How to predict emissions of volatile organic compounds from solid building materials? A critical review on mass transfer models.

Volatile organic compounds (VOCs) emitting from solid building materials can cause adverse human health and environmental climate effects. It's more cost effective and powerful for mass-transfer emission models to describe the emission characteristic of VOCs than emission chamber studies. In this review, the existing main physical mechanism-based models for predicting VOCs emissions from dry solid building materials have been discussed, as well as their differences and similarities. Ignoring internal diffusion and porosity of solid materials, single-phase model is generally quite safe for use in actual condition. Conversely, porous media model is good for understanding VOC-transfer principles in porous materials. Additionally, the porous media model and the single-phase model can be transformed mutually because their model parameters are correlative. The availability of emission models is largely determined by the reliable and useful model parameters. Therefore, substantial technologies and novel methods have been developed for parameter estimation, which have also been reviewed in this paper. How to readily and rapidly obtain model parameters is a future development direction. In addition, applying emission models to predict and control VOCs emission from other solid waste materials is another future research prospect.

Process Kinetics of the Carbonation of Fly Ashes: A Research Study.

The aim of the paper is to present the results of research on the carbonation process kinetics of coal combustion ashes originating from fluidized bed boilers used in power plants. Based on the thermogravimetric analysis (TGA), the hypothesis that carbon dioxide is bounded by the mineral substances (calcium compounds) in the fly ashes was confirmed. Determining the kinetic parameters of the carbonation of fly ashes requires simultaneously taking into consideration the kinetics of the drying process of the sample. The drying process of the sample masks the effect of the reaction of CO2 with calcium compound. Unlike the ashes generated in pulverized fuel boilers, fly ashes contain irregular amorphic mineral components or poorly crystalized products of complete or partial dehydroxylation of claystone substance present in shale formations constituting the gangue as well as anhydrite (CaSO4), a desulfurization product. The content of free calcium oxide (CaO) in such ashes ranges from a few to several percent, which is a significant obstacle considering their use in cement and concrete production as type II admixtures understood to be inorganic grained materials of pozzolanic or latent hydraulic properties. The paper presents effective mechanisms which reduce the content of free CaO in ashes from Fluidized Bed Combustion (FBC) boilers to a level that allows their commercial utilization in the cement industry.

Modeling of COVID-19 propagation with compartment models.

The current pandemic is a great challenge for several research areas. In addition to virology research, mathematical models and simulations can be a valuable contribution to the understanding of the dynamics of the pandemic and can give recommendations to both physicians and politicians. In this paper we give an overview about mathematical models to describe the pandemic by differential equations. As a matter of principle the historic origin of the epidemic growth models will be remembered. Moreover we discuss models for the actual pandemic of 2020/2021. This will be done based on actual data of people infected with COVID-19 from the European Centre for Disease Prevention and Control (ECDC), input parameters of mathematical models will be determined and applied. These parameters will be estimated for the UK, Italy, Spain, and Germany and used in a SIR-type model. As a basis for the model's calibration, the initial exponential growth phase of the COVID-19 pandemic in the named countries is used. Strategies for the commencing and ending of social and economic shutdown measures are discussed. To respect heterogeneity of the people density in the different federal states of Germany diffusion effects are considered.

Generalized parameter estimation and calibration for biokinetic models using correlation and single variable optimisations: Application to sulfate reduction modelling in anaerobic digestion.

In this work, a generalized method for the estimation of biokinetic parameters in anaerobic digestion (AD) models is proposed. The method consists of a correlation-based approach to estimate specific groups of parameters mechanistically, followed by a sensitivity-based hierarchical and sequential single parameter optimisation (SHSSPO) calibration method for the remaining groups of parameters. The method was evaluated to estimate and calibrate the parameter values for sulfate reduction processes when included into the IWA Anaerobic Digestion Model No. 1 (ADM1) and simulations were compared with experimental data from literature. Under the proposed method, a large number of biokinetic parameters, namely biomass yields, maximum specific uptake rates, and half saturation constants, can first be estimated using mechanistic correlations. This achieves a significant reduction in the number of parameters to be fitted to data. For the remaining parameters, a method is proposed based on the overall sensitivity and degree of ubiquity of each parameter to establish a hierarchy in a sequential single parameter optimisation against the experimental data. This approach aims at eliminating the uncertainty on optimality (and therefore parameter identification) associated to multivariable parameter calibration problems. The method was applied to the sulfate reduction related parameters and led to the hydrogen sulfide inhibition parameters as the only ones requiring optimisation against experimental data. Comparison of the proposed SHSSPO performance with that of multi-dimensional parameter optimisation methods shows a superior performance in terms of overall error and computation times. Also, final simulation results led to model predictions of similar, if not better, quality than those achieved by multivariable parameter optimisation methods. The experimental variables optimized for included liquid effluent concentrations of sulfur species and volatile fatty acids as well as effluent methane gas flow. Overall, the proposed parameter estimation and calibration method provides a deterministic step-by-step approach to parameter estimation that decreases identifiability uncertainty at a very low computational effort. The results obtained suggest that the method could be generically applied with similar success to other biokinetic models frequently used in wastewater treatment.

Parameter Estimation of a Two-Colored Urn Model Class.

Though widely used in applications, reinforced random walk on graphs have never been the subject of a valid statistical inference. We develop in this paper a statistical framework for a general two-colored urn model. The probability to draw a ball at each step depends on the number of balls of each color and on a multidimensional parameter through a function, called choice function. We introduce two estimators of the parameter: the maximum likelihood estimator and a weighted least squares estimator which is less efficient, but is closer to the calibration techniques used in the applied literature. In general, the model is an inhomogeneous Markov chain and this property makes the estimation of the parameter impossible on a single path, even if it were infinite. Therefore we assume that we observe i.i.d. experiments, each of a predetermined finite length. This is coherent with the usual experimental set-ups. We apply the statistical framework to a real life experiment: the selection of a path among pre-existing channels by an ant colony. We performed experiments, which consisted of letting ants pass through the branches of a fork. We consider the particular urn model proposed by J.-L. Deneubourg et al. in 1990 to describe this phenomenon. We simulate this model for several parameter values in order to assess the accuracy of the MLE and the WLSE. Then we estimate the parameter from the experimental data and evaluate confident regions with Bootstrap algorithms. The findings of this paper do not contradict the biological literature, but give statistical significance to the values of the parameter found therein.