Optimization of lipase production from yeasts strains isolated from olive mil wastewater

Dissertação submetida à Universidade de Lisboa, Faculdade de Ciências para a obtenção do Grau de Mestre em Microbiologia Aplicada.

Probabilistic design optimization of horizontal axis wind turbine rotors

Considerable interest in renewable energy has increased in recent years due to the concerns raised over the environmental impact of conventional energy sources and their price volatility. In particular, wind power has enjoyed a dramatic global growth in installed capacity over the past few decades. Nowadays, the advancement of wind turbine industry represents a challenge for several engineering areas, including materials science, computer science, aerodynamics, analytical design and analysis methods, testing and monitoring, and power electronics. In particular, the technological improvement of wind turbines is currently tied to the use of advanced design methodologies, allowing the designers to develop new and more efficient design concepts. Integrating mathematical optimization techniques into the multidisciplinary design of wind turbines constitutes a promising way to enhance the profitability of these devices. In the literature, wind turbine design optimization is typically performed deterministically. Deterministic optimizations do not consider any degree of randomness affecting the inputs of the system under consideration, and result, therefore, in an unique set of outputs. However, given the stochastic nature of the wind and the uncertainties associated, for instance, with wind turbine operating conditions or geometric tolerances, deterministically optimized designs may be inefficient. Therefore, one of the ways to further improve the design of modern wind turbines is to take into account the aforementioned sources of uncertainty in the optimization process, achieving robust configurations with minimal performance sensitivity to factors causing variability. The research work presented in this thesis deals with the development of a novel integrated multidisciplinary design framework for the robust aeroservoelastic design optimization of multi-megawatt horizontal axis wind turbine (HAWT) rotors, accounting for the stochastic variability related to the input variables. The design system is based on a multidisciplinary analysis module integrating several simulations tools needed to characterize the aeroservoelastic behavior of wind turbines, and determine their economical performance by means of the levelized cost of energy (LCOE). The reported design framework is portable and modular in that any of its analysis modules can be replaced with counterparts of user-selected fidelity. The presented technology is applied to the design of a 5-MW HAWT rotor to be used at sites of wind power density class from 3 to 7, where the mean wind speed at 50 m above the ground ranges from 6.4 to 11.9 m/s. Assuming the mean wind speed to vary stochastically in such range, the rotor design is optimized by minimizing the mean and standard deviation of the LCOE. Airfoil shapes, spanwise distributions of blade chord and twist, internal structural layup and rotor speed are optimized concurrently, subject to an extensive set of structural and aeroelastic constraints. The effectiveness of the multidisciplinary and robust design framework is demonstrated by showing that the probabilistically designed turbine achieves more favorable probabilistic performance than those of the initial baseline turbine and a turbine designed deterministically.

Analysis of N-nitrosames in water using micellar electrokinetic chromatography (MEKC) and development of online and offline methods of extraction and enrichment of neutral polar analytes

Evaluation of the quality of the environment is essential for human wellness as pollutants in trace amounts can cause serious health problem. Nitrosamines are a group of compounds that are considered potential carcinogens and can be found in drinking water (as disinfection byproducts), foods, beverages and cosmetics. To monitor the level of these compounds to minimize daily intakes, fast and reliable analytical techniques are required. As these compounds are relatively highly polar, extraction and enrichment from environmental samples (aqueous) are challenging. Also, the trend of analytical techniques toward the reduction of sample size and minimization of organic solvent use demands new methods of analysis. In light of fulfilling these requirements, a new method of online preconcentration tailored to an electrokinetic chromatography is introduced. In this method, electroosmotic flow (EOF) was suppressed to increase the interaction time between analyte and micellar phase, therefore the only force to mobilize the neutral analytes is the interaction of analyte with moving micelles. In absence of EOF, polarity of applied potential was switched (negative or positive) to force (anionic or cationic) micelles to move toward the detector. To avoid the excessive band broadening due to longer analysis time caused by slow moving micelles, auxiliary pressure was introduced to boost the micelle movement toward the detector using an in house designed and built apparatus. Applying the external auxiliary pressure significantly reduced the analysis times without compromising separation efficiency. Parameters, such as type of surfactants, composition of background electrolyte (BGE), type of capillary, matrix effect, organic modifiers, etc., were evaluated in optimization of the method. The enrichment factors for targeted analytes were impressive, particularly; cationic surfactants were shown to be suitable for analysis of nitrosamines due to their ability to act as hydrogen bond donors. Ammonium perfluorooctanoate (APFO) also showed remarkable results in term of peak shapes and number of theoretical plates. It was shown that the separation results were best when a high conductivity sample was paired with a BGE of lower conductivity. Using higher surfactant concentrations (up to 200 mM SDS) than usual (50 mM SDS) for micellar electrokinetic chromatography (MEKC) improved the sweeping. A new method for micro-extraction and enrichment of highly polar neutral analytes (N-Nitrosamines in particular) based on three-phase drop micro-extraction was introduced and its performance studied. In this method, a new device using some easy-to-find components was fabricated and its operation and application demonstrated. Compared to conventional extraction methods (liquid-liquid extraction), consumption of organic solvents and operation times were significantly lower.

