Modeling, characterization and microalgal cultivation in an airlift panel photobioreactors.

Microalgae cultures are attracting great attentions in many industrial applications. However, one of the technical challenges is to cut down the capital and operational costs of microalgae production systems, with special difficulty in reactor design and scale-up. The thesis work open with an overview on the microalgae cultures as a possible answer to solve some of the upcoming planet issues and their applications in several fields. After the work offers a general outline on the state of the art of microalgae culture systems, taking a special look to the enclosed photobioreactors (PBRs). The overall objective of this study is to advance the knowledge of PBRs design and lead to innovative large scale processes of microalgae cultivation. An airlift flat panel photobioreactor was designed, modeled and experimentally characterized. The gas holdup, liquid flow velocity and oxygen mass transfer of the reactor were experimentally determined and mathematically modeled, and the performance of the reactor was tested by cultivation of microalgae. The model predicted data correlated well with experimental data, and the high concentration of suspension cell culture could be achieved with controlled conditions. The reactor was inoculated with the algal strain Scenedesmus obliquus sp. first and with Chlorella sp. later and sparged with air. The reactor was operated in batch mode and daily monitored for pH, temperature, and biomass concentration and activity. The productivity of the novel device was determined, suggesting the proposed design can be effectively and economically used in carbon dioxide mitigation technologies and in the production of algal biomass for biofuel and other bioproducts. Those research results favored the possibility of scaling the reactor up into industrial scales based on the models employed, and the potential advantages and disadvantages were discussed for this novel industrial design.

Multiphase flow in porous media: meta-modeling techniques for sensitivity analysis and risk assessment

One of the biggest challenges that contaminant hydrogeology is facing, is how to adequately address the uncertainty associated with model predictions. Uncertainty arise from multiple sources, such as: interpretative error, calibration accuracy, parameter sensitivity and variability. This critical issue needs to be properly addressed in order to support environmental decision-making processes. In this study, we perform Global Sensitivity Analysis (GSA) on a contaminant transport model for the assessment of hydrocarbon concentration in groundwater. We provide a quantification of the environmental impact and, given the incomplete knowledge of hydrogeological parameters, we evaluate which are the most influential, requiring greater accuracy in the calibration process. Parameters are treated as random variables and a variance-based GSA is performed in a optimized numerical Monte Carlo framework. The Sobol indices are adopted as sensitivity measures and they are computed by employing meta-models to characterize the migration process, while reducing the computational cost of the analysis. The proposed methodology allows us to: extend the number of Monte Carlo iterations, identify the influence of uncertain parameters and lead to considerable saving computational time obtaining an acceptable accuracy.

Kinetic modeling for the synthesis of ethyl levulinate

This work is focused on studying the kinetics of esterification of levulinic acid in an isothermal batch reactor using ethanol as a reactant and as a protic polar solvent at the same time and in the presence of an acid catalyst (sulfuric acid). The choice of solvent is important as it affects the kinetics and thermodynamics of the reaction system moreover, the knowledge of the reaction kinetics plays an important role in the design of the process. This work is divided into two stages; The first stage is the experimental part in which the experimental matrix was developed by changing the process variables one at a time (temperature, molar ratio between reactants, and catalyst concentration) in order to study their influence on the kinetics; the second stage is using the obtained data from the experiments to build the modeling part in order to estimate the thermodynamics parameters.

Bio-QA-GNN: Reasoning with Language Models and Knowledge Graphs for Interpretable Biomedical Question Answering

Nowadays the idea of injecting world or domain-specific structured knowledge into pre-trained language models (PLMs) is becoming an increasingly popular approach for solving problems such as biases, hallucinations, huge architectural sizes, and explainability lack—critical for real-world natural language processing applications in sensitive fields like bioinformatics. One recent work that has garnered much attention in Neuro-symbolic AI is QA-GNN, an end-to-end model for multiple-choice open-domain question answering (MCOQA) tasks via interpretable text-graph reasoning. Unlike previous publications, QA-GNN mutually informs PLMs and graph neural networks (GNNs) on top of relevant facts retrieved from knowledge graphs (KGs). However, taking a more holistic view, existing PLM+KG contributions mainly consider commonsense benchmarks and ignore or shallowly analyze performances on biomedical datasets. This thesis start from a propose of a deep investigation of QA-GNN for biomedicine, comparing existing or brand-new PLMs, KGs, edge-aware GNNs, preprocessing techniques, and initialization strategies. By combining the insights emerged in DISI's research, we introduce Bio-QA-GNN that include a KG. Working with this part has led to an improvement in state-of-the-art of MCOQA model on biomedical/clinical text, largely outperforming the original one (+3.63\% accuracy on MedQA). Our findings also contribute to a better understanding of the explanation degree allowed by joint text-graph reasoning architectures and their effectiveness on different medical subjects and reasoning types. Codes, models, datasets, and demos to reproduce the results are freely available at: \url{https://github.com/disi-unibo-nlp/bio-qagnn}.