Technological processes study for high value health ingredients and foods production

This PhD thesis describes set up of technological models for obtaining high health value foods and ingredients that preserve the final product characteristics as well as enrich with nutritional components. In particular, the main object of my research has been Virgin Olive Oil (VOO) and its important antioxidant compounds which differentiate it from all other vegetables oils. It is well known how the qualitative and quantitative presence of phenolic molecules extracted from olives during oil production is fundamental for its oxidative and nutritional quality. For this purpose, agronomic and technological conditions of its production have been investigated. It has also been examined how this fraction can be better preserved during storage. Moreover, its relation with VOO sensorial characteristics and its interaction with a protein in emulsion foods have also been studied. Finally, an experimental work was carried out to determine the antioxidative and heat resistance properties of a new antioxidant (EVS-OL) when used for high temperature frying such as is typically employed for the preparation of french fries. Results of the scientific research have been submitted for a publication and some data has already been published in national and international scientific journals.

Quality and safety of oils and fats obtained as co-products or by-products of the food chain and destined to animal production

The aim of the first part of this thesis was to evaluate the effect of trans fatty acid- (TFA), contaminant, polycyclic aromatic hydrocarbon (PAH)- and oxidation productenriched diets on the content of TFA and conjugated linoleic acid (CLA) isomers in meat and liver of both poultry and rabbit. The enriched feedings were prepared with preselected fatty co-and by-products that contained low and high levels of TFA (low, palm fatty acid distillate; high, hydrogenated palm fatty acid distillate), environmental contaminants (dioxins and PCBs) (two different fish oils), PAH (olive oil acid oils and pomace olive oil from chemical refining, for low and high levels) and oxidation products (sunflower-olive oil blend before and after frying), so as to obtain single feedings with three enrichment degrees (high, medium and low) of the compound of interest. This experimental set-up is a part of a large, collaborative European project (http://www.ub.edu/feedfat/), where other chemical and health parameters are assessed. Lipids were extracted, methylated with diazomethane, then transmethylated with 2N KOH/methanol and analyzed by GC and silver-ion TLC-GC. TFA and CLA were determined in the fats, the feedings, meat and liver of both poultry and rabbit. In general, the level of TFA and CLA in meat and liver mainly varied according to those originally found in the feeding fats. It must be pointed out, though, that TFA and CLA accumulation was different for the two animal species, as well as for the two types of tissues. The TFA composition of meat and liver changes according to the composition of the oils added to the feeds with some differences between species. Chicken meat with skin shows higher TFA content (2.6–5.4 fold) than rabbit meat, except for the “PAH” trial. Chicken liver shows higher TFA content (1.2–2.1 fold) than rabbit liver, except for the “TRANS” and “PAH” trials. In both chicken and rabbit meats, the TFA content was higher for the “TRANS” trial, followed by the “DIOXIN” trial. Slight differences were found on the “OXIDATION” and “PAH” trends in both types of meats. In both chicken and rabbit livers, the TFA content was higher for the “TRANS” trial, followed by those of the “PAH”, “DIOXIN” and “OXIDATION” trials. This trend, however, was not identical to that of feeds, where the TFA content varied as follows: “TRANS” > “DIOXIN” >“PAH” > “OXIDATION”. In chicken and rabbit meat samples, C18:1 TFA were the most abundant, followed by C18:2 TFA and C18:3 TFA, except for the “DIOXIN” trial where C18:3 TFA > C18:2 TFA. In chicken and rabbit liver samples of the “TRANS” and “OXIDATION” trials, C18:1 TFA were the most abundant, followed by C18:2 TFA and C18:3 TFA, whereas C18:3 TFA > C18:2 in the “DIOXIN” trial. Slight differences were found on the “PAH” trend in livers from both species. The second part of the thesis dealt with the study of lipid oxidation in washed turkey muscle added with different antioxidants. The evaluation on the oxidative stability of muscle foods found that oxidation could be measured by headspace solid phase microestraction (SPME) of hexanal and propanal. To make this method effective, an antioxidant system was added to stored muscle to stop the oxidative processes. An increase in ionic strength of the sample was also implemented to increase the concentration of aldehydes in the headspace. This method was found to be more sensitive than the commonly used thiobarbituric acid reactive substances (TBARs) method. However, after antioxidants were added and oxidation was stopped, the concentration of aldehydes decreased. It was found that the decrease in aldehyde concentration was due to the binding of the aldehydes to muscle proteins, thus decreasing the volatility and making them less detectable.

