Mechanochemical synthesis of TiO2/NiFe2O4 magnetic catalysts for operation under RF field

Composite NiFe2O4–TiO2 magnetic catalysts were prepared by mechanochemical synthesis from a mixture of titania supported nickel ferrite nanoparticles and P25 titania (Evonic). The former provides fast and efficient heating under radiofrequency field, while the latter serves as an active catalyst or catalyst support. The highest heating rate was observed over a catalyst prepared for a milling time of 30 min. The catalytic activity was measured over the sulfated composite catalysts in the condensation of aniline and 3-phenylbutyric acid in a stirred tank reactor and in a continuous RF heated flow reactor in the 140–170 °C range. The product yield of 47% was obtained over the sulfated P25 titania catalyst in the flow reactor.

The influence of the degree of back-mixing on hydrogen production in an anaerobic fixed-bed reactor

This paper reports on results obtained from experiments carried out in an acidogenic anaerobic reactor aiming at the optimization of hydrogen production by altering the degree of back-mixing. It was hypothesized that there is an optimum operating point that maximizes the hydrogen yield. Experiments were performed in a packed-bed bioreactor by covering a broad range of recycle ratios (R) and the optimum point was obtained for an R value of 0.6. In this operating condition the reactor behaved as 8 continuous stirred-tank reactors in series and the maximum yield was 4.22 mol H-2 mol sucrose(-1). Such optimum point was estimated by deriving a polynomial function fitted to experimental data and it was obtained as the conjugation of three factors related to the various degrees of back-mixing applied to the reactor: mass transfer from the bulk liquid to the biocatalyst, liquid-to-gas mass transfer and the kinetic behavior of irreversible reactions in series. Copyright (C) 2012, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

Responsive brushes and gels as components of soft nanotechnology

Progress in the development of generic molecular devices based on responsive polymers is discussed. Characterisation of specially synthesised polyelectrolyte gels, "grafted from" brushes and triblock copolymers is reported. A Landolt pH-oscillator, based on bromate/ sulfite/ferrocyanide, with a room temperature period of 20 min and a range of 3.1 continuously stirred, constant volume, tank reactor was set-up on an optical microscope and the reaction pH and gel size monitored. The cyclic force generation of this system has been measured directly in a modified JKR experiment. The responsive nature of polyelectrolyte brushes, grown by surface initiated ATRP, have been characterised by scanning force microscopy, neutron reflectometry and single molecule force measurements. Triblock copolymers, based on hydrophobic end-blocks and either polyacid or polybase mid-block, have been used to produce polymer gels where the deformation of the molecules can be followed directly by SAXS and a correlation between molecular shape change and macroscopic deformation has been established. The three systems studied allow both the macroscopic and a molecular response to be investigated independently for the crosslinked gels and the brushes. The triblock copolymers demonstrate that the individual response of the polyelectrolyte molecules scale-up to give the macroscopic response of the system in an oscillating chemical reaction.

Hydrothermal syntheses of zeolite N from kaolin

Zeolite N, an EDI type framework structure with ideal chemical formula K12Al10Si10O40Cl2•5H2O, was produced from kaolin between 100oC and 200oC in a continuously stirred reactor using potassic and potassic+sodic liquors containing a range of anions. Reactions using liquors such as KOH, KOH + KX (where X = F, Cl, Br, I, NO3, NO2), K2X (where X=CO3), KOH + NaCl or NaOH + KCl were complete (>95% product) in less than two hours depending on the batch composition and temperature of reaction. With KOH and KCl in the reaction mixture and H2O/Al2O3~49, zeolite N was formed over a range of concentrations (1M < [KOH] < 18M) and reaction times (0.5h < t < 60h). At higher temperatures or higher KOH molarity, other potassic phases such as kalsilite or kaliophyllite formed. In general, temperature and KOH molarity defined the extent of zeolite N formation under these conditions. The introduction of sodic reagents to the starting mixture or use of one potassic reagent in the starting mixture reduced the stability field for zeolite N formation. Zeolite N was also formed using zeolite 4A as a source of Al and Si albeit for longer reaction times at a particular temperature when compared with kaolin as the source material.

Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

Biogas and bio-hydrogen: production and uses. A review

The first part of this essay aims at investigating the already available and promising technologies for the biogas and bio-hydrogen production from anaerobic digestion of different organic substrates. One strives to show all the peculiarities of this complicate process, such as continuity, number of stages, moisture, biomass preservation and rate of feeding. The main outcome of this part is the awareness of the huge amount of reactor configurations, each of which suitable for a few types of substrate and circumstance. Among the most remarkable results, one may consider first of all the wet continuous stirred tank reactors (CSTR), right to face the high waste production rate in urbanised and industrialised areas. Then, there is the up-flow anaerobic sludge blanket reactor (UASB), aimed at the biomass preservation in case of highly heterogeneous feedstock, which can also be treated in a wise co-digestion scheme. On the other hand, smaller and scattered rural realities can be served by either wet low-rate digesters for homogeneous agricultural by-products (e.g. fixed-dome) or the cheap dry batch reactors for lignocellulose waste and energy crops (e.g. hybrid batch-UASB). The biological and technical aspects raised during the first chapters are later supported with bibliographic research on the important and multifarious large-scale applications the products of the anaerobic digestion may have. After the upgrading techniques, particular care was devoted to their importance as biofuels, highlighting a further and more flexible solution consisting in the reforming to syngas. Then, one shows the electricity generation and the associated heat conversion, stressing on the high potential of fuel cells (FC) as electricity converters. Last but not least, both the use as vehicle fuel and the injection into the gas pipes are considered as promising applications. The consideration of the still important issues of the bio-hydrogen management (e.g. storage and delivery) may lead to the conclusion that it would be far more challenging to implement than bio-methane, which can potentially “inherit” the assets of the similar fossil natural gas. Thanks to the gathered knowledge, one devotes a chapter to the energetic and financial study of a hybrid power system supplied by biogas and made of different pieces of equipment (natural gas thermocatalitic unit, molten carbonate fuel cell and combined-cycle gas turbine structure). A parallel analysis on a bio-methane-fed CCGT system is carried out in order to compare the two solutions. Both studies show that the apparent inconvenience of the hybrid system actually emphasises the importance of extending the computations to a broader reality, i.e. the upstream processes for the biofuel production and the environmental/social drawbacks due to fossil-derived emissions. Thanks to this “boundary widening”, one can realise the hidden benefits of the hybrid over the CCGT system.

Ferrous iron oxidation by foam immobilized Acidithiobacillus ferrooxidans: Experiments and modeling

Ferrous iron bio-oxidation by Acidithiobacillus ferrooxidans immobilized on polyurethane foam was investigated. Cells were immobilized on foams by placing them in a growth environment and fully bacterially activated polyurethane foams (BAPUFs) were prepared by serial subculturing in batches with partially bacterially activated foam (pBAPUFs). The dependence of foam density on cell immobilization process, the effect of pH and BAPUF loading on ferrous oxidation were studied to choose operating parameters for continuous operations. With an objective to have high cell densities both in foam and the liquid phase, pretreated foams of density 50 kg/m3 as cell support and ferrous oxidation at pH 1.5 to moderate the ferric precipitation were preferred. A novel basket-type bioreactor for continuous ferrous iron oxidation, which features a multiple effect of stirred tank in combination with recirculation, was designed and operated. The results were compared with that of a free cell and a sheet-type foam immobilized reactors. A fivefold increase in ferric iron productivity at 33.02 g/h/L of free volume in foam was achieved using basket-type bioreactor when compared to a free cell continuous system. A mathematical model for ferrous iron oxidation by Acidithiobacillus ferrooxidans cells immobilized on polyurethane foam was developed with cell growth in foam accounted by an effectiveness factor. The basic parameters of simulation were estimated using the experimental data on free cell growth as well as from cell attachment to foam under nongrowing conditions. The model predicted the phase of both oxidation of ferrous in shake flasks by pBAPUFs as well as by fully activated BAPUFs for different cell loadings in foam. Model for stirred tank basket bioreactor predicted within 5% both transient and steady state of the experiments closely for the simulated dilution rates. Bio-oxidation at high Fe2+ concentrations were simulated with experiments when substrate and product inhibition coefficients were factored into cell growth kinetics.

Mass Transfer Coefficients in Mixer-Settlers

Mass transfer coefficients have been determined for transfer into a highly viscous phase in a stirred tank involving high Schmidt numbers. The results have been used to compute mass transfer coefficients in the extraction of free fatty acids from oils using alcohol and show good agreement with experimental results

Evolutionary Algorithm for the Determination of Optimal Mode of Bioreactor Operation

Stirred tank bioreactors, employed in the production of a variety of biologically active chemicals, are often operated in batch, fed-batch, and continuous modes of operation. The optimal design of bioreactor is dependent on the kinetics of the biological process, as well as the performance criteria (yield, productivity, etc.) under consideration. In this paper, a general framework is proposed for addressing the two key issues related to the optimal design of a bioreactor, namely, (i) choice of the best operating mode and (ii) the corresponding flow rate trajectories. The optimal bioreactor design problem is formulated with initial conditions and inlet and outlet flow rate trajectories as decision variables to maximize more than one performance criteria (yield, productivity, etc.) as objective functions. A computational methodology based on genetic algorithm approach is developed to solve this challenging multiobjective optimization problem with multiple decision variables. The applicability of the algorithm is illustrated by solving two challenging problems from the bioreactor optimization literature.

