Masks thermal degradation as an alternative of waste valorization on the COVID-19 pandemic: A kinetic study.

The COVID-19 pandemic generated a new dynamic around waste management. Personal protective equipment such as masks, gloves, and face shields were essential to prevent the spread of the disease. However, despite the increase in waste, no technical alternatives were foreseen for the recovery of these wastes, which are made up of materials that can be valued for energy recovery. It is essential to design processes such as waste to energy to promote the circular economy. Therefore, techniques such as pyrolysis and thermal oxidative decomposition of waste materials need to be studied and scaled up, for which kinetic models and thermodynamic parameters are required to allow the design of this reaction equipment. This work develops kinetic models of the thermal degradation process by pyrolysis as an alternative for energy recovery of used masks generated by the COVID-19 pandemic. The wasted masks were isolated for 72 h for virus inactivation and characterized by FTIR-ATR spectroscopy, elemental analysis, and determinate the higher calorific value (HCV). The composition of the wasted masks included polypropylene, polyethylene terephthalate, nylon, and spandex, with higher calorific values than traditional fuels. For this reason, they are susceptible to value as an energetic material. Thermal degradation was performed by thermogravimetric analysis at different heating rates in N2 atmosphere. The gases produced were characterized by gas chromatography and mass spectrometry. The kinetic model was based on the mass loss of the masks on the thermal degradation, then calculated activation energies, reaction orders, pre-exponential factors, and thermodynamic parameters. Kinetics models such as Coats and Redfern, Horowitz and Metzger, Kissinger-Akahira-Sunose were studied to find the best-fit models between the experimental and calculated data. The kinetic and thermodynamic parameters of the thermal degradation processes demonstrated the feasibility and high potential of recovery of these residues with conversions higher than 89.26% and obtaining long-chain branched hydrocarbons, cyclic hydrocarbons, and CO2 as products.

Application of Machine Learning Techniques to an Agent-Based Model of Pantoea.

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea. We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py. Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R 2 coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

INDISIM-Denitrification, an individual-based model for study the denitrification process.

Denitrification is one of the key processes of the global nitrogen (N) cycle driven by bacteria. It has been widely known for more than 100 years as a process by which the biogeochemical N-cycle is balanced. To study this process, we develop an individual-based model called INDISIM-Denitrification. The model embeds a thermodynamic model for bacterial yield prediction inside the individual-based model INDISIM and is designed to simulate in aerobic and anaerobic conditions the cell growth kinetics of denitrifying bacteria. INDISIM-Denitrification simulates a bioreactor that contains a culture medium with succinate as a carbon source, ammonium as nitrogen source and various electron acceptors. To implement INDISIM-Denitrification, the individual-based model INDISIM was used to give sub-models for nutrient uptake, stirring and reproduction cycle. Using a thermodynamic approach, the denitrification pathway, cellular maintenance and individual mass degradation were modeled using microbial metabolic reactions. These equations are the basis of the sub-models for metabolic maintenance, individual mass synthesis and reducing internal cytotoxic products. The model was implemented in the open-access platform NetLogo. INDISIM-Denitrification is validated using a set of experimental data of two denitrifying bacteria in two different experimental conditions. This provides an interactive tool to study the denitrification process carried out by any denitrifying bacterium since INDISIM-Denitrification allows changes in the microbial empirical formula and in the energy-transfer-efficiency used to represent the metabolic pathways involved in the denitrification process. The simulator can be obtained from the authors on request.

INDISIM-Paracoccus, an individual-based and thermodynamic model for a denitrifying bacterium.

We have developed an individual-based model for denitrifying bacteria. The model, called INDISIM-Paracoccus, embeds a thermodynamic model for bacterial yield prediction inside the individual-based model INDISIM, and is designed to simulate the bacterial cell population behavior and the product dynamics within the culture. The INDISIM-Paracoccus model assumes a culture medium containing succinate as a carbon source, ammonium as a nitrogen source and various electron acceptors such as oxygen, nitrate, nitrite, nitric oxide and nitrous oxide to simulate in continuous or batch culture the different nutrient-dependent cell growth kinetics of the bacterium Paracoccus denitrificans. The individuals in the model represent microbes and the individual-based model INDISIM gives the behavior-rules that they use for their nutrient uptake and reproduction cycle. Three previously described metabolic pathways for P. denitrificans were selected and translated into balanced chemical equations using a thermodynamic model. These stoichiometric reactions are an intracellular model for the individual behavior-rules for metabolic maintenance and biomass synthesis and result in the release of different nitrogen oxides to the medium. The model was implemented using the NetLogo platform and it provides an interactive tool to investigate the different steps of denitrification carried out by a denitrifying bacterium. The simulator can be obtained from the authors on request.