Innovation in conceptual design of chemical processes

Nowadays, the chemical industry has reached significant goals to produce essential components for human being. The growing competitiveness of the market caused an important acceleration in R&D activities, introducing new opportunities and procedures for the definition of process improvement and optimization. In this dynamicity, sustainability is becoming one of the key aspects for the technological progress encompassing economic, environmental protection and safety aspects. With respect to the conceptual definition of sustainability, literature reports an extensive discussion of the strategies, as well as sets of specific principles and guidelines. However, literature procedures are not completely suitable and applicable to process design activities. Therefore, the development and introduction of sustainability-oriented methodologies is a necessary step to enhance process and plant design. The definition of key drivers as support system is a focal point for early process design decisions or implementation of process modifications. In this context, three different methodologies are developed to support design activities providing criteria and guidelines in a sustainable perspective. In this framework, a set of key Performance Indicators is selected and adopted to characterize the environmental, safety, economic and energetic aspects of a reference process. The methodologies are based on heat and material balances and the level of detailed for input data are compatible with available information of the specific application. Multiple case-studies are defined to prove the effectiveness of the methodologies. The principal application is the polyolefin productive lifecycle chain with particular focus on polymerization technologies. In this context, different design phases are investigated spanning from early process feasibility study to operative and improvements assessment. This flexibility allows to apply the methodologies at any level of design, providing supporting guidelines for design activities, compare alternative solutions, monitor operating process and identify potential for improvements.

Application of chromatographic and spectroscopic techniques in the evaluation of the lipid fraction of animal products

Lipolysis and oxidation of lipids in foods are the major biochemical and chemical processes that cause food quality deterioration, leading to the characteristic, unpalatable odour and flavour called rancidity. In addition to unpalatability, rancidity may give rise to toxic levels of certain compounds like aldehydes, hydroperoxides, epoxides and cholesterol oxidation products. In this PhD study chromatographic and spectroscopic techniques were employed to determine the degree of rancidity in different animal products and its relationship with technological parameters like feeding fat sources, packaging, processing and storage conditions. To achieve this goal capillary gas chromatography (CGC) was employed not only to determine the fatty acids profile but also, after solid phase extraction, the amount of free fatty acids (FFA), diglycerides (DG), sterols (cholesterol and phytosterols) and cholesterol oxidation products (COPs). To determine hydroperoxides, primary products of oxidation and quantify secondary products UV/VIS absorbance spectroscopy was applied. Most of the foods analysed in this study were meat products. In actual fact, lipid oxidation is a major deterioration reaction in meat and meat products and results in adverse changes in the colour, flavour and texture of meat. The development of rancidity has long recognized as a serious problem during meat handling, storage and processing. On a dairy product, a vegetal cream, a study of lipid fraction and development of rancidity during storage was carried out to evaluate its shelf-life and some nutritional features life saturated/unsaturated fatty acids ratio and phytosterols content. Then, according to the interest that has been growing around functional food in the last years, a new electrophoretic method was optimized and compared with HPLC to check the quality of a beehive product like royal jelly. This manuscript reports the main results obtained in the five activities briefly summarized as follows: 1) comparison between HPLC and a new electrophoretic method in the evaluation of authenticity of royal jelly; 2) study of the lipid fraction of a vegetal cream under different storage conditions; 3) study of lipid oxidation in minced beef during storage under a modified atmosphere packaging, before and after cooking; 4) evaluation of the influence of dietary fat and processing on the lipid fraction of chicken patties; 5) study of the lipid fraction of typical Italian and Spanish pork dry sausages and cured hams.

Food lipids: their effects on quality and oxidative stability of animal tissues and emulsions