Optimization of aeroponic technology for future integration in quality potato seed production systems in tropical environments

Virus and soil borne pathogens negatively impact on the production of potatoes in tropical highland and sub-tropical environments, limiting supply of an increasingly popular and important vegetable in these regions. It is common for latent disease infected seed tubers or field grown cuttings to be used as potato planting material. We utilised an International Potato Centre technique, using aeroponic technology, to produce low cost mini-tubers in tropical areas. The system has been optimised for increased effectiveness in tropical areas. High numbers of seed tubers of cultivar Sebago (630) and Nicola per m2 (>900) were obtained in the first generation, and the system is capable of producing five crops of standard cultivars in every two years. Initial results indicate that quality seed could be produced by nurseries and farmers, therefore contributing to the minimisation of soil borne diseases in an integrated management plan. This technology reduces seed production costs, benefiting seed and potato growers. © ISHS.

STATISTICAL METHODS FOR ADVANCED ECONOMETRIC MODELS WITH APPLICATIONS TO VEHICLE HOLDING AND SPEED QUANTILES DISTRIBUTION

This dissertation proposes statistical methods to formulate, estimate and apply complex transportation models. Two main problems are part of the analyses conducted and presented in this dissertation. The first method solves an econometric problem and is concerned with the joint estimation of models that contain both discrete and continuous decision variables. The use of ordered models along with a regression is proposed and their effectiveness is evaluated with respect to unordered models. Procedure to calculate and optimize the log-likelihood functions of both discrete-continuous approaches are derived, and difficulties associated with the estimation of unordered models explained. Numerical approximation methods based on the Genz algortithm are implemented in order to solve the multidimensional integral associated with the unordered modeling structure. The problems deriving from the lack of smoothness of the probit model around the maximum of the log-likelihood function, which makes the optimization and the calculation of standard deviations very difficult, are carefully analyzed. A methodology to perform out-of-sample validation in the context of a joint model is proposed. Comprehensive numerical experiments have been conducted on both simulated and real data. In particular, the discrete-continuous models are estimated and applied to vehicle ownership and use models on data extracted from the 2009 National Household Travel Survey. The second part of this work offers a comprehensive statistical analysis of free-flow speed distribution; the method is applied to data collected on a sample of roads in Italy. A linear mixed model that includes speed quantiles in its predictors is estimated. Results show that there is no road effect in the analysis of free-flow speeds, which is particularly important for model transferability. A very general framework to predict random effects with few observations and incomplete access to model covariates is formulated and applied to predict the distribution of free-flow speed quantiles. The speed distribution of most road sections is successfully predicted; jack-knife estimates are calculated and used to explain why some sections are poorly predicted. Eventually, this work contributes to the literature in transportation modeling by proposing econometric model formulations for discrete-continuous variables, more efficient methods for the calculation of multivariate normal probabilities, and random effects models for free-flow speed estimation that takes into account the survey design. All methods are rigorously validated on both real and simulated data.