Bayesian Analysis of Linear Inverse Problems with Applications in Economics and Finance

In my PhD thesis I propose a Bayesian nonparametric estimation method for structural econometric models where the functional parameter of interest describes the economic agent's behavior. The structural parameter is characterized as the solution of a functional equation, or by using more technical words, as the solution of an inverse problem that can be either ill-posed or well-posed. From a Bayesian point of view, the parameter of interest is a random function and the solution to the inference problem is the posterior distribution of this parameter. A regular version of the posterior distribution in functional spaces is characterized. However, the infinite dimension of the considered spaces causes a problem of non continuity of the solution and then a problem of inconsistency, from a frequentist point of view, of the posterior distribution (i.e. problem of ill-posedness). The contribution of this essay is to propose new methods to deal with this problem of ill-posedness. The first one consists in adopting a Tikhonov regularization scheme in the construction of the posterior distribution so that I end up with a new object that I call regularized posterior distribution and that I guess it is solution of the inverse problem. The second approach consists in specifying a prior distribution on the parameter of interest of the g-prior type. Then, I detect a class of models for which the prior distribution is able to correct for the ill-posedness also in infinite dimensional problems. I study asymptotic properties of these proposed solutions and I prove that, under some regularity condition satisfied by the true value of the parameter of interest, they are consistent in a "frequentist" sense. Once I have set the general theory, I apply my bayesian nonparametric methodology to different estimation problems. First, I apply this estimator to deconvolution and to hazard rate, density and regression estimation. Then, I consider the estimation of an Instrumental Regression that is useful in micro-econometrics when we have to deal with problems of endogeneity. Finally, I develop an application in finance: I get the bayesian estimator for the equilibrium asset pricing functional by using the Euler equation defined in the Lucas'(1978) tree-type models.

Non-Linear Pricing in Imperfectly Competitive Markets

This thesis is dedicated to the analysis of non-linear pricing in oligopoly. Non-linear pricing is a fairly predominant practice in most real markets, mostly characterized by some amount of competition. The sophistication of pricing practices has increased in the latest decades due to the technological advances that have allowed companies to gather more and more data on consumers preferences. The first essay of the thesis highlights the main characteristics of oligopolistic non-linear pricing. Non-linear pricing is a special case of price discrimination. The theory of price discrimination has to be modified in presence of oligopoly: in particular, a crucial role is played by the competitive externality that implies that product differentiation is closely related to the possibility of discriminating. The essay reviews the theory of competitive non-linear pricing by starting from its foundations, mechanism design under common agency. The different approaches to model non-linear pricing are then reviewed. In particular, the difference between price and quantity competition is highlighted. Finally, the close link between non-linear pricing and the recent developments in the theory of vertical differentiation is explored. The second essay shows how the effects of non-linear pricing are determined by the relationship between the demand and the technological structure of the market. The chapter focuses on a model in which firms supply a homogeneous product in two different sizes. Information about consumers' reservation prices is incomplete and the production technology is characterized by size economies. The model provides insights on the size of the products that one finds in the market. Four equilibrium regions are identified depending on the relative intensity of size economies with respect to consumers' evaluation of the good. Regions for which the product is supplied in a single unit or in several different sizes or in only a very large one. Both the private and social desirability of non-linear pricing varies across different equilibrium regions. The third essay considers the broadband internet market. Non discriminatory issues seem the core of the recent debate on the opportunity or not of regulating the internet. One of the main questions posed is whether the telecom companies, owning the networks constituting the internet, should be allowed to offer quality-contingent contracts to content providers. The aim of this essay is to analyze the issue through a stylized two-sided market model of the web that highlights the effects of such a discrimination over quality, prices and participation to the internet of providers and final users. An overall welfare comparison is proposed, concluding that the final effects of regulation crucially depend on both the technology and preferences of agents.