Remoção de contaminantes nitrogenados e sulfurados de cargas modelo de óeo diesel: estudo do adsorvente

A preocupação com o meio ambiente deve fazer parte da rotina de uma indústria de petróleo e derivados. A presença de compostos heterocíclicos em correntes de diesel motiva a sua remoção, pois além do aspecto ambiental, esses compostos podem interferir no desempenho de processos de hidrotratamento (HDT). A adsorção é uma das opções para minimizar esse problema. Nesse contexto, o objetivo deste trabalho foi estudar o adsorvente comercial mais adequado através de um estudo cinético realizado em tanque agitado e suportado por alguns ensaios de equilíbrio. Foi dada ênfase preferencial à remoção de compostos nitrogenados, sendo avaliada a remoção de compostos sulfurados nos adsorventes mais promissores. Foram selecionados, como adsorventes comerciais, as argilas bentoníticas TCO 626G (Süd-Chemie) e F-24 (Engelhard), a -alumina CCI (Süd-Chemie), a sílica-alumina SIRAL 40 (Sasol) e a zeólita Y ultraestável USY (cedida pelo CENPES-Petrobras). Na composição do óleo diesel modelo encontra-se quinolina, carbazol e benzotiofeno, com n-hexadecano como diluente. A caracterização destes adsorventes incluiu análise química por fluorescência de raios X, análise estrutural por difração de raios X, análise textural por fisissorção de N2, análises de acidez por termodessorção de amônia (TPD de NH3) e por espectroscopia no infravermelho de piridina adsorvida. Os estudos cinéticos mostraram que a quinolina é adsorvida rapidamente, principalmente na zeolita USY, que apresentou a maior capacidade adsortiva. Observou-se que a ordem decrescente de melhor adsorvente seguiu a mesma ordem da quantidade de sítios ácidos encontrada por TPD-NH3. Nos estudos cinéticos com carbazol, a zeólita USY também foi o melhor adsorvente. Não houve acordo com relação a acidez, o que se esperava uma vez que se trata de um composto nitrogenado não básico. A presença de carbazol e quinolina na mesma solução, não alterou o desempenho da cinética de remoção de ambos, indicando que provavelmente não estão competindo pelos mesmos sítios de adsorção. Quando foi introduzido um composto sulfurado no sistema, a zeólita se manteve como o melhor adsorvente, a quinolina continuou sendo eficazmente removida, mas a remoção de carbazol sofreu alguma interferência que pode indicar a competição das moléculas pelo mesmo sítio. Nos estudos com carga real de óleo diesel, ao contrário do observado para as cargas modelo, a TCO 626G mostrou-se mais efetiva na remoção de compostos heterocíclicos que a USY. O modelo cinético proposto ajustou adequadamente as curvas e as isotermas de adsorção para quinolina e carbazol, relativas a USY e a TCO 626G, foram melhor ajustadas pelo modelo de Freundlich

Laboratory Simulation Of Chemical Processes Induced By Estuarine Mixing - The Behavior Of Iron And Phosphate In Estuaries

A series of well stirred tank reactors has been shown to provide an adaptable laboratory analogue of a one-dimensional estuarine mixing profile which can be applied dynamically to the study of the chemistry of estuarine mixing. Simulations of the behaviour of iron and phosphate in the low salinity region of an estuary have been achieved with this system. The well documented general features of iron removal, involving rapid aggregation of river-borne colloids, were reproduced. Phosphate removal is attributable in part to the coagulation process, although specific adsorption of phosphate by colloids also appears to be significant.

Development of human central nervous system 3D in vitro models for preclinical research