Spectral graph theory with applications to quantum adiabatic optimization

In this dissertation I draw a connection between quantum adiabatic optimization, spectral graph theory, heat-diffusion, and sub-stochastic processes through the operators that govern these processes and their associated spectra. In particular, we study Hamiltonians which have recently become known as ``stoquastic'' or, equivalently, the generators of sub-stochastic processes. The operators corresponding to these Hamiltonians are of interest in all of the settings mentioned above. I predominantly explore the connection between the spectral gap of an operator, or the difference between the two lowest energies of that operator, and certain equilibrium behavior. In the context of adiabatic optimization, this corresponds to the likelihood of solving the optimization problem of interest. I will provide an instance of an optimization problem that is easy to solve classically, but leaves open the possibility to being difficult adiabatically. Aside from this concrete example, the work in this dissertation is predominantly mathematical and we focus on bounding the spectral gap. Our primary tool for doing this is spectral graph theory, which provides the most natural approach to this task by simply considering Dirichlet eigenvalues of subgraphs of host graphs. I will derive tight bounds for the gap of one-dimensional, hypercube, and general convex subgraphs. The techniques used will also adapt methods recently used by Andrews and Clutterbuck to prove the long-standing ``Fundamental Gap Conjecture''.

Numerical methods for solar tomography in the STEREO era

In this thesis, we propose several advances in the numerical and computational algorithms that are used to determine tomographic estimates of physical parameters in the solar corona. We focus on methods for both global dynamic estimation of the coronal electron density and estimation of local transient phenomena, such as coronal mass ejections, from empirical observations acquired by instruments onboard the STEREO spacecraft. We present a first look at tomographic reconstructions of the solar corona from multiple points-of-view, which motivates the developments in this thesis. In particular, we propose a method for linear equality constrained state estimation that leads toward more physical global dynamic solar tomography estimates. We also present a formulation of the local static estimation problem, i.e., the tomographic estimation of local events and structures like coronal mass ejections, that couples the tomographic imaging problem to a phase field based level set method. This formulation will render feasible the 3D tomography of coronal mass ejections from limited observations. Finally, we develop a scalable algorithm for ray tracing dense meshes, which allows efficient computation of many of the tomographic projection matrices needed for the applications in this thesis.

Crocin and β-Carotene bleaching assays as analytical tools in the optimization of the extraction of hydrophilic and lipophilic antioxidants from tomato

Tomato (Lycopersicon esculentum Mill.) is the second most important vegetable crop worldwide and a rich source of hydrophilic (H) and lipophilic (L) antioxidants. The H fraction is constituted mainly by ascorbic acid and soluble phenolic compounds, while the L fraction contains carotenoids (mostly lycopene), tocopherols, sterols and lipophilic phenolics [1,2]. To obtain these antioxidants it is necessary to follow appropriate extraction methods and processing conditions. In this regard, this study aimed at determining the optimal extraction conditions for H and L antioxidants from a tomato surplus. A 5-level full factorial design with 4 factors (extraction time (I, 0-20 min), temperature (T, 60-180 •c), ethanol percentage (Et, 0-100%) and solid/liquid ratio (S/L, 5-45 g!L)) was implemented and the response surface methodology used for analysis. Extractions were carried out in a Biotage Initiator Microwave apparatus. The concentration-time response methods of crocin and P-carotene bleaching were applied (using 96-well microplates), since they are suitable in vitro assays to evaluate the antioxidant activity of H and L matrices, respectively [3]. Measurements were carried out at intervals of 3, 5 and 10 min (initiation, propagation and asymptotic phases), during a time frame of 200 min. The parameters Pm (maximum protected substrate) and V m (amount of protected substrate per g of extract) and the so called IC50 were used to quantify the response. The optimum extraction conditions were as follows: r~2.25 min, 7'=149.2 •c, Et=99.1 %and SIL=l5.0 giL for H antioxidants; and t=l5.4 min, 7'=60.0 •c, Et=33.0% and S/L~l5.0 g/L for L antioxidants. The proposed model was validated based on the high values of the adjusted coefficient of determination (R2.wi>0.91) and on the non-siguificant differences between predicted and experimental values. It was also found that the antioxidant capacity of the H fraction was much higher than the L one.

