Higher heating value estimation of wastes and fuels from ultimate and proximate analysis by using artificial neural networks.

Higher heating value (HHV) is one of the most important parameters in determining the quality of the fuels. In this study, comparatively large datasets of ultimate and proximate analysis are constructed to be used in HHV estimation of several classes of fuels, including char & fossil fuels, agricultural wastes, manure (chicken, cow, horse, sheep, llama, and pig), sludge (like paper, paper-mil, sewage, and pulp), micro/macro-algae's, wastes (RDF and MSW), treated woods, untreated woods, and others (non-fossil pyrolysis oils) between the HHV range of 4.22-55.55 MJ/kg. The relationships of carbon, hydrogen, and oxygen atomic ratios for fuel classes are illustrated by using ternary plots, and the effects of elemental composition on HHV was analyzed with the extensive dataset. Then, the ultimate (U) and ultimate & proximate (UP) datasets were utilized separately to estimate the HHV by using artificial neural networks (ANN). Hyperparameter optimization was carried out and the best performing ANNs were determined for each dataset, which yielded R2 values of 0.9719 and 0.9715, respectively. The results indicated that while ANNs trained by both datasets perform remarkably well, utilization of U dataset is sufficient for HHV estimation. Finally, the best performing ANN models for both U and UP datasets are given in a directly utilizable format enabling the accurate estimation of HHV of any fuel for optimization of fuel processing and waste management operations.

Development and characterization of edible films based on modified corn starch and grape juice.

Chemically modified corn starch with sodium trimetaphosphate (STMP) or citric acid (CA) and grape juice was used to produce edible films. Modification reactions were discussed by results of FT-IR scan, water solubility, swelling power, viscosity and degree of cross-linking properties. Mechanical, barrier, physical (solubility, color, transparency, microstructure) and glass transition temperature properties of films were studied to understand the effects of grape juice and modified starch usage in films. Usage of starch cross-linked with STMP decreased significantly oxygen permeability from 5.82 to 2.51 cm3 µm m-2 d-1 kPa-1, water vapor permeability from 1.89 to 1.38 g mm m-2 h-1 kPa-1, solubility from 0.65 to 0.55 g soluble solid/total solid, percent elongation from 62.96 to 16.47. The chemical reaction between starch and CA affected barrier, solubility and elongation properties of films and values were higher than values of STMP films.

The role of flexibility and optimality in the prediction of intracellular fluxes of microbial central carbon metabolism.

Prediction of intracellular metabolic fluxes based on optimal biomass assumption is a well-known computational approach. While there has been a significant emphasis on the optimality, cellular flexibility, the co-occurrence of suboptimal flux distributions in a microbial population, has hardly been considered in the related computational methods. We have implemented a flexibility-incorporated optimization framework to calculate intracellular fluxes based on a few extracellular measurement constraints. Taking into account slightly suboptimal flux distributions together with a dual-optimality framework (maximization of the growth rate followed by the minimization of the total enzyme amount) we were able to show the positive effect of incorporating flexibility and minimal enzyme consumption on the better prediction of intracellular fluxes of central carbon metabolism of two microorganisms: E. coli and S. cerevisiae.

Sparsity as cellular objective to infer directed metabolic networks from steady-state metabolome data: a theoretical analysis.

Since metabolome data are derived from the underlying metabolic network, reverse engineering of such data to recover the network topology is of wide interest. Lyapunov equation puts a constraint to the link between data and network by coupling the covariance of data with the strength of interactions (Jacobian matrix). This equation, when expressed as a linear set of equations at steady state, constitutes a basis to infer the network structure given the covariance matrix of data. The sparse structure of metabolic networks points to reactions which are active based on minimal enzyme production, hinting at sparsity as a cellular objective. Therefore, for a given covariance matrix, we solved Lyapunov equation to calculate Jacobian matrix by a simultaneous use of minimization of Euclidean norm of residuals and maximization of sparsity (the number of zeros in Jacobian matrix) as objective functions to infer directed small-scale networks from three kingdoms of life (bacteria, fungi, mammalian). The inference performance of the approach was found to be promising, with zero False Positive Rate, and almost one True positive Rate. The effect of missing data on results was additionally analyzed, revealing superiority over similarity-based approaches which infer undirected networks. Our findings suggest that the covariance of metabolome data implies an underlying network with sparsest pattern. The theoretical analysis forms a framework for further investigation of sparsity-based inference of metabolic networks from real metabolome data.