Lipolysis and oxidation of lipids in foods are the major biochemical and chemical processes that cause food quality deterioration, leading to the characteristic, unpalatable odour and flavour called rancidity. In addition to unpalatability, rancidity may give rise to toxic levels of certain compounds like aldehydes, hydroperoxides, epoxides and cholesterol oxidation products. In this PhD study chromatographic and spectroscopic techniques were employed to determine the degree of lipid oxidation in different animal products and its relationship with technological parameters like feeding fat sources, packaging, processing and storage conditions. To achieve this goal capillary gas chromatography (CGC) was employed not only to determine the fatty acids profile but also, after solid phase extraction, the amount of sterols (cholesterol and phytosterols) and cholesterol oxidation products (COPs). To determine hydroperoxides, primary products of oxidation and quantify secondary products UV/VIS absorbance spectroscopy was applied. Beef and pork meat in this study were analysed. In actual fact, lipid oxidation is a major deterioration reaction in meat, meat products and results in adverse changes in the colour, flavour, texture of meat and develops different compounds which should be a risk to human health as oxysterols. On beef and pork meat, a study of lipid fraction during storage was carried out to evaluate its shelf-life and some nutritional features life saturated/unsaturated fatty acids ratio and sterols content, in according to the interest that has been growing around functional food in the last years. The last part of this research was focused on the study of lipid oxidation in emulsions. In oil-in-water emulsions antioxidant activity of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) was evaluated. The rates of lipid oxidation of 1.0% stripped soybean oil-in-water emulsions with DOPC were followed by monitoring lipid hydroperoxide and hexanal as indicators of primary and secondary oxidation products and the droplet surface charge or zeta potential (ζ) of the emulsions with varying concentrations of DOPC were tested. This manuscript reports the main results obtained in the three activities briefly summarized as follows: 1. study on effects of feeding composition on the photoxidative stability of lipids from beef meat, evaluated during storage under commercial retail conditions; 2. evaluation of effects of diets and storage conditions on the oxidative stability of pork meat lipids; 3. study on oxidative behavior of DOPC in stripped soybean oil-in-water emulsions stabilized by nonionic surfactant.

Kinematic description of the rupture from strong motion data: strategies for a robust inversion

We present a non linear technique to invert strong motion records with the aim of obtaining the final slip and rupture velocity distributions on the fault plane. In this thesis, the ground motion simulation is obtained evaluating the representation integral in the frequency. The Green’s tractions are computed using the discrete wave-number integration technique that provides the full wave-field in a 1D layered propagation medium. The representation integral is computed through a finite elements technique, based on a Delaunay’s triangulation on the fault plane. The rupture velocity is defined on a coarser regular grid and rupture times are computed by integration of the eikonal equation. For the inversion, the slip distribution is parameterized by 2D overlapping Gaussian functions, which can easily relate the spectrum of the possible solutions with the minimum resolvable wavelength, related to source-station distribution and data processing. The inverse problem is solved by a two-step procedure aimed at separating the computation of the rupture velocity from the evaluation of the slip distribution, the latter being a linear problem, when the rupture velocity is fixed. The non-linear step is solved by optimization of an L2 misfit function between synthetic and real seismograms, and solution is searched by the use of the Neighbourhood Algorithm. The conjugate gradient method is used to solve the linear step instead. The developed methodology has been applied to the M7.2, Iwate Nairiku Miyagi, Japan, earthquake. The estimated magnitude seismic moment is 2.6326 dyne∙cm that corresponds to a moment magnitude MW 6.9 while the mean the rupture velocity is 2.0 km/s. A large slip patch extends from the hypocenter to the southern shallow part of the fault plane. A second relatively large slip patch is found in the northern shallow part. Finally, we gave a quantitative estimation of errors associates with the parameters.

Parallel modeling of the electric field distribution in the brain

The term "Brain Imaging" identi�es a set of techniques to analyze the structure and/or functional behavior of the brain in normal and/or pathological situations. These techniques are largely used in the study of brain activity. In addition to clinical usage, analysis of brain activity is gaining popularity in others recent �fields, i.e. Brain Computer Interfaces (BCI) and the study of cognitive processes. In this context, usage of classical solutions (e.g. f MRI, PET-CT) could be unfeasible, due to their low temporal resolution, high cost and limited portability. For these reasons alternative low cost techniques are object of research, typically based on simple recording hardware and on intensive data elaboration process. Typical examples are ElectroEncephaloGraphy (EEG) and Electrical Impedance Tomography (EIT), where electric potential at the patient's scalp is recorded by high impedance electrodes. In EEG potentials are directly generated from neuronal activity, while in EIT by the injection of small currents at the scalp. To retrieve meaningful insights on brain activity from measurements, EIT and EEG relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of the electric �field distribution therein. The inhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeo�ff between physical accuracy and technical feasibility, which currently severely limits the capabilities of these techniques. Moreover elaboration of data recorded requires usage of regularization techniques computationally intensive, which influences the application with heavy temporal constraints (such as BCI). This work focuses on the parallel implementation of a work-flow for EEG and EIT data processing. The resulting software is accelerated using multi-core GPUs, in order to provide solution in reasonable times and address requirements of real-time BCI systems, without over-simplifying the complexity and accuracy of the head models.

