Microalgae fractionation using steam explosion, dynamic and tangential cross-flow membrane filtration.

In this study, the microalga Nannochloropsis gaditana was subjected to acid catalysed steam explosion treatment and the resulting exploded material was subsequently fractionated to separate the different fractions (lipids, sugars and solids). Conventional and vibrational membrane setups were used with several polymeric commercial membranes. Two different routes were followed: 1) filtration+lipid solvent extraction and 2) lipid solvent extraction+filtration. Route 1 revealed to be much better since the used membrane for filtration was able to permeate the sugar aqueous phase and retained the fraction containing lipids; after this, an extraction required a much lower amount of solvent and a better recovering yield. Filtration allowed complete lipid rejection. Dynamic filtration improved permeability compared to the tangential cross-flow filtration. Best membrane performance was achieved using a 5000Da membrane with the dynamic system, obtaining a permeability of 6L/h/m2/bar.

Effect of pre-treatments on the production of biofuels from Phaeodactylum tricornutum.

Several characteristics make Phaeodactylum tricornutum potential candidate for biofuels production such as methane and biodiesel. For this reason, some alternatives are evaluated in this manuscript to improve the conversion of this microalgae into methane. One of these alternatives is the addition of sewage sludge to Phaeodactylum tricornutum for anaerobic co-digestion. Although the co-digestion resulted in lack of synergy, the absence of inhibition indicated that both substrates could be co-digested under certain circumstances, for example if microalgae are cultivated for wastewater treatment purposes. The extraction of lipids using organic solvents has been evaluated for biodiesel production but also as a pre-treatment for anaerobic digestion. The results revealed that the type of solvent influences lipid and biodiesel yields. The high polarity of the mixture methanol/hexane increased the lipid and the biodiesel yields from 10 ± 1 to 53 ± 2 gLipids/100 gVS and from 7 ± 1 to 11 ± 1 gBiodiesel/100 gVS compared with hexane. However, none of these solvents affected the composition of biodiesel. Regarding the methane production after the extraction, it yielded 257 ± 8 and 180 ± 6 mLCH4/gVS from lipid-extracted P. tricornutum using hexane and methanol/hexane respectively. The methane production from the raw microalga was 258 ± 5 mLCH4/gVS in the same experiment. The difference in methane production, mainly after the extraction with methanol/hexane, was a consequence of the changes in the composition of the microalgae after extraction. The extraction did not influence the biodegradability. The ultrasonic pre-treatment prior anaerobic digestion completely disrupted the microalgae cells, but the solubilisation of the organic fraction was scarce (<9.5%). The methane production from pre-treated samples was barely 10-11% higher than the obtained from non pre-treated samples, indicating that the refractory nature of the organic fraction in P. tricornutum is the main obstacle for the methane production.

Biorefinery concept in a microalgae pilot plant. Culturing, dynamic filtration and steam explosion fractionation.

A 53 m(3) microalgae pilot plant with semi-closed photobioreactors has been operated in batch mode at Tarragona, where proper climatic conditions exist. Operations carried-out were microalgae culturing, concentration, cell disruption and lipid extraction. Culturing was performed with and without CO2 fertilization. pH was used to control CO2 dosing. With CO2 fertilization best microalgae concentrations were obtained. Productivities up to 19.9 g/m(2)/day were reached. Although sedimentation and centrifugation was performed, studies focused on dynamic membrane filtration as improved technique compared with conventional one. Significant fouling reduction was obtained by using a vibrational membrane setup. Cell disruption including product pre-extraction was performed by using steam explosion at moderate conditions, as novel technique for this application. Lipid extraction was performed by using conventional techniques with and without exploded material. The amount of lipids obtained from exploded material was higher than with non-exploded material, which shows that steam explosion provides a clear enhancement.

Kinematic Bézier Maps.

The kinematics of a robot with many degrees of freedom is a very complex function. Learning this function for a large workspace with a good precision requires a huge number of training samples, i.e., robot movements. In this paper, we introduce the Kinematic Bézier Map (KB-Map), a parameterizable model without the generality of other systems but whose structure readily incorporates some of the geometric constraints of a kinematic function. In this way, the number of training samples required is drastically reduced. Moreover, the simplicity of the model reduces learning to solving a linear least squares problem. Systematic experiments have been carried out showing the excellent interpolation and extrapolation capabilities of KB-Maps and their relatively low sensitivity to noise.

Architecture-independent approximation of functions.

We show that minimizing the expected error of a feedforward network over a distribution of weights results in an approximation that tends to be independent of network size as the number of hidden units grows. This minimization can be easily performed, and the complexity of the resulting function implemented by the network is regulated by the variance of the weight distribution. For a fixed variance, there is a number of hidden units above which either the implemented function does not change or the change is slight and tends to zero as the size of the network grows. In sum, the control of the complexity depends on only the variance, not the architecture, provided it is large enough.

Neural cost functions and search strategies for the generation of block designs: an experimental evaluation.

A constraint satisfaction problem, namely the generation of Balanced Incomplete Block Designs (v, b, r, kappa, lambda)-BIBDs, is cast in terms of function optimization. A family of cost functions that both suit the problem and admit a neural implementation is defined. An experimental comparison spanning this repertoire of cost functions and three neural relaxation strategies (Down-Hill search, Simulated Annealing and a new Parallel Mean Search procedure), as applied to all BIBDs of up to 1000 entries, has been undertaken. The experiments were performed on a Connection Machine CM-200 and their analysis required a careful study of performance measures. The simplest cost function stood out as the best one for the three strategies. Parallel Mean Search, with several processors searching cooperatively in parallel, could solve a larger number of problems than the same number of processors working independently, but Simulated Annealing yielded overall the best results. Other conclusions, as detailed in the paper, could be drawn from the comparison, BIBDs remaining a challenging problem for neural optimization algorithms.

Self-calibration of a space robot.

We present a neural-network method to recalibrate automatically a commercial robot after undergoing wear or damage, which works on top of the nominal inverse kinematics embedded in its controller. Our starting point has been the work of Ritter et al. (1989, 1992) on the use of extended self-organizing maps to learn the whole inverse kinematics mapping from scratch. Besides adapting their approach to learning only the deviations from the nominal kinematics, we have introduced several modifications to improve the cooperation between neurons. These modifications not only speed up learning by two orders of magnitude, but also produce some desirable side effects, like parameter stability. After extensive experimentation through simulation, the recalibration system has been installed in the REIS robot included in the space-station mock-up at Daimler-Benz Aerospace. Tests performed in this set-up have been constrained by the need to preserve robot integrity, but the results have been concordant with those predicted through simulation.

On-line learning with minimal degradation in feedforward networks.

Dealing with nonstationary processes requires quick adaptation while at the same time avoiding catastrophic forgetting. A neural learning technique that satisfies these requirements, without sacrificing the benefits of distributed representations, is presented. It relies on a formalization of the problem as the minimization of the error over the previously learned input-output patterns, subject to the constraint of perfect encoding of the new pattern. Then this constrained optimization problem is transformed into an unconstrained one with hidden-unit activations as variables. This new formulation leads to an algorithm for solving the problem, which we call learning with minimal degradation (LMD). Some experimental comparisons of the performance of LMD with backpropagation are provided which, besides showing the advantages of using LMD, reveal the dependence of forgetting on the learning rate in backpropagation. We also explain why overtraining affects forgetting and fault tolerance, which are seen as related problems.