Estimating a Stoichiometric Solid’s Gibbs Free Energy Model by Means of a Constrained Evolutionary Strategy

Modeling of thermodynamic properties, like heat capacities for stoichiometric solids, includes the treatment of different sources of data which may be inconsistent and diverse. In this work, an approach based on the covariance matrix adaptation evolution strategy (CMA-ES) is proposed and described as an alternative method for data treatment and fitting with the support of data source dependent weight factors and physical constraints. This is applied to a Gibb’s Free Energy stoichiometric model for different magnesium sulfate hydrates by means of the NASA9 polynomial. Its behavior is proved by: (i) The comparison of the model to other standard methods for different heat capacity data, yielding a more plausible curve at high temperature ranges; (ii) the comparison of the fitted heat capacity values of MgSO4·7H2O against DSC measurements, resulting in a mean relative error of a 0.7% and a normalized root mean square deviation of 1.1%; and (iii) comparing the Van’t Hoff and proposed Stoichiometric model vapor-solid equilibrium curves to different literature data for MgSO4·7H2O, MgSO4·6H2O, and MgSO4·1H2O, resulting in similar equilibrium values, especially for MgSO4·7H2O and MgSO4·6H2O. The results show good agreement with the employed data and confirm this method as a viable alternative for fitting complex physically constrained data sets, while being a potential approach for automatic data fitting of substance data.

Thermodynamic Analysis of Biomass Gasification Using Aspen Plus: Comparison of Stoichiometric and Non-Stoichiometric Models

The gasification process involves several reactions that occur simultaneously and are interrelated by several independent variables. Simulation tools can help us to understand the process behaviour and predict the efficiency and final composition of the products. In this work, two thermodynamic equilibrium models developed in Aspen Plus® software were assessed: a non-stoichiometric model based on the feedstock composition and on the most probable compounds expected from the results of the gasification process using minimisation of Gibbs free energy and a stoichiometric model based on a set of chemical reactions considered as the most relevant to describe the gasification process. Both models were validated with experimental data from a bubbling fluidised bed semi-pilot scale gasifier using pine kernel shells (PKS) as feedstock. The influence of temperature, stoichiometric ratio (SR) and steam to biomass ratio (SBR) were analysed. Overall, predictions of the gas composition and gasification efficiency parameters by the stoichiometric model showed better agreement to the experimental results. Our results point out the significance of an accurate description of the equilibrium composition of producer gas with the stoichiometric model for the gasification of biomass.

A unifying modelling formalism for the integration of stoichiometric and kinetic models

Current research on systems and synthetic biology relies heavily on mathematical models of the systems under study. The usefulness of such models depends on the quantity and quality of biological data, and on the availability of appropriate modelling formalisms that can gather and accommodate such data so that they can be exploited properly. Given our incomplete knowledge of biological systems and the fact that they consist of many subsystems, biological data are usually uncertain and heterogeneous. These facts hinder the use of mathematical models and computational methods. In the scope of dynamic biological systems, e.g. metabolic networks, this difficulty can be overcome by the novel modelling formalism of flexible nets (FNs). We show that an FN can combine, in a natural way, a stoichiometric model and a kinetic model. Moreover, the resulting net admits nonlinear dynamics and can be analysed in both transient and steady states.

Dynamics of nitrogen-fixing cyanobacteria with heterocysts: a stoichiometric model

A simulation model for nitrogen-fixing cyanobacteria was formulated to predict population and nutrient dynamics in water quality studies. The model tracks population biomasses of nitrogen and phosphorus, which potentially limit population growth. Lack of intracellular nitrogen cues the differentiation of specialised heterocysts for nitrogen fixation. Ecoevolutionary analysis presented here predicts that natural selection optimises heterocyst differentiation in relation to external supplies of nitrogen and phosphorus. Modelling the production of N-rich toxins (e.g. anatoxins, saxitoxins) suggests that both total biomass and the biomass N:P ratio can predict concentrations of toxins. The results suggest hypotheses that major taxa of nitrogen-fixing, nuisance cyanobacteria are differentially adapted to varying nitrogen and phosphorus supplies, and that biomass stoichiometry is related to toxins production in this major group of harmful algae. This approach can be extended into models of community and ecosystem dynamics to explore implications of nitrogen fixation for cyanobacterial biomass and toxins production.

A Highly Selective Fluorescent Chemosensor for Al3+ Based on 2,2':6',2''-Terpyridine with a Salicylal Schiff Base

Two 2,2':6',2''-terpyridine-based Schiff bases (TPySSB and TPySB) have been synthesized. The TPySSB shows remarkable selective ‘off-on’ fluorescence for Al3+ by photoinduced electron transfer (PET) mechanism of sensing. Chemosensor TPySSB binds Al3+ in a 1:2 ratio with an association constant 6.8×105 (R2=0.98) and this 1:2 stoichiometric model is established on Job’s plot and 1H NMR. Compared TPySSB and TPySB, it is of great importance of the existence of salicylidene unit due to its strong binding abilities of both phenol and C=N structure to the Al3+.