Neurological disorders are a major concern in modern societies, with increasing prevalence mainly related with the higher life expectancy. Most of the current available therapeutic options can only control and ameliorate the patients’ symptoms, often be-coming refractory over time. Therapeutic breakthroughs and advances have been hampered by the lack of accurate central nervous system (CNS) models. The develop-ment of these models allows the study of the disease onset/progression mechanisms and the preclinical evaluation of novel therapeutics. This has traditionally relied on genetically engineered animal models that often diverge considerably from the human phenotype (developmentally, anatomically and physiologically) and 2D in vitro cell models, which fail to recapitulate the characteristics of the target tissue (cell-cell and cell-matrix interactions, cell polarity). The in vitro recapitulation of CNS phenotypic and functional features requires the implementation of advanced culture strategies that enable to mimic the in vivo struc-tural and molecular complexity. Models based on differentiation of human neural stem cells (hNSC) in 3D cultures have great potential as complementary tools in preclinical research, bridging the gap between human clinical studies and animal models. This thesis aimed at the development of novel human 3D in vitro CNS models by integrat-ing agitation-based culture systems and a wide array of characterization tools. Neural differentiation of hNSC as 3D neurospheres was explored in Chapter 2. Here, it was demonstrated that human midbrain-derived neural progenitor cells from fetal origin (hmNPC) can generate complex tissue-like structures containing functional dopaminergic neurons, as well as astrocytes and oligodendrocytes. Chapter 3 focused on the development of cellular characterization assays for cell aggregates based on light-sheet fluorescence imaging systems, which resulted in increased spatial resolu-tion both for fixed samples or live imaging. The applicability of the developed human 3D cell model for preclinical research was explored in Chapter 4, evaluating the poten-tial of a viral vector candidate for gene therapy. The efficacy and safety of helper-dependent CAV-2 (hd-CAV-2) for gene delivery in human neurons was evaluated, demonstrating increased neuronal tropism, efficient transgene expression and minimal toxicity. The potential of human 3D in vitro CNS models to mimic brain functions was further addressed in Chapter 5. Exploring the use of 13C-labeled substrates and Nucle-ar Magnetic Resonance (NMR) spectroscopy tools, neural metabolic signatures were evaluated showing lineage-specific metabolic specialization and establishment of neu-ron-astrocytic shuttles upon differentiation. Chapter 6 focused on transferring the knowledge and strategies described in the previous chapters for the implementation of a scalable and robust process for the 3D differentiation of hNSC derived from human induced pluripotent stem cells (hiPSC). Here, software-controlled perfusion stirred-tank bioreactors were used as technological system to sustain cell aggregation and dif-ferentiation. The work developed in this thesis provides practical and versatile new in vitro ap-proaches to model the human brain. Furthermore, the culture strategies described herein can be further extended to other sources of neural phenotypes, including pa-tient-derived hiPSC. The combination of this 3D culture strategy with the implemented characterization methods represents a powerful complementary tool applicable in the drug discovery, toxicology and disease modeling.

Combined bias and outlier identification in dynamic data reconciliation

Measured process data normally contain inaccuracies because the measurements are obtained using imperfect instruments. As well as random errors one can expect systematic bias caused by miscalibrated instruments or outliers caused by process peaks such as sudden power fluctuations. Data reconciliation is the adjustment of a set of process data based on a model of the process so that the derived estimates conform to natural laws. In this paper, techniques for the detection and identification of both systematic bias and outliers in dynamic process data are presented. A novel technique for the detection and identification of systematic bias is formulated and presented. The problem of detection, identification and elimination of outliers is also treated using a modified version of a previously available clustering technique. These techniques are also combined to provide a global dynamic data reconciliation (DDR) strategy. The algorithms presented are tested in isolation and in combination using dynamic simulations of two continuous stirred tank reactors (CSTR).

Effects of culture conditions on the production of inulinase by Kluyveromyces marxianus

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Modeling and simulation of cephalosporin C production in a fed-batch tower-type bioreactor

Immobilized cell utilization in tower-type bioreactor is one of the main alternatives being studied to improve the industrial bioprocess. Other alternatives for the production of beta -lactam antibiotics, such as a cephalosporin C fed-batch p recess in an aerated stirred-tank bioreactor with free cells of Cepha-losporium acremonium or a tower-type bioreactor with immobilized cells of this fungus, have proven to be more efficient than the batch profess. In the fed-batch process, it is possible to minimize the catabolite repression exerted by the rapidly utilization of carbon sources (such as glucose) in the synthesis of antibiotics by utilizing a suitable flow rate of supplementary medium. In this study, several runs for cephalosporin C production, each lasting 200 h, were conducted in a fed-batch tower-type bioreactor using different hydrolyzed sucrose concentrations, For this study's model, modifications were introduced to take into account the influence of supplementary medium flow rate. The balance equations considered the effect of oxygen limitation inside the bioparticles. In the Monod-type rate equations, eel concentrations, substrate concentrations, and dissolved oxygen were included as reactants affecting the bioreaction rate. The set of differential equations was solved by the numerical method, and the values of the parameters were estimated by the classic nonlinear regression method following Marquardt's procedure with a 95% confidence interval. The simulation results showed that the proposed model fit well with the experimental data,and based on the experimental data and the mathematical model an optimal mass flow rate to maximize the bioprocess productivity could be proposed.