R. Maciel Filho

Production of bioethanol, methane and heat from sugarcane bagasse in a biorefinery concept

The potential of biogas production from the residues of second generation bioethanol production was investigated taking into consideration two types of pretreatment: lime or alkaline hydrogen peroxide. Bagasse was pretreated, enzymatically hydrolyzed and the wastes from pretreatment and hydrolysis were used to produce biogas. Results have shown that if pretreatment is carried out at a bagasse concentration of 4% DM, the highest global methane production is obtained with the peroxide pretreatment: 72.1 Lmethane/kgbagasse. The recovery of lignin from the peroxide pretreatment liquor was also the highest, 112.7 ± 0.01 g/kg of bagasse. Evaluation of four different biofuel production scenarios has shown that 63-65% of the energy that would be produced by bagasse incineration can be recovered by combining ethanol production with the combustion of lignin and hydrolysis residues, along with the anaerobic digestion of pretreatment liquors, while only 32-33% of the energy is recovered by bioethanol production alone.

ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process

Abstract This paper presents the development and the industrial implementation of a virtual sensor (soft-sensor) in the polyethylene terephthalate (PET) production process. This soft-sensor, based on a feed-forward artificial neural network (ANN), was primarily used to provide on-line estimates of the PET viscosity, which is necessary for process control purposes. The ANN-based soft-sensor (ANN-SS) was also used for providing redundant measurements of the viscosity that could be compared to the results obtained from the process viscometer. It was shown that the proposed ANN-SS was able to adequately infer the polymer viscosity, in such a way so as this soft-sensor could be used in the real-time process control strategy. The proposed control system has successfully been applied in servo and regulatory problems, thus allowing an effective and feasible operation of the industrial plant.

Processing modelling development through artificial neural networks and hybrid models

Abstract Developing fully mechanistic models for bioprocess is expensive and time-consuming. On the other hand, using pure ‘black-box’ approaches can lead to a misuse of available information, because there are aspects of the process that can be accurately described by simple equations as, for example, mass balances. This work analyses the use of different types of ‘black-box’ and hybrid models to outline the dynamics of a batch beer production. The hybrid models, combine mechanistic equations with ‘black-box’ techniques (reserved only for the unclear parts of the system), in order to achieve an efficient use of the available information. The hybrid models can also be called ‘grey-box’ approaches. To generate the hybrid models, different level of information is introduced into the ‘black-box’ models, allowing for an interesting model performance comparison in the end. Results demonstrate that the ‘black-box’ models present a good performance in the range of process conditions used to develop them. However, the inclusion of mechanistic knowledge into the hybrid models increase the model extrapolative capability. In this work, artificial neural networks (ANN) are used as the main technique for both the ‘black-box’ models and the ‘black-box’ parts in the hybrid models.

Rigorous modeling and simulation of molecular distillators: development of a simulator under conditions of non ideality of the vapor phase

Abstract In this work, a more rigorous model of the vapor phase was considered in characterizing the molecular distillation more realistically. The model used here tries to predict the behavior of the molecular distillation in terms of several factors that, in a considerable way, influence the evaporation efficiency, e.g. design of the molecular distillators in relation to the distance between the evaporator and the condenser and their geometries, pressure of the system, and condensation temperature. This model was developed in the literature by several authors. The objective here is to consider it in the dismol software (developed by the authors of this work) taking into account the main contributions available.

A computer aided tool for heavy oil thermal cracking process simulation

Abstract This work presents the model for the development of a computer aided tool for heavy oil thermal cracking process simulation. It is proposed a dual plug flow reactor representation for the light pyrolysis of petroleum distillation residues in coil-type reactors. The resulting reactor model consists of two parallel plug flows, one vapor and the other liquid, traveling at different speeds in a coil. Reaction is assumed to be the rate controlling process with equilibrium between the phases. Because of the pyrolysis reactions and pressure drop, vaporization takes place continuously along the coil so there is a decrease in the liquid hold-up. The model derived uses a heuristic lumping approach based on pilot plant data and the resulting pseudokinetic scheme presents a certain feed independence within the range of stocks available for the study. An industrial case study (delayed coking) is explored to provide insight into the problem of reconciling the kinetics of pyrolysis and carbonization for the upgrade of distillation residues.

