Quantum Critical Ferromagnetism in Ni x Pd1- x Alloys

We analyze the non-Fermi liquid behavior in the proximity of the quantum phase transition induced by the strong polarization of the electrons due to local magnetic moments in Ni _x Pd_1-x alloys. We use the renormalization group approach introduced by Hertz–Mi11is–Moriya (HMM) to estimate the temperature dependence of the electrical resistivity for the case of three dimensional itinerant ferromagnets. We study two different cases, namely, the clean and disordered ferromagnets, and argue that at the present time more experimental data are needed to decide if the HMM theory can accurately describe the physical properties in Ni _x Pd_1-x alloys.     

Textural Transitions in a Rotating Slab of 3He-A

The operation of a torsional oscillator for detection, of flow-driven textural transitions in, ³He-A is described. The detection technique, tracking the shift of the resonant frequency of the torsional oscillator, allows us to observe textural changes and the presence of vortices in zero magnetic field. It relies on. the anisotropic superfluid density of ³He-A and its change due to reorientation of the Î-texture caused by counterflow.     

Effects of flux enhancing polymer on the characteristics of sludge in membrane bioreactor process.

A newly developed membrane performance enhancer (MPE) was used to prevent membrane fouling in a membrane bioreactor (MBR) process. It transpired that 1,000 mg/l of MPE reduced polysaccharide levels from 41 mg/I to 21 mg/I on average under the experimental condition. Repeated experiments also confirmed that 50-1,000 mg/l of MPE could reduce membrane fouling significantly and increase the intervals between membrane cleanings. Depending on MPE dosages and experimental conditions, trans-membrane pressure (TMP) increase was suppressed for 20-30 days, while baseline TMP surged within a few days. In addition, MPE allowed MBR operation even at 50,000 mg/l of total solid and reduced permeate COD. However, no evidence of toxicity for sludge was found from respiratory works.     

Towards new indicators for the prediction of solid waste anaerobic digestion properties.

The biochemical composition can be seen as a good indicator of both the biodegradability and the methane potential of a given waste. The work presented here is an attempt to elaborate a typology of wastes and to compare it to the anaerobic degradation characteristics. The first data indicate that there is a link between the ligno-cellulosic content of the waste and the biodegradability. When dealing with application to anaerobic digestion processes, having a tool to predict the ability of the waste to be degraded could be of the greatest interest for preventing failures, estimating biogas production, methane content, or for the management of co-digestion processes.     

An integrated system to remote monitor and control anaerobic wastewater treatment plants through the internet.

The TELEMAC project brings new methodologies from the Information and Science Technologies field to the world of water treatment. TELEMAC offers an advanced remote management system which adapts to most of the anaerobic wastewater treatment plants that do not benefit from a local expert in wastewater treatment. The TELEMAC system takes advantage of new sensors to better monitor the process dynamics and to run automatic controllers that stabilise the treatment plant, meet the depollution requirements and provide a biogas quality suitable for cogeneration. If the automatic system detects a failure which cannot be solved automatically or locally by a technician, then an expert from the TELEMAC Control Centre is contacted via the internet and manages the problem.     

Current and future water issues in the Oldman River Basin of Alberta, Canada.

Long-term trends in alpine and prairie snow pack accumulation and melt are affecting streamflow within the Oldman River Basin in southern Alberta, Canada. Unchecked rural and urban development also has contributed to changes in water quality, including enhanced microbial populations and increased waterborne pathogen occurrence. In this study we look at changing environment within the Oldman River Basin and its impact on water quality and quantity. The cumulative effects include a decline in net water supplies, and declining quality resulting in increased risk of disease. Our data indicates that decreases in the rate of flow of water can result in sedimentation of bacterial contaminants within the water column. Water for ecosystems, urban consumption, recreation and distribution through irrigation is often drawn from waterholding facilities such as dams and weirs, and concern must be expressed over the potential for contaminate build-up and disproportionate potential of these structures to pose a risk to human and animal health. With disruption of natural flow rates for water resulting from environmental change such as global warming and/or human intervention, increased attention needs to be paid to use of best management practices to protect source water supplies.     

Implementation of adaptive critic-based neurocontrollers for turbogenerators in a multimachine power system

This paper presents the design and practical hardware implementation of optimal neurocontrollers that replace the conventional automatic voltage regulator (AVR) and the turbine governor of turbogenerators on multimachine power systems. The neurocontroller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP neurocontrollers' training and testing are implemented on the Innovative Integration M67 card consisting of the TMS320C6701 processor. The measured results show that the DHP neurocontrollers are robust and their performance does not degrade unlike the conventional controllers even when a power system stabilizer (PSS) is included, for changes in system operating conditions and configurations. This paper also shows that it is possible to design and implement optimal neurocontrollers for multiple turbogenerators in real time, without having to do continually online training of the neural networks, thus avoiding risks of instability.