Ultrasound and microwave assisted extraction of ergosterol from Agaricus bisporus L.: optimization through response surface methodology

There is scientific evidence demonstrating the benefits of mushrooms ingestion due to their richness in bioactive compounds such as mycosterols, in particular ergosterol [I]. Agaricus bisporus L. is the most consumed mushroom worldwide presenting 90% of ergosterol in its sterol fraction [2]. Thus, it is an interesting matrix to obtain ergosterol, a molecule with a high commercial value. According to literature, ergosterol concentration can vary between 3 to 9 mg per g of dried mushroom. Nowadays, traditional methods such as maceration and Soxhlet extraction are being replaced by emerging methodologies such as ultrasound (UAE) and microwave assisted extraction (MAE) in order to decrease the used solvent amount, extraction time and, of course, increasing the extraction yield [2]. In the present work, A. bisporus was extracted varying several parameters relevant to UAE and MAE: UAE: solvent type (hexane and ethanol), ultrasound amplitude (50 - 100 %) and sonication time (5 min-15 min); MAE: solvent was fixed as ethanol, time (0-20 min), temperature (60-210 •c) and solid-liquid ratio (1-20 g!L). Moreover, in order to decrease the process complexity, the pertinence to apply a saponification step was evaluated. Response surface methodology was applied to generate mathematical models which allow maximizing and optimizing the response variables that influence the extraction of ergosterol. Concerning the UAE, ethanol proved to be the best solvent to achieve higher levels of ergosterol (671.5 ± 0.5 mg/100 g dw, at 75% amplitude for 15 min), once hexane was only able to extract 152.2 ± 0.2 mg/100 g dw, in the same conditions. Nevertheless, the hexane extract showed higher purity (11%) when compared with the ethanol counterpart ( 4% ). Furthermore, in the case of the ethanolic extract, the saponification step increased its purity to 21%, while for the hexane extract the purity was similar; in fact, hexane presents higher selectivity for the lipophilic compounds comparatively with ethanol. Regarding the MAE technique, the results showed that the optimal conditions (19 ± 3 min, 133 ± 12 •c and 1.6 ± 0.5 g!L) allowed higher ergosterol extraction levels (556 ± 26 mg/100 g dw). The values obtained with MAE are close to the ones obtained with conventional Soxhlet extraction (676 ± 3 mg/100 g dw) and UAE. Overall, UAE and MAE proved to he efficient technologies to maximize ergosterol extraction yields.

Optimization of microwave-assisted extraction of hydrophilic and lipophilic antioxidants from a surplus tomato crop by response surface methodology

Tomato is the second most important vegetable crop worldwide and a rich source of industrially interesting antioxidants. Hence, the microwave-assisted extraction of hydrophilic (H) and lipophilic (L) antioxidants from a surplus tomato crop was optimized using response surface methodology. The relevant independent variables were temperature (T), extraction time (t), ethanol concentration (Et) and solid/liquid ratio (S/L). The concentration-time response methods of crocin and β-carotene bleaching were applied, since they are suitable in vitro assays to evaluate the antioxidant activity of H and L matrices, respectively. The optimum operating conditions that maximized the extraction were as follows: t, 2.25 min; T, 149.2 ºC; Et, 99.1 %; and S/L, 45.0 g/L for H antioxidants; and t, 15.4 min; T, 60.0 ºC; Et, 33.0 %; and S/L, 15.0 g/L for L antioxidants. This industrial approach indicated that surplus tomatoes possess a high content of antioxidants, offering an alternative source for obtaining natural value-added compounds. Additionally, by testing the relationship between the polarity of the extraction solvent and the antioxidant activity of the extracts in H and L media (polarity-activity relationship), useful information for the study of complex natural extracts containing components with variable degrees of polarity was obtained.

