types of functionality not previously found in a profiler for lazy functional programs. Firstly, the profiler is able to produce results based on an accurate method of cost inheritance. We have found that this reduces the possibility of the programmer obtaining misleading profiling results. Secondly, the programmer is able to explore the results after the execution of the program. This is done by selecting and deselecting parts of the program using a post-processor. This greatly reduces the analysis time as no further compilation, execution or profiling of the program is needed. Finally, the new profiling system allows the user to examine aspects of the run-time call structure of the program. This is useful in the analysis of the run-time behaviour of the program. Previous attempts at extending the results produced by a profiler in such a way have failed due to the exceptionally high overheads. Exploration of the overheads produced by the new profiling scheme show that typical overheads in profiling the LOLITA system are: a 10% increase in compilation time; a 7% increase in executable size and a 70% run-time overhead. These overheads mean a considerable saving in time in the detailed analysis of profiling a large, lazy functional program

The "Business-As-Usual" growth of global primary energy use and carbon dioxide emissions – historical trends and near-term forecasts

We analyse the global primary energy use and total CO2 emissions time series since 1850 and show that their relative growth rates appear to exhibit periodicity with a fundamental timescale of ~60 years and with significant harmonic behaviour. Quantifying the inertia inherent in these dynamics allows forecasting of future "business as usual" energy needs and their associated CO2 emissions. Our best estimates for 2020 are 800 EJ yr−1 for global energy use and 14 Gt yr−1 for global CO2 emissions, with both being above almost all other published forecasts. This suggests the energy and total CO2 emissions landscape in 2020 may be significantly more challenging than currently envisaged

Analogy and Authority in Cyberterrorism Discourse: An Analysis of Global News Media Coverage

This article explores constructions of cyberterrorism within the global news media between 2008 and 2013. It begins by arguing that the preoccupation with questions of definition, threat and response in academic literature on cyberterrorism is problematic, for two reasons. First, because it neglects the constitutivity of representations of cyberterrorism in the news media and beyond; and, second, because it prioritises policy-relevant research. To address this, the article provides a discursive analysis drawing on original empirical research into 31 news media outlets across the world. Although there is genuine heterogeneity in representations of cyberterrorism therein, we argue that constructions of this threat rely heavily on two strategies. First, appeals to authoritative or expert ‘witnesses’ and their institutional or epistemic credibility. And, second, generic or historical analogies, which help shape understanding of the likelihood and consequences of cyberterrorist attack. These strategies have particularly discursive importance, we argue, given the lack of readily available empirical examples of the ‘reality’ of cyberterroris

An intergenerational transmission of sustainability?:ancestral habitus and food production in a traditional agro-ecosystem of the Upper Guinea Forest, West Africa

The need for sustainable alternatives to industrial farming has led to a revival of interest in traditional agro-ecosystems. Whilst it is well recognised that traditional agro-ecosystems are both social and physical–technical – few case studies have examined interactions between both these dimensions in a single system. For a system to be considered sustainable it needs to be shown to have retained key characteristics over several generations. Case studies therefore demand a location where intergenerational transmission of agro-ecological knowledge and practice is ongoing. This paper examines the intergenerational transmission of social and physical–technical dimensions of a traditional agro-ecosystem of the Loma people in NW Liberia. We engage an innovative interdisciplinary combination of methodologies in analyzing interactions between: (i) energy efficiencies (yield to labour) in 3 different Loma food production systems, using longitudinal quantitative surveys, and, (ii) the social institutions that mediate food production practice, using qualitative methods. Our energy efficiency calculations show that AfDE cultivation is more than twice as efficient at producing calories as either shifting cultivation or forest and river extractivism, yet GPS mapping demonstrates that AfDE cultivation is highly spatially restricted despite clear opportunities for expansion and optimisation. This raises an important question: why have the Loma not expanded AfDE cultivation? We propose that despite being fully aware of the opportunities AfDEs present, a Loma ‘ancestral habitus’ and substantive economic rationality restrict areal expansion and optimisation of AfDE cultivation, curtailing growth and structuring a social-ecological system in dynamic equilibrium. Our findings underline that sustainability is not simply a physio-technical issue; social and belief issues appear to be far more important in framing behaviour in traditional agro-ecosystems