Use of RSM for the multivariate, simultaneous multiobjective optimization of the operating conditions of aliphatic carboxylic acids ion-exclusion chromatography column: Quantitative study of hydrodynamic, isotherm, and thermodynamic behavior.

The present study evaluates the capability of ion exclusion chromatography (IEC) of short chain aliphatic carboxylic acids using a cation exchange column (8% sulfonated cross-linked styrene-divinylbenzene copolymer) in different experimental conditions. Since one of the prerequisites to the development of an efficient carboxylic acid separation process is to obtain the optimum operational conditions, response surface methodology (RSM) was used to develop an approach to evaluate carboxylic acids separation process in IEC columns. The effect of the operating conditions such as column temperature, sulfuric acid concentration as the mobile phase, and the flow rate was studied using Central Composite Face (CCF) design. The optimum operating conditions for the separate injection of lactic acid and acetic acid is temperature of 75 °C, sulfuric acid concentration of 0.003 N for both acids and flow rate of 0.916 (0.886) mL/min for acetic acid (lactic acid). Likewise, the optimum conditions for the simultaneous injection of acetic and lactic acid mixture are the column temperature of 68 °C, sulfuric acid concentration of 0.0003 N, and flow rate of 0.777 mL/min. In the next step, the adsorption equilibria of acetic acid and lactic acid on the stationary phase were investigated through a series of Frontal Analysis (FA), Frontal Analysis by Characteristic Points (FACP), and using Langmuir isotherm model. The results showed an excellent agreement between the model and experimental data. Finally, the results of thermodynamic studies proved that the IEC process for separation of acetic and lactic acid is a spontaneous, feasible, exothermic, and random process with a physical adsorption mechanism. The results of the current paper can be a valuable information in the stages of designing IEC columns for separation of aliphatic carboxylic acids.

Model-based identifiable parameter determination applied to a simultaneous saccharification and fermentation process model for bio-ethanol production.

In this work, a methodology for the model-based identifiable parameter determination (MBIPD) is presented. This systematic approach is proposed to be used for structure and parameter identification of nonlinear models of biological reaction networks. Usually, this kind of problems are over-parameterized with large correlations between parameters. Hence, the related inverse problems for parameter determination and analysis are mathematically ill-posed and numerically difficult to solve. The proposed MBIPD methodology comprises several tasks: (i) model selection, (ii) tracking of an adequate initial guess, and (iii) an iterative parameter estimation step which includes an identifiable parameter subset selection (SsS) algorithm and accuracy analysis of the estimated parameters. The SsS algorithm is based on the analysis of the sensitivity matrix by rank revealing factorization methods. Using this, a reduction of the parameter search space to a reasonable subset, which can be reliably and efficiently estimated from available measurements, is achieved. The simultaneous saccharification and fermentation (SSF) process for bio-ethanol production from cellulosic material is used as case study for testing the methodology. The successful application of MBIPD to the SSF process demonstrates a relatively large reduction in the identified parameter space. It is shown by a cross-validation that using the identified parameters (even though the reduction of the search space), the model is still able to predict the experimental data properly. Moreover, it is shown that the model is easily and efficiently adapted to new process conditions by solving reduced and well conditioned problems.

Optimal determination of steric mass action model parameters for beta-lactoglobulin using static batch experiments.

In this work, parameters of the steric mass-formalism SMA are optimally ascertained for a reliable determination of the adsorption isotherms of beta-lactoglobulin A and B under non-isocratic conditions. For this purpose, static batch experiments are used in contrast to the protocols based on different experimental steps, which use a chromatographic column. It is shown that parameters can already be determined for a small number of experiments by using a systematic procedure based on optimal model-based experimental design and an efficient NLP-solver. The in different works observed anti-Langmuir shape of the isotherm for small concentrations of beta-lactoglobulin A was corroborated. Moreover, we also found indications for a porosity variation with changing protein concentrations.

Modeling and parameter identification of the simultaneous saccharification-fermentation process for ethanol production.

Despite many environmental advantages of using alcohol as a fuel, there are still serious questions about its economical feasibility when compared with oil-based fuels. The bioethanol industry needs to be more competitive, and therefore, all stages of its production process must be simple, inexpensive, efficient, and "easy" to control. In recent years, there have been significant improvements in process design, such as in the purification technologies for ethanol dehydration (molecular sieves, pressure swing adsorption, pervaporation, etc.) and in genetic modifications of microbial strains. However, a lot of research effort is still required in optimization and control, where the first step is the development of suitable models of the process, which can be used as a simulated plant, as a soft sensor or as part of the control algorithm. Thus, toward developing good, reliable, and simple but highly predictive models that can be used in the future for optimization and process control applications, in this paper an unstructured and a cybernetic model are proposed and compared for the simultaneous saccharification-fermentation process (SSF) for the production of ethanol from starch by a recombinant Saccharomyces cerevisiae strain. The cybernetic model proposed is a new one that considers the degradation of starch not only into glucose but also into dextrins (reducing sugars) and takes into account the intracellular reactions occurring inside the cells, giving a more detailed description of the process. Furthermore, an identification procedure based on the Metropolis Monte Carlo optimization method coupled with a sensitivity analysis is proposed for the identification of the model's parameters, employing experimental data reported in the literature.