Modeling of fermentative hydrogen production from sweet sorghum extract based on modified ADM1

The Anaerobic digestion model 1 (ADM1) framework can be used to predict fermentative hydrogen production, since the latter is directly related to the acidogenic stage of the anaerobic digestion process. In this study, the ADM1 model framework was used to simulate and predict the process of fermentative hydrogen production from the extractable sugars of sweet sorghum biomass. Kinetic parameters for sugars’ consumption and yield coefficients of acetic, propionic and butyric acid production were estimated using the experimental data obtained from the steady states of a CSTR. Batch experiments were used for kinetic parameter validation. Since the ADM1 does not account for metabolic products such as lactic acid and ethanol that are crucial during the fermentative hydrogen production process, the structure of the model was modified to include lactate and ethanol among the metabolites and to improve the predictions. The modified ADM1 simulated satisfactorily batch experiments although further modifications could be made in order to further improve the predictions for the hydrogenogenic process.     

Effect of substrate concentration on fermentative hydrogen production from sweet sorghum extract

The aim of the present study was to assess the influence of substrate concentration on the fermentative hydrogen production from sweet sorghum extract, in a continuous stirred tank bioreactor. The reactor was operated at a Hydraulic Retention Time (HRT) of 12 h and carbohydrate concentrations ranging from 9.89 to 20.99 g/L, in glucose equivalents. The maximum hydrogen production rate and yield were obtained at the concentration of 17.50 g carbohydrates/L and were 2.93 ± 0.09 L H₂/L reactor/d and 0.74 ± 0.02 mol H₂/mol glucose consumed, corresponding to 8.81 ± 0.02 L H₂/kg sweet sorghum, respectively. The main metabolic product at all steady states was butyric acid, while ethanol production was high at high substrate concentrations. The experiments showed that hydrogen productivity depends significantly on the initial carbohydrate concentration, which also influences the distribution of the metabolic products.     

Information and competitive equilibria in the housing market

This paper derives necessary and sufficient conditions for the existence and occurrence likelihood of competitive equilibria in urban land and housing markets: (i) when there is imperfect information causing costly search trips and (ii) when land and housing are differentiated according to between‐zone and within‐zone heterogeneity. Such competitive equilibria are contingent on the possibility that consumers spill over into the markets of less preferred zones. The main conclusions are that markets tend to be partitioned into homogeneous sub‐groups and when they do not, heterogeneity may hamper the effectiveness of search and hence work against the existence of competitive equilibria.     

Classical and alternative fuel mix optimization in cement production using mathematical programming

In this paper a systematic methodology is presented for the simultaneous optimal selection of raw materials, fossil fuels and alternative fuels in cement production. The aim is to offer a generic mathematical formulation that can be used as the basis for developing case specific mathematical formulations that can assist the strategic decision-making process. The mathematical model presented takes into consideration the essential elements of a cement plant operation. The final formulation is a mixed integer linear programming problem that aims at minimizing the overall operating cost. A realistic case study is presented which demonstrates the usefulness of the proposed mathematical programming methodology.     

Neural Structures Using the Eigenstates of a Quantum Harmonic Oscillator

The main result of the paper is the use of orthogonal Hermite polynomials as the basis functions of feedforward neural networks. The proposed neural networks have some interesting properties: (i) the basis functions are invariant under the Fourier transform, subject only to a change of scale, (ii) the basis functions are the eigenstates of the quantum harmonic oscillator, and stem from the solution of Schrödinger's diffusion equation. The proposed feed-forward neural networks demonstrate the particle-wave nature of information and can be used in nonparametric estimation. Possible applications of the proposed neural networks include function approximation, image processing and system modelling.