Procedures of quality control and data analysis of multi-site ground-based observations for the absolute flux calibration of Gaia

The Gaia space mission is a major project for the European astronomical community. As challenging as it is, the processing and analysis of the huge data-flow incoming from Gaia is the subject of thorough study and preparatory work by the DPAC (Data Processing and Analysis Consortium), in charge of all aspects of the Gaia data reduction. This PhD Thesis was carried out in the framework of the DPAC, within the team based in Bologna. The task of the Bologna team is to define the calibration model and to build a grid of spectro-photometric standard stars (SPSS) suitable for the absolute flux calibration of the Gaia G-band photometry and the BP/RP spectrophotometry. Such a flux calibration can be performed by repeatedly observing each SPSS during the life-time of the Gaia mission and by comparing the observed Gaia spectra to the spectra obtained by our ground-based observations. Due to both the different observing sites involved and the huge amount of frames expected (≃100000), it is essential to maintain the maximum homogeneity in data quality, acquisition and treatment, and a particular care has to be used to test the capabilities of each telescope/instrument combination (through the “instrument familiarization plan”), to devise methods to keep under control, and eventually to correct for, the typical instrumental effects that can affect the high precision required for the Gaia SPSS grid (a few % with respect to Vega). I contributed to the ground-based survey of Gaia SPSS in many respects: with the observations, the instrument familiarization plan, the data reduction and analysis activities (both photometry and spectroscopy), and to the maintenance of the data archives. However, the field I was personally responsible for was photometry and in particular relative photometry for the production of short-term light curves. In this context I defined and tested a semi-automated pipeline which allows for the pre-reduction of imaging SPSS data and the production of aperture photometry catalogues ready to be used for further analysis. A series of semi-automated quality control criteria are included in the pipeline at various levels, from pre-reduction, to aperture photometry, to light curves production and analysis.

Pyrolysis-Gas Chromatography-Mass Spectometry and Chemometric Analysis for the Characterization of Complex Matrices

The present PhD thesis was focused on the development and application of chemical methodology (Py-GC-MS) and data-processing method by multivariate data analysis (chemometrics). The chromatographic and mass spectrometric data obtained with this technique are particularly suitable to be interpreted by chemometric methods such as PCA (Principal Component Analysis) as regards data exploration and SIMCA (Soft Independent Models of Class Analogy) for the classification. As a first approach, some issues related to the field of cultural heritage were discussed with a particular attention to the differentiation of binders used in pictorial field. A marker of egg tempera the phosphoric acid esterified, a pyrolysis product of lecithin, was determined using HMDS (hexamethyldisilazane) rather than the TMAH (tetramethylammonium hydroxide) as a derivatizing reagent. The validity of analytical pyrolysis as tool to characterize and classify different types of bacteria was verified. The FAMEs chromatographic profiles represent an important tool for the bacterial identification. Because of the complexity of the chromatograms, it was possible to characterize the bacteria only according to their genus, while the differentiation at the species level has been achieved by means of chemometric analysis. To perform this study, normalized areas peaks relevant to fatty acids were taken into account. Chemometric methods were applied to experimental datasets. The obtained results demonstrate the effectiveness of analytical pyrolysis and chemometric analysis for the rapid characterization of bacterial species. Application to a samples of bacterial (Pseudomonas Mendocina), fungal (Pleorotus ostreatus) and mixed- biofilms was also performed. A comparison with the chromatographic profiles established the possibility to: • Differentiate the bacterial and fungal biofilms according to the (FAMEs) profile. • Characterize the fungal biofilm by means the typical pattern of pyrolytic fragments derived from saccharides present in the cell wall. • Individuate the markers of bacterial and fungal biofilm in the same mixed-biofilm sample.

Data Mining in Clinical Practice for the Quantification of Motor Impairment in Parkinson's Disease

Advances in biomedical signal acquisition systems for motion analysis have led to lowcost and ubiquitous wearable sensors which can be used to record movement data in different settings. This implies the potential availability of large amounts of quantitative data. It is then crucial to identify and to extract the information of clinical relevance from the large amount of available data. This quantitative and objective information can be an important aid for clinical decision making. Data mining is the process of discovering such information in databases through data processing, selection of informative data, and identification of relevant patterns. The databases considered in this thesis store motion data from wearable sensors (specifically accelerometers) and clinical information (clinical data, scores, tests). The main goal of this thesis is to develop data mining tools which can provide quantitative information to the clinician in the field of movement disorders. This thesis will focus on motor impairment in Parkinson's disease (PD). Different databases related to Parkinson subjects in different stages of the disease were considered for this thesis. Each database is characterized by the data recorded during a specific motor task performed by different groups of subjects. The data mining techniques that were used in this thesis are feature selection (a technique which was used to find relevant information and to discard useless or redundant data), classification, clustering, and regression. The aims were to identify high risk subjects for PD, characterize the differences between early PD subjects and healthy ones, characterize PD subtypes and automatically assess the severity of symptoms in the home setting.