Physiology and Biotechnology of the Hydrogen Production with the Green Microalga Chlamydomonas reinhardtii

The hydrogen production in the green microalga Chlamydomonas reinhardtii was evaluated by means of a detailed physiological and biotechnological study. First, a wide screening of the hydrogen productivity was done on 22 strains of C. reinhardtii, most of which mutated at the level of the D1 protein. The screening revealed for the first time that mutations upon the D1 protein may result on an increased hydrogen production. Indeed, productions ranged between 0 and more than 500 mL hydrogen per liter of culture (Torzillo, Scoma et al., 2007a), the highest producer (L159I-N230Y) being up to 5 times more performant than the strain cc124 widely adopted in literature (Torzillo, Scoma, et al., 2007b). Improved productivities by D1 protein mutants were generally a result of high photosynthetic capabilities counteracted by high respiration rates. Optimization of culture conditions were addressed according to the results of the physiological study of selected strains. In a first step, the photobioreactor (PBR) was provided with a multiple-impeller stirring system designed, developed and tested by us, using the strain cc124. It was found that the impeller system was effectively able to induce regular and turbulent mixing, which led to improved photosynthetic yields by means of light/dark cycles. Moreover, improved mixing regime sustained higher respiration rates, compared to what obtained with the commonly used stir bar mixing system. As far as the results of the initial screening phase are considered, both these factors are relevant to the hydrogen production. Indeed, very high energy conversion efficiencies (light to hydrogen) were obtained with the impeller device, prooving that our PBR was a good tool to both improve and study photosynthetic processes (Giannelli, Scoma et al., 2009). In the second part of the optimization, an accurate analysis of all the positive features of the high performance strain L159I-N230Y pointed out, respect to the WT, it has: (1) a larger chlorophyll optical cross-section; (2) a higher electron transfer rate by PSII; (3) a higher respiration rate; (4) a higher efficiency of utilization of the hydrogenase; (5) a higher starch synthesis capability; (6) a higher per cell D1 protein amount; (7) a higher zeaxanthin synthesis capability (Torzillo, Scoma et al., 2009). These information were gathered with those obtained with the impeller mixing device to find out the best culture conditions to optimize productivity with strain L159I-N230Y. The main aim was to sustain as long as possible the direct PSII contribution, which leads to hydrogen production without net CO2 release. Finally, an outstanding maximum rate of 11.1 ± 1.0 mL/L/h was reached and maintained for 21.8 ± 7.7 hours, when the effective photochemical efficiency of PSII (ΔF/F'm) underwent a last drop to zero. If expressed in terms of chl (24.0 ± 2.2 µmoles/mg chl/h), these rates of production are 4 times higher than what reported in literature to date (Scoma et al., 2010a submitted). DCMU addition experiments confirmed the key role played by PSII in sustaining such rates. On the other hand, experiments carried out in similar conditions with the control strain cc124 showed an improved final productivity, but no constant PSII direct contribution. These results showed that, aside from fermentation processes, if proper conditions are supplied to selected strains, hydrogen production can be substantially enhanced by means of biophotolysis. A last study on the physiology of the process was carried out with the mutant IL. Although able to express and very efficiently utilize the hydrogenase enzyme, this strain was unable to produce hydrogen when sulfur deprived. However, in a specific set of experiments this goal was finally reached, pointing out that other than (1) a state 1-2 transition of the photosynthetic apparatus, (2) starch storage and (3) anaerobiosis establishment, a timely transition to the hydrogen production is also needed in sulfur deprivation to induce the process before energy reserves are driven towards other processes necessary for the survival of the cell. This information turned out to be crucial when moving outdoor for the hydrogen production in a tubular horizontal 50-liter PBR under sunlight radiation. First attempts with laboratory grown cultures showed that no hydrogen production under sulfur starvation can be induced if a previous adaptation of the culture is not pursued outdoor. Indeed, in these conditions the hydrogen production under direct sunlight radiation with C. reinhardtii was finally achieved for the first time in literature (Scoma et al., 2010b submitted). Experiments were also made to optimize productivity in outdoor conditions, with respect to the light dilution within the culture layers. Finally, a brief study of the anaerobic metabolism of C. reinhardtii during hydrogen oxidation has been carried out. This study represents a good integration to the understanding of the complex interplay of pathways that operate concomitantly in this microalga.

