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.

Analysis of line x environment interactions for yield in navy beans. 2. Pattern analysis of lines and environment within years

Seven years of multi-environment yield trials of navy bean (Phaseolus vulgaris L.) grown in Queensland were examined. As is common with plant breeding evaluation trials, test entries and locations varied between years. Grain yield data were analysed for each year using cluster and ordination analyses (pattern analyses). These methods facilitate descriptions of genotype performance across environments and the discrimination among genotypes provided by the environments. The observed trends for genotypic yield performance across environments were partly consistent with agronomic and disease reactions at specific environments and also partly explainable by breeding and selection history. In some cases, similarities in discrimination among environments were related to geographic proximity, in others management practices, and in others similarities occurred between geographically widely separated environments which differed in management practices. One location was identified as having atypical line discrimination. The analysis indicated that the number of test locations was below requirements for adequate representation of line x environment interaction. The pattern analyses methods used were an effective aid in describing the patterns in data for each year and illustrated the variations in adaptive patterns from year to year. The study has implications for assessing the number and location of test sites for plant breeding multi-environment trials, and for the understanding of genetic traits contributing to line x environment interactions.

Probabilistic cost benefit analysis of generation investment in a deregulated electricity market

A new methodology is proposed for the analysis of generation capacity investment in a deregulated market environment. This methodology proposes to make the investment appraisal using a probabilistic framework. The probabilistic production simulation (PPC) algorithm is used to compute the expected energy generated, taking into account system load variations and plant forced outage rates, while the Monte Carlo approach has been applied to model the electricity price variability seen in a realistic network. The model is able to capture the price and hence the profitability uncertainties for generator companies. Seasonal variation in the electricity prices and the system demand are independently modeled. The method is validated on IEEE RTS system, augmented with realistic market and plant data, by using it to compare the financial viability of several generator investments applying either conventional or directly connected generator (powerformer) technologies. The significance of the results is assessed using several financial risk measures.