Molecular Distillation Process for Recovering Biodiesel and Carotenoids from Palm Oil

Carotenoids and biodiesel from palm oil were recovered through a process involving neutralization and transesterification of palm oil followed by molecular distillation of the esters. The concentrated obtained contains more than 30,000 ppm of carotenoids and the distillate contains above 95% of light-colored biodiesel. The experimental data were obtained from falling film and centrifugal molecular distillators. It can be seen that each one has its own characteristics, which are a function of the operating temperatures and of the tendency of the material thermal decomposition. These characteristics can determine the type of equipment to be used, since they have different operating conditions. The experimental results were compared to the ones from simulations using the mathematical modeling for the falling film and centrifugal distillators developed.

Development of a complete model for an air-lift reactor

An 1D hydrodynamic model has been developed for gas hold-up and liquid circulation velocity prediction in air-lift reactors. The model is based on momentum balance equations and has been adjusted to experimental data collected on a pilot plant reactor equipped with two types of gas distributors and using water and water/butanol as the liquid phase. Agreement between the hydrodynamic model and pilot experimental points is shown to be fairly good. Different techniques of signal analysis have also been applied to pressure fluctuations in order to extract information about flow regimes and regime transitions. A good knowledge of the flow pattern is essential to establish adequate hydrodynamic correlations. This model has also been combined with mass transfer and the kinetics of a chemical reaction to yield a complete model of the performance of a reactor.

A HYBRID NEURAL MODEL FOR THE OPTIMIZATION OF FED-BATCH FERMENTATIONS

In this work a hybrid neural modelling methodology, which combines mass balance equations with functional link networks (FLNs), used to represent kinetic rates, is developed for bioprocesses. The simple structure of the FLNs allows the easy and rapid estimation of network weights and, consequently, the use of the hybrid model in an adaptive form. As the proposed model is able to adjust to kinetic and environmental changes, it is suitable for use in the development of optimization strategies for fed-batch bioreactors. The proposed methodology is used to model the processes for penicillin and ethanol production, and the development of an adaptive optimal control scheme is discussed using ethanol fermentation as an example.

Hierarchical fuzzy models within the framework of orthonormal basis functions and their application to bioprocess control

Fuzzy models within the framework of orthonormal basis functions (OBF fuzzy models) have been introduced in previous works and shown to be a very promising approach to the areas of nonlinear system identification and control, since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. As fuzzy models, however, they exhibit the dimensionality problem which is the main drawback to the application of neural networks and fuzzy systems to the modeling and control of large-scale systems. This problem has successfully been dealt with in the literature by means of hierarchical structures composed of submodels connected in cascade. In the present paper a hierarchical fuzzy model within the OBF framework is presented. A data-driven hybrid identification method based on genetic and gradient-based algorithms is described in details. A model-based predictive control scheme is also presented and applied to control of a complex industrial process for ethyl alcohol (ethanol) production.

A tuned approach of the predictive–adaptive GPC controller applied to a fed-batch bioreactor using complete factorial design

Abstract This work presents a novel tuned approach of the Generalized Predictive Control controller in both adaptive and nonadaptive configurations applied to a fed-batch penicillin process using the complete factorial design method. The controller stabilizes the dissolved oxygen concentration through agitation manipulation. The operating process variables were calculated by a mathematical model solved numerically. This new approach employed complete factorial design methodology to provide the optimal set of parameters, estimating the design parameters influence on the integral of the absolute error between the controlled variable and the set point (IAE). The controller parameters analyzed were prediction and control horizons, suppression factor, reference trajectory and integral factor. Moreover, the robustness performance was evaluated using the white noise presence in the measured variable, different polynomial orders of the internal model, several delay time and sampling periods of the controlled variable, and the presence of the internal polynomial. This controller performed better than PID and DMC controllers