Constraint based methods for allocation and scheduling of periodic applications

This work presents exact algorithms for the Resource Allocation and Cyclic Scheduling Problems (RA&CSPs). Cyclic Scheduling Problems arise in a number of application areas, such as in hoist scheduling, mass production, compiler design (implementing scheduling loops on parallel architectures), software pipelining, and in embedded system design. The RA&CS problem concerns time and resource assignment to a set of activities, to be indefinitely repeated, subject to precedence and resource capacity constraints. In this work we present two constraint programming frameworks facing two different types of cyclic problems. In first instance, we consider the disjunctive RA&CSP, where the allocation problem considers unary resources. Instances are described through the Synchronous Data-flow (SDF) Model of Computation. The key problem of finding a maximum-throughput allocation and scheduling of Synchronous Data-Flow graphs onto a multi-core architecture is NP-hard and has been traditionally solved by means of heuristic (incomplete) algorithms. We propose an exact (complete) algorithm for the computation of a maximum-throughput mapping of applications specified as SDFG onto multi-core architectures. Results show that the approach can handle realistic instances in terms of size and complexity. Next, we tackle the Cyclic Resource-Constrained Scheduling Problem (i.e. CRCSP). We propose a Constraint Programming approach based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions. The proposed approaches have been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances achieving good results on practical size problem.

Quantum Integrability in Non-Linear Sigma Models related to Gauge/String Correspondences

The Thermodynamic Bethe Ansatz analysis is carried out for the extended-CP^N class of integrable 2-dimensional Non-Linear Sigma Models related to the low energy limit of the AdS_4xCP^3 type IIA superstring theory. The principal aim of this program is to obtain further non-perturbative consistency check to the S-matrix proposed to describe the scattering processes between the fundamental excitations of the theory by analyzing the structure of the Renormalization Group flow. As a noteworthy byproduct we eventually obtain a novel class of TBA models which fits in the known classification but with several important differences. The TBA framework allows the evaluation of some exact quantities related to the conformal UV limit of the model: effective central charge, conformal dimension of the perturbing operator and field content of the underlying CFT. The knowledge of this physical quantities has led to the possibility of conjecturing a perturbed CFT realization of the integrable models in terms of coset Kac-Moody CFT. The set of numerical tools and programs developed ad hoc to solve the problem at hand is also discussed in some detail with references to the code.

Mathematical Optimization Applied to Thermal and Electrical Energy Systems

This Thesis aims at building and discussing mathematical models applications focused on Energy problems, both on the thermal and electrical side. The objective is to show how mathematical programming techniques developed within Operational Research can give useful answers in the Energy Sector, how they can provide tools to support decision making processes of Companies operating in the Energy production and distribution and how they can be successfully used to make simulations and sensitivity analyses to better understand the state of the art and convenience of a particular technology by comparing it with the available alternatives. The first part discusses the fundamental mathematical background followed by a comprehensive literature review about mathematical modelling in the Energy Sector. The second part presents mathematical models for the District Heating strategic network design and incremental network design. The objective is the selection of an optimal set of new users to be connected to an existing thermal network, maximizing revenues, minimizing infrastructure and operational costs and taking into account the main technical requirements of the real world application. Results on real and randomly generated benchmark networks are discussed with particular attention to instances characterized by big networks dimensions. The third part is devoted to the development of linear programming models for optimal battery operation in off-grid solar power schemes, with consideration of battery degradation. The key contribution of this work is the inclusion of battery degradation costs in the optimisation models. As available data on relating degradation costs to the nature of charge/discharge cycles are limited, we concentrate on investigating the sensitivity of operational patterns to the degradation cost structure. The objective is to investigate the combination of battery costs and performance at which such systems become economic. We also investigate how the system design should change when battery degradation is taken into account.

Development of methodologies supporting the sustainable and safe integration of offshore conventional and renewable energy production