Ultra-low power WSNs: distributed signal processing and dynamic resource management

This thesis presents several data processing and compression techniques capable of addressing the strict requirements of wireless sensor networks. After introducing a general overview of sensor networks, the energy problem is introduced, dividing the different energy reduction approaches according to the different subsystem they try to optimize. To manage the complexity brought by these techniques, a quick overview of the most common middlewares for WSNs is given, describing in detail SPINE2, a framework for data processing in the node environment. The focus is then shifted on the in-network aggregation techniques, used to reduce data sent by the network nodes trying to prolong the network lifetime as long as possible. Among the several techniques, the most promising approach is the Compressive Sensing (CS). To investigate this technique, a practical implementation of the algorithm is compared against a simpler aggregation scheme, deriving a mixed algorithm able to successfully reduce the power consumption. The analysis moves from compression implemented on single nodes to CS for signal ensembles, trying to exploit the correlations among sensors and nodes to improve compression and reconstruction quality. The two main techniques for signal ensembles, Distributed CS (DCS) and Kronecker CS (KCS), are introduced and compared against a common set of data gathered by real deployments. The best trade-off between reconstruction quality and power consumption is then investigated. The usage of CS is also addressed when the signal of interest is sampled at a Sub-Nyquist rate, evaluating the reconstruction performance. Finally the group sparsity CS (GS-CS) is compared to another well-known technique for reconstruction of signals from an highly sub-sampled version. These two frameworks are compared again against a real data-set and an insightful analysis of the trade-off between reconstruction quality and lifetime is given.

Complex chemical dynamics through engineering-like methods

Most of the problems in modern structural design can be described with a set of equation; solutions of these mathematical models can lead the engineer and designer to get info during the design stage. The same holds true for physical-chemistry; this branch of chemistry uses mathematics and physics in order to explain real chemical phenomena. In this work two extremely different chemical processes will be studied; the dynamic of an artificial molecular motor and the generation and propagation of the nervous signals between excitable cells and tissues like neurons and axons. These two processes, in spite of their chemical and physical differences, can be both described successfully by partial differential equations, that are, respectively the Fokker-Planck equation and the Hodgkin and Huxley model. With the aid of an advanced engineering software these two processes have been modeled and simulated in order to extract a lot of physical informations about them and to predict a lot of properties that can be, in future, extremely useful during the design stage of both molecular motors and devices which rely their actions on the nervous communications between active fibres.

Sustainable processes using heterogeneous acid catalysts. Some examples of industrial interest.

In recent years the need for the design of more sustainable processes and the development of alternative reaction routes to reduce the environmental impact of the chemical industry has gained vital importance. Main objectives especially regard the use of renewable raw materials, the exploitation of alternative energy sources, the design of inherently safe processes and of integrated reaction/separation technologies (e.g. microreactors and membranes), the process intensification, the reduction of waste and the development of new catalytic pathways. The present PhD thesis reports results derived during a three years research period at the School of Chemical Sciences of Alma Mater Studiorum-University of Bologna, Dept. of Industrial Chemistry and Materials (now Dept. of Industrial Chemistry “Toso Montanari”), under the supervision of Prof. Fabrizio Cavani (Catalytic Processes Development Group). Three research projects in the field of heterogeneous acid catalysis focused on potential industrial applications were carried out. The main project, regarding the conversion of lignocellulosic materials to produce monosaccharides (important intermediates for production of biofuels and bioplatform molecules) was financed and carried out in collaboration with the Italian oil company eni S.p.A. (Istituto eni Donegani-Research Center for non-Conventional Energies, Novara, Italy) The second and third academic projects dealt with the development of green chemical processes for fine chemicals manufacturing. In particular, (a) the condensation reaction between acetone and ammonia to give triacetoneamine (TAA), and (b) the Friedel-Crafts acylation of phenol with benzoic acid were investigated.

