Low-frequency electrical response to microbial induced sulfide precipitation

We investigated the sensitivity of low-frequency electrical measurements to microbe-induced metal sulfide precipitation. Three identical sand-packed monitoring columns were used; a geochemical column, an electrical column and a control column. In the first experiment, continuous upward flow of nutrients and metals in solution was established in each column. Cells of Desulfovibrio vulgaris (D. vulgaris) were injected into the center of the geochemical and electrical columns. Geochemical sampling and post-experiment destructive analysis showed that microbial induced sulfate reduction led to metal precipitation on bacteria cells, forming motile biominerals. Precipitation initially occurred in the injection zone, followed by chemotactic migration of D. vulgaris and ultimate accumulation around the nutrient source at the column base. Results from this experiment conducted with metals show (1) polarization anomalies, up to 14 mrad, develop at the bacteria injection and final accumulation areas, (2) the onset of polarization increase occurs concurrently with the onset of lactate consumption, (3) polarization profiles are similar to calculated profiles of the rate of lactate consumption, and (4) temporal changes in polarization and conduction correlate with a geometrical rearrangement of metal-coated bacterial cells. In a second experiment, the same biogeochemical conditions were established except that no metals were added to the flow solution. Polarization anomalies were absent when the experiment was replicated without metals in solution. We therefore attribute the polarization increase observed in the first experiment to a metal-fluid interfacial mechanism that develops as metal sulfides precipitate onto microbial cells and form biominerals. Temporal changes in polarization and conductivity reflect changes in (1) the amount of metal-fluid interfacial area, and (2) the amount of electronic conduction resulting from microbial growth, chemotactic movement and final coagulation. This polarization is correlated with the rate of microbial activity inferred from the lactate concentration gradient, probably via a common total metal surface area effect.

Intelligent demand response electric water heating to integrate wind power in Ireland

Dwindling fossil fuel resources and pressures to reduce greenhouse gas (GHG) emissions will result in a more diverse range of generation portfolios for future electricity systems. Irrespective of the portfolio mix the overarching requirement for all electricity suppliers and system operators is that supply instantaneously meets demand and that robust operating standards are maintained to ensure a consistent supply of high quality electricity to end-users. Therefore all electricity market participants will ultimately need to use a variety of tools to balance the power system. Thus the role of demand side management (DSM) with energy storage will be paramount to integrate future diverse generation portfolios. Electric water heating (EWH) has been studied previously, particularly at the domestic level to provide load control, peak shave and to benefit end-users financially with lower bills, particularly in vertically integrated monopolies. In this paper, a continuous Direct Load Control (DLC) EWH algorithm is applied in a liberalized market environment using actual historical electricity system and market data to examine the potential energy savings, cost reductions and electricity system operational improvements.

The continuous erythropoietin receptor activator (C.E.R.A.) corrects anemia at extended administration intervals in patients with chronic kidney disease not on dialysis:results of a phase II study

This study was designed to assess the potential of the continuous erythropoietin receptor activator (C.E.R.A.) to correct anemia at extended administration intervals in erythropoiesis-stimulating agent-naīve patients with chronic kidney disease (CKD) not on dialysis and to determine its optimal starting dose.

Blind Characterisation of Sensors with Second-Order Dynamic Response

Compensation for the dynamic response of a temperature sensor usually involves the estimation of its input on the basis of the measured output and model parameters. In the case of temperature measurement, the sensor dynamic response is strongly dependent on the measurement environment and fluid velocity. Estimation of time-varying sensor model parameters therefore requires continuous textit{in situ} identification. This can be achieved by employing two sensors with different dynamic properties, and exploiting structural redundancy to deduce the sensor models from the resulting data streams. Most existing approaches to this problem assume first-order sensor dynamics. In practice, however second-order models are more reflective of the dynamics of real temperature sensors, particularly when they are encased in a protective sheath. As such, this paper presents a novel difference equation approach to solving the blind identification problem for sensors with second-order models. The approach is based on estimating an auxiliary ARX model whose parameters are related to the desired sensor model parameters through a set of coupled non-linear algebraic equations. The ARX model can be estimated using conventional system identification techniques and the non-linear equations can be solved analytically to yield estimates of the sensor models. Simulation results are presented to demonstrate the efficiency of the proposed approach under various input and parameter conditions.