Against a backdrop of rapidly increasing worldwide population and growing energy demand, the development of renewable energy technologies has become of primary importance in the effort to reduce greenhouse gas emissions. However, it is often technically and economically infeasible to transport discontinuous renewable electricity for long distances to the shore. Another shortcoming of non-programmable renewable power is its integration into the onshore grid without affecting the dispatching process. On the other hand, the offshore oil & gas industry is striving to reduce overall carbon footprint from onsite power generators and limiting large expenses associated to carrying electricity from remote offshore facilities. Furthermore, the increased complexity and expansion towards challenging areas of offshore hydrocarbons operations call for higher attention to safety and environmental protection issues from major accident hazards. Innovative hybrid energy systems, as Power-to-Gas (P2G), Power-to-Liquid (P2L) and Gas-to-Power (G2P) options, implemented at offshore locations, would offer the opportunity to overcome challenges of both renewable and oil & gas sectors. This study aims at the development of systematic methodologies based on proper sustainability and safety performance indicators supporting the choice of P2G, P2L and G2P hybrid energy options for offshore green projects in early design phases. An in-depth analysis of the different offshore hybrid strategies was performed. The literature reviews on existing methods proposing metrics to assess sustainability of hybrid energy systems, inherent safety of process routes in conceptual design stage and environmental protection of installations from oil and chemical accidental spills were carried out. To fill the gaps, a suite of specific decision-making methodologies was developed, based on representative multi-criteria indicators addressing technical, economic, environmental and societal aspects of alternative options. A set of five case-studies was defined, covering different offshore scenarios of concern, to provide an assessment of the effectiveness and value of the developed tools.

Production and characterization of novel thermoplastic (nano)composite materials for additive manufacturing applications

The increasing environmental global regulations have directed scientific research towards more sustainable materials, even in the field of composite materials for additive manufacturing. In this context, the presented research is devoted to the development of thermoplastic composites for FDM application with a low environmental impact, focusing on the possibility to use wastes from different industrial processes as filler for the production of composite filaments for FDM 3D printing. In particular carbon fibers recycled by pyro-gasification process of CFRP scraps were used as reinforcing agent for PLA, a biobased polymeric matrix. Since the high value of CFs, the ability to re-use recycled CFs, replacing virgin ones, seems to be a promising option in terms of sustainability and circular economy. Moreover, wastes from different agricultural industries, i.e. wheat and rice production processes, were valorised and used as biofillers for the production of PLA-biocomposites. The integration of these agricultural wastes into PLA bioplastic allowed to obtain biocomposites with improved eco-sustainability, biodegradability, lightweight, and lower cost. Finally, the study of novel composites for FDM was extended towards elastomeric nanocomposite materials, in particular TPU reinforced with graphene. The research procedure of all projects involves the optimization of production methods of composite filaments with a particular attention on the possible degradation of polymeric matrices. Then, main thermal properties of 3D printed object are evaluated by TGA, DSC characterization. Additionally, specific heat capacity (CP) and Coefficient of Linear Thermal Expansion (CLTE) measurements are useful to estimate the attitude of composites for the prevention of typical FDM issues, i.e. shrinkage and warping. Finally, the mechanical properties of 3D printed composites and their anisotropy are investigated by tensile test using distinct kinds of specimens with different printing angles with respect to the testing direction.

Data driven regularization models of non-linear ill-posed inverse problems in imaging

Imaging technologies are widely used in application fields such as natural sciences, engineering, medicine, and life sciences. A broad class of imaging problems reduces to solve ill-posed inverse problems (IPs). Traditional strategies to solve these ill-posed IPs rely on variational regularization methods, which are based on minimization of suitable energies, and make use of knowledge about the image formation model (forward operator) and prior knowledge on the solution, but lack in incorporating knowledge directly from data. On the other hand, the more recent learned approaches can easily learn the intricate statistics of images depending on a large set of data, but do not have a systematic method for incorporating prior knowledge about the image formation model. The main purpose of this thesis is to discuss data-driven image reconstruction methods which combine the benefits of these two different reconstruction strategies for the solution of highly nonlinear ill-posed inverse problems. Mathematical formulation and numerical approaches for image IPs, including linear as well as strongly nonlinear problems are described. More specifically we address the Electrical impedance Tomography (EIT) reconstruction problem by unrolling the regularized Gauss-Newton method and integrating the regularization learned by a data-adaptive neural network. Furthermore we investigate the solution of non-linear ill-posed IPs introducing a deep-PnP framework that integrates the graph convolutional denoiser into the proximal Gauss-Newton method with a practical application to the EIT, a recently introduced promising imaging technique. Efficient algorithms are then applied to the solution of the limited electrods problem in EIT, combining compressive sensing techniques and deep learning strategies. Finally, a transformer-based neural network architecture is adapted to restore the noisy solution of the Computed Tomography problem recovered using the filtered back-projection method.