An experimental study of adaptive control for evolutionary algorithms

International audience

Using a Combination of Phylogenetic Comparative and Paleobotanical Methods to Elucidate Patterns of Lineage Selection in Rosales (Plantae: Anthophyta)

An increasing focus in evolutionary biology is on the interplay between mesoscale ecological and evolutionary processes such as population demographics, habitat tolerance, and especially geographic distribution, as potential drivers responsible for patterns of diversification and extinction over geologic time. However, few studies to date connect organismal processes such as survival and reproduction through mesoscale patterns to long-term macroevolutionary trends. In my dissertation, I investigate how mechanism of seed dispersal, mediated through geographic range size, influences diversification rates in the Rosales (Plantae: Anthophyta). In my first chapter, I validate the phylogenetic comparative methods that I use in my second and third chapters. Available state speciation and extinction (SSE) models assumptions about evolution known to be false through fossil data. I show, however, that as long as net diversification rates remain positive – a condition likely true for the Rosales – these violations of SSE’s assumptions do not cause significantly biased results. With SSE methods validated, my second chapter reconstructs three associations that appear to increase diversification rate for Rosalean genera: (1) herbaceous habit; (2) a three-way interaction combining animal dispersal, high within-genus species richness, and geographic range on multiple continents; (3) a four-way interaction combining woody habit with the other three characteristics of (2). I suggest that the three- and four-way interactions represent colonization ability and resulting extinction resistance in the face of late Cenozoic climate change; however, there are other possibilities as well that I hope to investigate in future research. My third chapter reconstructs the phylogeographic history of the Rosales using both non-fossil-assisted SSE methods as well as fossil-informed traditional phylogeographic analysis. Ancestral state reconstructions indicate that the Rosaceae diversified in North America while the other Rosalean families diversified elsewhere, possibly in Eurasia. SSE is able to successfully identify groups of genera that were likely to have been ancestrally widespread, but has poorer taxonomic resolution than methods that use fossil data. In conclusion, these chapters together suggest several potential causal links between organismal, mesoscale, and geologic scale processes, but further work will be needed to test the hypotheses that I raise here.

Multi-level, Multi-stage and Stochastic Optimization Models for Energy Conservation in Buildings for Federal, State and Local Agencies

Energy Conservation Measure (ECM) project selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. Many of these energy efficient retrofit projects should be viewed as a series of investments with annual returns for these traditionally risk-averse agencies. Given a list of ECMs available, federal, state and local agencies must determine how to implement projects at lowest costs. The most common methods of implementation planning are suboptimal relative to cost. Federal, state and local agencies can obtain greater returns on their energy conservation investment over traditional methods, regardless of the implementing organization. This dissertation outlines several approaches to improve the traditional energy conservations models. Any public buildings in regions with similar energy conservation goals in the United States or internationally can also benefit greatly from this research. Additionally, many private owners of buildings are under mandates to conserve energy e.g., Local Law 85 of the New York City Energy Conservation Code requires any building, public or private, to meet the most current energy code for any alteration or renovation. Thus, both public and private stakeholders can benefit from this research. The research in this dissertation advances and presents models that decision-makers can use to optimize the selection of ECM projects with respect to the total cost of implementation. A practical application of a two-level mathematical program with equilibrium constraints (MPEC) improves the current best practice for agencies concerned with making the most cost-effective selection leveraging energy services companies or utilities. The two-level model maximizes savings to the agency and profit to the energy services companies (Chapter 2). An additional model presented leverages a single congressional appropriation to implement ECM projects (Chapter 3). Returns from implemented ECM projects are used to fund additional ECM projects. In these cases, fluctuations in energy costs and uncertainty in the estimated savings severely influence ECM project selection and the amount of the appropriation requested. A risk aversion method proposed imposes a minimum on the number of “of projects completed in each stage. A comparative method using Conditional Value at Risk is analyzed. Time consistency was addressed in this chapter. This work demonstrates how a risk-based, stochastic, multi-stage model with binary decision variables at each stage provides a much more accurate estimate for planning than the agency’s traditional approach and deterministic models. Finally, in Chapter 4, a rolling-horizon model allows for subadditivity and superadditivity of the energy savings to simulate interactive effects between ECM projects. The approach makes use of inequalities (McCormick, 1976) to re-express constraints that involve the product of binary variables with an exact linearization (related to the convex hull of those constraints). This model additionally shows the benefits of learning between stages while remaining consistent with the single congressional appropriations framework.

A Bayesian optimization algorithm for the nurse scheduling problem

A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse’s assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.