Abstract An attractive operation strategy for the enzymatic hydrolysis of lignocellulosics results from dividing the process into three stages with complementary goals: continuous enzyme adsorption at low-solids loading (5% w/w) with recycling of the liquid phase; continuous liquefaction at high-solids content (up to 20% w/w); and, finally, continuous or semicontinuous hydrolysis with supplementation of fresh enzymes. This paper presents a detailed modeling and simulation framework for the aforementioned operation strategies. The limiting micromixing situations of macrofluid and microfluid are used to predict conversions. The adsorption and liquefaction stages are modeled as a continuous stirred tank and a plug flow reactor, respectively. Two alternatives for the third stage are studied: a train of five cascading stirred tanks and a battery of batch reactors in parallel. Simulation results show that glucose concentrations greater than 100 g L-1 could be reached with both of the alternatives for the third stage.

This study is aimed at comparing two traditional methods of drying of enzymes and at verifying the efficiency of each one and their advantages and disadvantages. The experiments were performed with a laboratory spray dryer and freeze-dryer using α-amylase as the model enzyme. An experimental design in star revealed that spray drying is mainly influenced by the inlet air temperature and feed flow rate, which were considered to be the main factors influencing the enzymatic activity and water activity; the long period of material exposure to high temperatures causes a partial activity loss. In the experiments of freeze drying, three methods of freezing were used (freezer, acetone and dry ice, and liquid nitrogen) and samples subsequently freeze-dried for times ranging between 0-24 hours. The product obtained from the two techniques showed high enzymatic activity and low water activity. For the drying of heat-resistant enzymes, in which the product to be obtained does not have high added value, spray drying may be more economically viable because, in the freeze drying process, the process time can be considered as a limiting factor when choosing a technique.

Environmental impact assessment is becoming indispensable for the design and operation of chemical plants. Structured and consistent methods for this purpose have experienced a rapid development. The more rigorous and sophisticated these methods become, the greater is the demand for convenient tools. On the other hand, despite the incredible advances in process simulators, some aspects have still not been sufficiently covered. To date, applications of these programs to quantify environmental impacts have been restricted to straightforward examples of steady-state processes. In this work, a life-cycle assessment implementation with the aim of process design will be described, with a brief discussion of a dynamic simulation for analysis of transient state operations, such as process start-up. A case study shows the importance of this analysis in making possible operation at a high performance level with reduced risks to the environment.

Most advanced computer-aided control applications rely on good dynamics process models. The performance of the control system depends on the accuracy of the model used. Typically, such models are developed by conducting off-line identification experiments on the process. These experiments for identification often result in input-output data with small output signal-to-noise ratio, and using these data results in inaccurate model parameter estimates [1]. In this work, a multivariable adaptive self-tuning controller (STC) was developed for a biotechnological process application. Due to the difficulties involving the measurements or the excessive amount of variables normally found in industrial process, it is proposed to develop "soft-sensors" which are based fundamentally on artificial neural networks (ANN). A second approach proposed was set in hybrid models, results of the association of deterministic models (which incorporates the available prior knowledge about the process being modeled) with artificial neural networks. In this case, kinetic parameters - which are very hard to be accurately determined in real time industrial plants operation - were obtained using ANN predictions. These methods are especially suitable for the identification of time-varying and nonlinear models. This advanced control strategy was applied to a fermentation process to produce ethyl alcohol (ethanol) in industrial scale. The reaction rate considered for substratum consumption, cells and ethanol productions are validated with industrial data for typical operating conditions. The results obtained show that the proposed procedure in this work has a great potential for application.

In this paper, a 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. 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 correlations for the hydrodynamic model.

The economy has become universal and competitive, thus the industries of vegetable oil extraction must advance in the sense of minimising production costs and, at the same time, generating products that obey more rigorous patterns of quality, including solutions that do not damage the environment. The conventional oilseed processing uses hexane as solvent. However, this solvent is toxic and highly flammable. Thus the search of substitutes for hexane in oleaginous extraction process has increased in the last years. The supercritical carbon dioxide is a potential substitute for hexane, but it is necessary more detailed studies to understand the phenomena taking place in such process. Thus, in this work a diffusive model for semi-continuous (batch for the solids and continuous for the solvent) isothermal and isobaric extraction process using supercritical carbon dioxide is presented and submitted to a parametric sensitivity analysis by means of a factorial design in two levels. The model parameters were disturbed and their main effects analysed, so that it is possible to propose strategies for high performance operation.

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.

This work presents an application of a recursive estimator of states and parameters in a fed-batch penicillin production process based on the use of the extended Kalman filter. The estimated state variables were the cell, substrate, product and dissolved oxygen concentrations, the fermenter volume and the oxygen transfer coefficient. A simplified model of this process was used for the filter, and the actual values for product amount and concentration of dissolved oxygen with independent random Gaussian white noise were obtained using a deterministic and nonstructured mathematical model. The influence of the filter parameters, initial deviations and presence of noise on the observed variables was analyzed. In addition, estimator performance was verified when the parameters and the structure of the process model were changed. The extended Kalman filter implemented was found to be suitable to predict the states of the system and the model parameters. Therefore, it can be used for optimization and control purposes in a fermentative process which requires some state variables that are measured with a long delay time or unmeasured parameters.

A dynamic mathematical model, simulation and computer control of a Continuous Affinity Recycle Extraction (CARE) process, a protein purification technique based on protein adsorption on solid-phase adsorbents is described in this work. This process, consisting of three reactors, is a multivariable process with considerable time delay in the on-line analyses of the controlled variable. An advanced predictive control configuration, specifically the Dynamic Matrix Control (DMC), was applied. The DMC algorithm was applied in process schemes where the aim was to maintain constant the enzyme concentration in the outlet of the third reactor. The performance of the DMC controller was analyzed in the feed-flow disturbances and the results are presented.

Two control algorithms were implemented in the stabilization of the dissolved oxygen concentration of the penicillin process production phase. A deterministic and nonstructured mathematical model was used, where were considered the balances of cell, substrate, dissolved oxygen and product formation as well as kinetic of the growth, respiration, product inhibition due to excess of substrate, penicillin hydrolyze, yield factors among cell growth, substrate consumption and dissolved oxygen consumption. The bioreactor was operated in a feed-batch way using an optimal strategy for the operational policy. The agitation speed was used as manipulated variable in order to achieve the dissolved oxygen control because it was found to be the most sensitive one. Two types of control configurations were implemented. First, the PID feedback control with the parameters estimated through Modified Simplex optimization method using the IAE index, and second, the DMC predictive control that had as control parameters the model, prediction and control horizons as well as suppression factor and the trajectory parameter. A sensitivity analysis of these two control algorithms was performed using the sample time and dead time as the index to make stability evaluation. Both configurations showed stable performance, however, the predictive one was found to be more robust in relation to the sample time, as well as the dead time variations. This is a very important characteristic to be considered for the implementation of control scheme in real fermentative process