Data protection by design in the e-health care sector: theoretical and applied perspectives

In the digital age, e-health technologies play a pivotal role in the processing of medical information. As personal health data represents sensitive information concerning a data subject, enhancing data protection and security of systems and practices has become a primary concern. In recent years, there has been an increasing interest in the concept of Privacy by Design, which aims at developing a product or a service in a way that it supports privacy principles and rules. In the EU, Article 25 of the General Data Protection Regulation provides a binding obligation of implementing Data Protection by Design technical and organisational measures. This thesis explores how an e-health system could be developed and how data processing activities could be carried out to apply data protection principles and requirements from the design stage. The research attempts to bridge the gap between the legal and technical disciplines on DPbD by providing a set of guidelines for the implementation of the principle. The work is based on literature review, legal and comparative analysis, and investigation of the existing technical solutions and engineering methodologies. The work can be differentiated by theoretical and applied perspectives. First, it critically conducts a legal analysis on the principle of PbD and it studies the DPbD legal obligation and the related provisions. Later, the research contextualises the rule in the health care field by investigating the applicable legal framework for personal health data processing. Moreover, the research focuses on the US legal system by conducting a comparative analysis. Adopting an applied perspective, the research investigates the existing technical methodologies and tools to design data protection and it proposes a set of comprehensive DPbD organisational and technical guidelines for a crucial case study, that is an Electronic Health Record system.

Machine learning as a service for high energy physics (MLaaS4HEP): a service for ML-based data analyses

With the CERN LHC program underway, there has been an acceleration of data growth in the High Energy Physics (HEP) field and the usage of Machine Learning (ML) in HEP will be critical during the HL-LHC program when the data that will be produced will reach the exascale. ML techniques have been successfully used in many areas of HEP nevertheless, the development of a ML project and its implementation for production use is a highly time-consuming task and requires specific skills. Complicating this scenario is the fact that HEP data is stored in ROOT data format, which is mostly unknown outside of the HEP community. The work presented in this thesis is focused on the development of a ML as a Service (MLaaS) solution for HEP, aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTP calls. These pipelines are executed by using the MLaaS4HEP framework, which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. Such a solution provides HEP users non-expert in ML with a tool that allows them to apply ML techniques in their analyses in a streamlined manner. Over the years the MLaaS4HEP framework has been developed, validated, and tested and new features have been added. A first MLaaS solution has been developed by automatizing the deployment of a platform equipped with the MLaaS4HEP framework. Then, a service with APIs has been developed, so that a user after being authenticated and authorized can submit MLaaS4HEP workflows producing trained ML models ready for the inference phase. A working prototype of this service is currently running on a virtual machine of INFN-Cloud and is compliant to be added to the INFN Cloud portfolio of services.

Big data analysis systems in IoE environments for managing privacy and data protection: pseudonymity, de-anonymization and the right to be forgotten

The thesis represents the conclusive outcome of the European Joint Doctorate programmein Law, Science & Technology funded by the European Commission with the instrument Marie Skłodowska-Curie Innovative Training Networks actions inside of the H2020, grantagreement n. 814177. The tension between data protection and privacy from one side, and the need of granting further uses of processed personal datails is investigated, drawing the lines of the technological development of the de-anonymization/re-identification risk with an explorative survey. After acknowledging its span, it is questioned whether a certain degree of anonymity can still be granted focusing on a double perspective: an objective and a subjective perspective. The objective perspective focuses on the data processing models per se, while the subjective perspective investigates whether the distribution of roles and responsibilities among stakeholders can ensure data anonymity.

Governing algorithms in the big data era for balancing new digital rights - designing GDPR compliant and trustworthy XAI systems

This thesis investigates the legal, ethical, technical, and psychological issues of general data processing and artificial intelligence practices and the explainability of AI systems. It consists of two main parts. In the initial section, we provide a comprehensive overview of the big data processing ecosystem and the main challenges we face today. We then evaluate the GDPR’s data privacy framework in the European Union. The Trustworthy AI Framework proposed by the EU’s High-Level Expert Group on AI (AI HLEG) is examined in detail. The ethical principles for the foundation and realization of Trustworthy AI are analyzed along with the assessment list prepared by the AI HLEG. Then, we list the main big data challenges the European researchers and institutions identified and provide a literature review on the technical and organizational measures to address these challenges. A quantitative analysis is conducted on the identified big data challenges and the measures to address them, which leads to practical recommendations for better data processing and AI practices in the EU. In the subsequent part, we concentrate on the explainability of AI systems. We clarify the terminology and list the goals aimed at the explainability of AI systems. We identify the reasons for the explainability-accuracy trade-off and how we can address it. We conduct a comparative cognitive analysis between human reasoning and machine-generated explanations with the aim of understanding how explainable AI can contribute to human reasoning. We then focus on the technical and legal responses to remedy the explainability problem. In this part, GDPR’s right to explanation framework and safeguards are analyzed in-depth with their contribution to the realization of Trustworthy AI. Then, we analyze the explanation techniques applicable at different stages of machine learning and propose several recommendations in chronological order to develop GDPR-compliant and Trustworthy XAI systems.