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Reliable Methodologies (reliable + methodology)
Selected AbstractsEnzymatic hydrolysis of sugarcane bagasse for bioethanol production: determining optimal enzyme loading using neural networksJOURNAL OF CHEMICAL TECHNOLOGY & BIOTECHNOLOGY, Issue 7 2010Elmer Ccopa Rivera Abstract BACKGROUND: The efficient production of a fermentable hydrolyzate is an immensely important requirement in the utilization of lignocellulosic biomass as a feedstock in bioethanol production processes. The identification of the optimal enzyme loading is of particular importance to maximize the amount of glucose produced from lignocellulosic materials while maintaining low costs. This requirement can only be achieved by incorporating reliable methodologies to properly address the optimization problem. RESULTS: In this work, a data-driven technique based on artificial neural networks and design of experiments have been integrated in order to identify the optimal enzyme combination. The enzymatic hydrolysis of sugarcane bagasse was used as a case study. This technique was used to build up a model of the combined effects of cellulase (FPU/L) and ,-glucosidase (CBU/L) loads on glucose yield (%) after enzymatic hydrolysis. The optimal glucose yield, above 99%, was achieved with cellulase and ,-glucosidase concentrations in the ranges of 460.0 to 580.0 FPU L,1 (15.3,19.3 FPU g,1 bagasse) and 750.0 to 1140.0 CBU L,1 (2,38 CBU g,1 bagasse), respectively. CONCLUSIONS: The dynamic model developed can be used not only to the prediction of glucose concentration profiles for different enzymatic loadings, but also to obtain the optimum enzymes loading that leads to high glucose yield. It can promote both a successful hydrolysis process control and a more effective employment of enzymes. Copyright © 2010 Society of Chemical Industry [source] Fractal response of physiological signals to stress conditions, environmental changes, and neurodegenerative diseases,COMPLEXITY, Issue 5 2007Nicola Scafetta In the past two decades the biomedical community has witnessed several applications of nonlinear system theory to the analysis of biomedical time series and the development of nonlinear dynamic models. The development of this area of medicine can best be described as nonlinear and fractal physiology. These studies have been intended to develop more reliable methodologies for understanding how biological systems respond to peculiar altered conditions induced by internal stress, environment stress, and/or disease. Herein, we summarize the theory and some of our results showing the fractal dependency on different conditions of physiological signals such as inter-breath intervals, heart inter-beat intervals, and human stride intervals. © 2007 Wiley Periodicals, Inc. Complexity 12: 12,17, 2007 [source] Dynamic peroxide method for kLaO2 estimationJOURNAL OF CHEMICAL TECHNOLOGY & BIOTECHNOLOGY, Issue 8 2009Marcos Marcelino Abstract BACKGROUND: A reliable kLaO2 estimation methodology in bioreactors is a recurrent topic in the literature owing to the significance of this value, particularly in respirometric measurements. The most common methodologies for kLaO2 estimation consist of modeling the profile of dissolved oxygen (DO) obtained after a perturbation of the system aeration. Among them, the dynamic peroxide method (DPM), which consists of a small addition of peroxide hydrogen for a sudden increase in the DO level, is a promising methodology. RESULTS: This work studies the reliability of kLaO2 estimates using DPM. Different experiments were performed with sludge cultures enriched with heterotrophs, nitrifiers and polyphosphate accumulating organisms (PAO). The influence of several operational conditions (i.e. air flow, sludge concentration, H2O2 volume addition) on kLaO2 estimates was studied and the reliability of DPM was compared with the widely used reaeration methodology. An application of DPM in the assessment of oxygen surface transfer in a mechanically stirred bioreactor is described. CONCLUSION: DPM is a reliable methodology for kLaO2 estimation that can be successfully applied to heterotrophs, nitrifiers and PAO without observing any inhibitory effect ([H2O2] , 6 mg L,1). Copyright © 2009 Society of Chemical Industry [source] Prediction of experimentally unknown re distances of organic molecules from Dunning basis set extrapolations for ab initio post-HF calculationsJOURNAL OF PHYSICAL ORGANIC CHEMISTRY, Issue 3 2006Alexander Neugebauer Abstract An approach to estimate equilibrium re bond lengths of organic molecules which contain standard bonding situations for CC, CH, CO and CN distances from only one equation is presented. For this, optimizations of molecular geometries using correlated post-Hartree,Fock and density functional methods have been performed. A selection scheme was developed to determine the most reliable methodology for prediction of equilibrium re distances of covalent bonds from a set of investigated theoretical methods. Consequently, distances computed in the CCSD(T) procedure via exponential extrapolation from a consecutive set of Dunning cc-pVXZ basis sets by use of Eqn (2) are accurate up to ±,0.0005,Å in comparison to experimentally available re distances. Applications for predictions of the experimentally unknown re distances of methanol, methylamine and methylenimine are presented. Additionally the estimation of re distances of larger, chemically more interesting molecules is possible by lower order calculations (e.g. DFT B3LYP/cc-pVDZ) via linear correlation statistics using the results from our re reference model system via Eqn (3). Copyright © 2006 John Wiley & Sons, Ltd. 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