Novel molecular sieve silica (MSS) membranes: characterisation and permeation of single-step and two-step sol-gel membranes

High quality MSS membranes were synthesised by a single-step and two-step catalysed hydrolyses employing tetraethylorthosilicate (TEOS), absolute ethanol (EtOH), I M nitric acid (HNO3) and distilled water (H2O). The Si-29 NMR results showed that the two-step xerogels consistently had more contribution of silanol groups (Q(3) and Q(2)) than the single-step xerogel. According to the fractal theory, high contribution of Q(2) and Q(3) species are responsible for the formation of weakly branched systems leading to low pore volume of microporous dimension. The transport of diffusing gases in these membranes is shown to be activated as the permeance increased with temperature. Albeit the permeance of He for both single-step and two-step membranes are very similar, the two-step membranes permselectivity (ideal separation factor) for He/CO2 (69-319) and He/CH4 (585-958) are one to two orders of magnitude higher than the single-step membranes results of 2-7 and 69, respectively. The two-step membranes have high activation energy for He and H-2 permeance, in excess of 16 kJ mol(-1). The mobility energy for He permeance is three to six-fold higher for the two-step than the single-step membranes. As the mobility energy is higher for small pores than large pores and coupled with the permselectivity results, the two-step catalysed hydrolysis sol-gel process resulted in the formation of pore sizes in the region of 3 Angstrom while the single-step process tended to produce slightly larger pores. (C) 2002 Elsevier Science B.V. All rights reserved.

Learning the dynamics of embedded clauses

Recent work by Siegelmann has shown that the computational power of recurrent neural networks matches that of Turing Machines. One important implication is that complex language classes (infinite languages with embedded clauses) can be represented in neural networks. Proofs are based on a fractal encoding of states to simulate the memory and operations of stacks. In the present work, it is shown that similar stack-like dynamics can be learned in recurrent neural networks from simple sequence prediction tasks. Two main types of network solutions are found and described qualitatively as dynamical systems: damped oscillation and entangled spiraling around fixed points. The potential and limitations of each solution type are established in terms of generalization on two different context-free languages. Both solution types constitute novel stack implementations - generally in line with Siegelmann's theoretical work - which supply insights into how embedded structures of languages can be handled in analog hardware.

A comprehensive insight into floc characteristics and their impact on compressibility and settleability of activated sludge

This paper presents a comprehensive study of sludge floc characteristics and their impact on compressibility and settleability of activated sludge in full scale wastewater treatment processes. The sludge flocs were characterised by morphological (floc size distribution, fractal dimension, filament index), physical (flocculating ability, viscosity, hydrophobicity and surface charge) and chemical (polymeric constituents and metal content) parameters. Compressibility and settleability were defined in terms of the sludge volume index (SVI) and zone settling velocity (ZSV). The floc morphological and physical properties had important influence on the sludge compressibility and settleability. Sludges containing large flocs and high quantities of filaments, corresponding to lower values of fractal dimension (D-f), demonstrated poor compressibility and settleability. Sludge flocs with high flocculating ability had lower SVI and higher ZSV, whereas high values of hydrophobicity, negative surface charge and viscosity of the sludge flocs correlated to high SVI and low ZSV. The quantity of the polymeric compounds protein. humic substances and carbohydrate in the sludge and the extracted extracellular polymeric substances (EPS) had significant positive correlations with SVI. The ZSV was quantitatively independent of the polymeric constituents. High concentrations of the extracted EPS were related to poor compressibility and settleability. The cationic ions Ca, Mg, Al and Fe in the sludge improved significantly the sludge compressibility and settleability. (C) 2003 Elsevier Science B.V. All rights reserved.

Impacts of structural characteristics on activated sludge floc stability

Activated sludge samples from seven full-scale plants were investigated in order to determine the relationship between floc structure and floc stability. Floc stability was determined by shear sensitivity and floc strength. Floc structure was considered in terms of two size scales, the micro- and macrostructure. The microstructure refers to the organization of the floc components, such as the individual microorganisms. The macrostructure refers to the overall floc. The floc macrostructure was characterized by filament index, sludge volume index, size, and fractal dimension. It had a significant impact on floc stability. Large and open floes with low fractal dimensions containing large number of filaments were more shear sensitive and had lower floc strength compared to small and dense floes. Fluorescent in situ hybridization analysis indicated that the organization of the bacterial cells might also have an effect on the floc stability. (C) 2003 Elsevier Ltd. All rights reserved.