Marcio Schwaab

Recent advances on biobutanol production

Recent studies have shown that butanol is a potential gasoline replacement that can also be blended in significant quantities with conventional diesel fuel. However, biotechnological production of butanol has some challenges such as low butanol titer, high cost feedstocks and product inhibition. The present work reviewed the technical and economic feasibility of the main technologies available to produce biobutanol. The latest studies integrating continuous fermentation processes with efficient product recovery and the use of mathematical models as tools for process scale-up, optimization and control are presented.

A comparison among stochastic optimization algorithms for parameter estimation of biochemical kinetic models

Mathematical models in biochemical engineering field are usually composed by nonlinear kinetic equations, where the number of parameters that must be estimated from a set of experimental measurements is usually very high. In these cases, the estimation of the model parameters comprises numerical iterative methods for minimization of the objective function. Classical methods for minimization of the objective function, like the Newton method, requires a good initial guess for all parameters and differentiation of the objective function and/or model equations with respect to the model parameters. Besides, the use of stochastic optimization methods for parameter estimation has gained attention, since these methods do not require a good initial guesses of all model parameters and neither the evaluation of derivatives. In this work, some stochastic optimization methods (Artificial Bee Colony, Differential Evolution, Particle Swarm Optimization and Simulated Annealing) were used in the estimation of kinetic parameters of a biochemical model for an alcoholic fermentation of cassava hydrolyzed. The results indicated that Differential Evolution provides better results among the stochastic optimization methods evaluated.

Statistical Analysis of Linear and Non-Linear Regression for the Estimation of Adsorption Isotherm Parameters

A very common practice during the parameter estimation of adsorption isotherms, including the well-known Langmuir and Freundlich isotherms, consists in manipulating the isotherm equation to obtain a linear equation and estimate the model parameters using a linear least squares method. This procedure is also usually used for estimating the thermodynamic adsorption parameters, despite the fact that personal computers and software are available for prompt implementation of non-linear solutions of the original parameter-estimation problem. For this reason, the main purpose of this work is to show that the use of linear least-squares methods for estimating adsorption isotherm parameters leads to some serious drawbacks, which can be readily avoided through proper use of non-linear procedures and posterior statistical analyses of the parameter-estimation results, enhancing the quality of the obtained results.

Use of papaya seeds as a biosorbent of methylene blue from aqueous solution

In this study papaya seeds were used to remove methylene blue dye from aqueous solution. Papaya seeds were characterized as possessing a macro/mesoporous texture and large pore size. Studies were carried out in batches to evaluate the effect of contact time and pH (2-12) on the removal of dye. It was observed that the adsorption of dye was better in the basic region (pH 12). The equilibrium data were analyzed using Langmuir, Freundlich, Dubinin-Raduschkevich, Tempkin, Jovanovich, Redlich-Peterson, Sips, Toth and Radke-Prausnitz isotherms. The equilibrium data were best described by the Langmuir isotherm with a maximum adsorption capacity of 637.29 mg g(-1). Adsorption kinetic data were fitted using the pseudo-first-order and pseudo-second-order model. The adsorption kinetic is very fast and was best described by the pseudo-second-order model.

Critical Analysis of Kinetic Modeling Procedures

In this work, issues related to the mathematical modeling and statistical analyses of kinetic data are discussed. Firstly, problems related to the combinatorial explosion of the number of plausible kinetic models are analyzed, when complex reaction mechanisms are taken into consideration and distinct rate determining steps are assumed. Although modeling procedures based on rate-determining steps can lead to oversimplification of kinetic models, these procedures are still very popular because the existence of multiple rate-determining steps usually renders the analytical derivation of kinetic rate expressions impossible. However, if the derived kinetic models are too simple, one can face serious difficulties to fit the proposed models to available experimental data. Secondly, problems related to the statistical analyses of experimental data are discussed. Particularly, very often statistical tools are used even when some of the fundamental assumptions required for their validity are violated. For this reason, the fundamental grounds that support some of the most popular statistical tools are discussed in the framework of the kinetic analysis.

Mathematical Modeling of Thin Layer Drying of Papaya Seeds in a Tunnel Dryer Using Particle Swarm Optimization Method

The processing of papaya generates a significant amount of solid wastes. The seeds represent a significant amount and a source of untapped products. Papaya seeds have useful compounds, which can be obtained from its extracts or oils and can be a very effective biosorbent. For good results in the compounds achievement and the pretreatment, it is essential the appropriated choice of the drying conditions. This article proposes the study of the drying of papaya seeds in a tunnel dryer at three drying temperatures (40, 70, and 100°C) and three air drying velocities (1.0, 1.5, and 2.0 m · s−1). Empirical and semi-empirical models were proposed to adjust the kinetic parameter as a function of the drying conditions. The particle swarm optimization method was used, obtaining, as a result, an exponential model with good prediction quality and with few parameters to be adjusted.

Statistical Evaluation of Non-Linear Parameter Estimation Procedures for Adsorption Equilibrium Models

Adsorption equilibrium is a fundamental concept in the adsorption science and relates the equilibrium between the quantity of the adsorbed material and its concentration in the bulk phase. Several models have been proposed for prediction of adsorption equilibrium and all models depend on parameters whose values must be estimated from available experimental data. Although linear parameter estimation procedures can be used for model fitting, through transformation of available experimental data and model parameters, non-linear parameter estimation procedures lead to more reliable results and allow for direct comparison of results obtained with different adsorption equilibrium models. The main objective of this work is to present and compare different non-linear procedures for parameter estimation of adsorption equilibrium models, based on theoretical arguments and also on the numerical estimation of adsorption equilibrium parameters, using available experimental data for adsorption of methylene blue onto activated carbon. The results obtained indicate that the best parameter estimation procedure is the one that relies on available equilibrium concentrations in the bulk phase as a function of the fluid volume, adsorbent mass and initial concentrations in the bulk phase, without transformation of measured experimental values and model parameters. Besides, it is shown that parameter estimates should be obtained through proper minimization of weighted least-squares objective function, in accordance with maximum likelihood procedures.

Parameter estimation and statistical methods

Abstract Parameter estimation procedures are very important in the chemical engineering field for development of mathematical models, since design, optimization, and advanced control of chemical processes usually rely on mathematical models, which in turn depend on parameter values (and respective uncertainties) obtained with help of available experimental data. For this reason, the parameter estimation problem is introduced here and discussed in terms of its three fundamental steps: the definition of the objective function, the minimization of the objective function, and the statistical analysis of the obtained results.