Eliminating non-renewable CO2 emissions from sewage treatment: An anaerobic migrating bed reactor pilot plant study

The aim of this work was to demonstrate at pilot scale a high level of energy recovery from sewage utilising a primary Anaerobic Migrating Bed Reactor (AMBR) operating at ambient temperature to convert COD to methane. The focus is the reduction in non-renewable CO2 emissions resulting from reduced energy requirements for sewage treatment. A pilot AMBR was operated on screened sewage over the period June 2003 to September 2004. The study was divided into two experimental phases. In Phase 1 the process operated at a feed rate of 10 L/h (HRT 50 h), SRT 63 days, average temperature 28 degrees C and mixing time fraction 0.05. In Phase 2 the operating parameters were 20 L/h, 26 days, 16 degrees C and 0.025. Methane production was 66% of total sewage COD in Phase 1 and 23% in Phase 2. Gas mixing of the reactor provided micro-aeration which suppressed sulphide production. Intermittent gas mixing at a useful power input of 6 W/m(3) provided satisfactory process performance in both phases. Energy consumption for mixing was about 1.5% of the energy conversion to methane in both operating phases. Comparative analysis with previously published data confirmed that methane supersaturation resulted in significant losses of methane in the effluent of anaerobic treatment systems. No cases have been reported where methane was considered to be supersaturated in the effluent. We have shown that methane supersaturation is likely to be significant and that methane losses in the effluent are likely to have been greater than previously predicted. Dissolved methane concentrations were measured at up to 2.2 times the saturation concentration relative to the mixing gas composition. However, this study has also demonstrated that despite methane supersaturation occurring, microaeration can result in significantly lower losses of methane in the effluent (< 11% in this study), and has demonstrated that anaerobic sewage treatment can genuinely provide energy recovery. The goal of demonstrating a high level of energy recovery in an ambient anaerobic bioreactor was achieved. An AMBR operating at ambient temperature can achieve up to 70% conversion of sewage COD to methane, depending on SRT and temperature. (c) 2006 Wiley Periodicals, Inc.

Stochastic modelling of the invasion process of nematodes in fly larvae

Stochastic models based on Markov birth processes are constructed to describe the process of invasion of a fly larva by entomopathogenic nematodes. Various forms for the birth (invasion) rates are proposed. These models are then fitted to data sets describing the observed numbers of nematodes that have invaded a fly larval after a fixed period of time. Non-linear birthrates are required to achieve good fits to these data, with their precise form leading to different patterns of invasion being identified for three populations of nematodes considered. One of these (Nemasys) showed the greatest propensity for invasion. This form of modelling may be useful more generally for analysing data that show variation which is different from that expected from a binomial distribution.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Formalising progress properties of non-blocking programs

A non-blocking program is one that uses non-blocking primitives, such as load-linked/store-conditional and compare-and-swap, for synchronisation instead of locks so that no process is ever blocked. According to their progress properties, non-blocking programs may be classified as wait-free, lock-free or obstruction-free. However, a precise description of these properties does not exist and it is not unusual to find a definition that is ambiguous or even incorrect. We present a formal definition of the progress properties so that any confusion is removed. The formalisation also allows one to prove the widely believed presumption that wait-freedom is a special case of lock-freedom, which in turn is a special case of obstruction-freedom.