Publications by authors named "Maria Rosaria Mattei"

In many applications, complex biological phenomena can be reproduced via structured mathematical models, which depend on numerous biotic and abiotic input parameters, whose effect on model outputs can be of paramount importance. The calibration of model parameters is crucial to obtain the best fit between simulated and experimental data. Sensitivity analysis and uncertainty quantification constitute essential tools in the field of biological systems modeling.

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Article Synopsis
  • The study investigates how trace metals (TMs) can enhance the performance of anaerobic digestion (AD) to increase methane production through various dosing strategies.
  • The researchers used a model-based approach to evaluate different dosing methods, including continuous, preloading, pulse dosing, and in-situ loading.
  • The findings highlight that repeated pulse dosing at low concentrations (specifically, 5 µM every 5 days) optimally maximizes methane yield while minimizing metal loss, with the ideal dosing form varying based on reactor setup.
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In this study, we present a mathematical model for plasmid spread in a growing biofilm, formulated as a nonlocal system of partial differential equations in a 1-D free boundary domain. Plasmids are mobile genetic elements able to transfer to different phylotypes, posing a global health problem when they carry antibiotic resistance factors. We model gene transfer regulation influenced by nearby potential receptors to account for recipient-sensing.

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Dosing trace metals into anaerobic digestors is proven to improve biogas production rate and yield by stimulating microorganisms involved in the metabolic pathways. Trace metal effects are governed by metal speciation and bioavailability. Though chemical equilibrium speciation models are well-established and widely used to understand metal speciation, the development of kinetic models considering biological and physicochemical processes has recently gained attention.

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A multiscale mathematical model describing the metals biosorption on algal-bacterial photogranules within a sequencing batch reactor (SBR) is presented. The model is based on systems of partial differential equations (PDEs) derived from mass conservation principles on a spherical free boundary domain with radial symmetry. Hyperbolic PDEs account for the dynamics of sessile species and their free sorption sites, where metals are adsorbed.

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Microfiltration is a widely used engineering technology for fresh water production and water treatment. The major concern in many applications is the formation of a biological fouling layer leading to increased hydraulic resistance and flux decline during membrane operations. The growth of bacteria constituting such a biological layer implicates the formation of a multispecies biofilm and the consequent increase of operational costs for reactor management and cleaning procedures.

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This work proposes a mathematical model on partial nitritation/anammox (PN/A) granular bioreactors, with a particular interest in the start-up phase. The formation and growth of granular biofilms is modelled by a spherical free boundary problem with radial symmetry and vanishing initial value. Hyperbolic PDEs describe the advective transport and growth of sessile species inhabiting the granules.

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The aim of this study was to develop a mathematical model to assess the effect of soluble microbial products production and external carbon source addition on the performance of a sulfur-driven autotrophic denitrification (SdAD) process. During SdAD, the growth of autotrophic biomass (AUT) was accompanied by the proliferation of heterotrophic biomass mainly consisting of heterotrophic denitrifiers (HD) and sulfate-reducing bacteria (SRB), which are able to grow on both the SMP derived from the microbial activities and on an external carbon source. The process was supposed to occur in a sequencing batch reactor to investigate the effects of the COD injection on both heterotrophic species and to enhance the production and consumption of SMP.

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In this work, an original mathematical model for metals leaching from electronic waste in a dark fermentation process is proposed. The kinetic model consists of a system of non-linear ordinary differential equations, accounting for the main biological, chemical, and physical processes occurring in the fermentation of soluble biodegradable substrates and in the dissolution process of metals. Ad-hoc experimental activities were carried out for model calibration purposes, and all experimental data were derived from specific lab-scale tests.

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Due to the multiplicity of biogeochemical processes taking place in anaerobic digestion (AD) systems and limitations of the available analytical techniques, assessing the bioavailability of trace elements (TEs) is challenging. Determination of TE speciation can be facilitated by developing a mathematical model able to consider the physicochemical processes affecting TEs dynamics. A modeling framework based on anaerobic digestion model no 1 (ADM1) has been proposed to predict the biogeochemical fate TEs in AD environments.

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In this study, a new model based on anaerobic digestion model no.1 (ADM1) approach has been proposed to simulate trace elements (TEs) complexation, precipitation and their effect on the anaerobic batch methane production. TEs complexation reactions with VFAs and EDTA have been incorporated in an extended ADM1 model which considers TE precipitation/dissolution reactions as well as biodegradation processes.

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Due to the complex biogeochemistry of trace elements (TEs, e.g. Fe, Ni and Co) in anaerobic digestion processes, their role and fate is poorly understood.

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The hydrolysis of elemental sulfur (S) coupled to S-based denitrification and denitritation was investigated in batch bioassays by microbiological and modeling approaches. In the denitrification experiments, the highest obtained NO-N removal rate was 20.9 mg/l·d.

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Synopsis of recent research by authors named "Maria Rosaria Mattei"

  • - Maria Rosaria Mattei's research primarily focuses on the mathematical modeling of bioprocesses, particularly in the fields of anaerobic digestion and biofilm dynamics, aiming to improve efficiency and understand complex biological interactions involved in these systems
  • - Recent findings highlight the favorable impacts of trace metal dosing strategies on methane yield in anaerobic digestion, alongside novel models for gene transfer in biofilms, emphasizing the quantitative assessment of microbial interactions and species dynamics
  • - Mattei has contributed to significant developments in modeling methodologies, such as multi-scale and non-local differential equations, providing insights into microbial behavior, metal biosorption, and the optimization of biotechnological processes across various applications, including wastewater treatment and e-waste recycling