Elmer Ccopa Rivera

Estimation of Temperature Dependent Parameters of a Batch Alcoholic Fermentation Process

In this work, a procedure was established to develop a mathematical model considering the effect of temperature on reaction kinetics. Experiments were performed in batch mode in temperatures from 30 to 38°C. The microorganism used was Saccharomyces cerevisiae and the culture media, sugarcane molasses. The objective is to assess the difficulty in updating the kinetic parameters when there are changes in fermentation conditions. We conclude that, although the re-estimation is a time-consuming task, it is possible to accurately describe the process when there are changes in raw material composition if a re-estimation of parameters is performed.

Biodiesel production from vegetable oils: Operational strategies for large scale systems

Abstract This work presents the transesterification process of vegetable oils with bioethanol in the presence of sodium hydroxide as catalyst, because it leads to better conversion and smaller reaction time. A computer-aided tool of this system to model the kinetic of biodiesel production was developed to explore the impact of each strategy on the process behaviour which is an important issue to lead the process to be operated at high level of performance. An analysis was made of the temperature effects on the reaction rates, and it was determined the reaction rate constants and the activation energies derived from experimental observation. The kinetic data showed to be satisfactory for a wide range of operating conditions. The assessment of possible implementation difficulties are carefully considered and discussed.

Study of kinetic parameters in a mechanistic model for bioethanol production through a screening technique and optimization

The accurate description of the kinetics and robust modeling of biotechnological processes can only be achieved by incorporating reliable methodologies to easily update the model when there are changes in operational conditions. The purpose of this work is to provide a systematic approach with which to perform model parameters screening and updating in biotechnological processes. Batch experiments are performed to develop a mechanistic model, considering the effect of temperature on the kinetics, and further experiments (batch fermentations using sugar cane molasses from a different harvest) are used to validate the effectiveness of screening before parameters updating. The reduction in the number of kinetic parameters to be re-estimated enabled by the screening procedure reduces significantly the complexity of the optimization, which makes the updating procedure to be significantly quicker, while resulting in accurate performance of the updated model.

Sustainability Assessment Methodologies

This chapter presents some of the most useful techniques applied in the VSB to support both the sustainability assessment and decision making regarding many products and process based on biomass, especially those using sugarcane as feedstock.

Hybrid Neural Network Model of an Industrial Ethanol Fermentation Process Considering the Effect of Temperature

In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These networks have been shown to have a good nonlinear approximation capability, although the estimation of its weights is linear. The proposed model considers the effect of temperature on the kinetics and has the neural network weights reestimated always so that a change in operational conditions occurs. This allow to follow the system behavior when changes in operating conditions occur.

Ethyl alcohol production optimization by coupling genetic algorithm and multilayer perceptron neural network.

In this present article, genetic algorithms and multilayer perceptron neural network (MLPNN) have been integrated in order to reduce the complexity of an optimization problem. A data-driven identification method based on MLPNN and optimal design of experiments is described in detail. The nonlinear model of an extractive ethanol process, represented by a MLPNN, is optimized using real-coded and binary-coded genetic algorithms to determine the optimal operational conditions. In order to check the validity of the computational modeling, the results were compared with the optimization of a deterministic model, whose kinetic parameters were experimentally determined as functions of the temperature.

Techno-Economic Analysis of Second-Generation Ethanol in Brazil: Competitive, Complementary Aspects with First-Generation Ethanol

Brazil achieved important success in the implementation of ethanol as a reality renewable energy source after the inception of the National Alcohol Program (PROALCOOL) in 1970. Today, ethanol produced from sugarcane replaces almost 50 % of gasoline in Brazil. More than 448 bioethanol production (first-generation ethanol) units are functional, which fulfill the 25 % ethanol blending to gasoline that eventually reduces the import of 550 million oil barrels improving the socioeconomic status and saving foreign exchange reserves. Brazil has more than 80 % of its light vehicles running on bioethanol, reducing greenhouse gas emissions. At present, this demand for ethanol is being met through first-generation (1G) ethanol which is directly produced from sugarcane juice and molasses. However, significant research in bioenergy in the last two decades has shown the possibilities of commercialization of second-generation (2G) ethanol, which can be produced from sugarcane bagasse (SB) and straw (SS), complementing 1G ethanol. Nevertheless, both the residues (SB and SS) are an excellent source for cogeneration of heat and power (CHP) in sugarcane processing units. Process simulation studies have provided additional source of information on the overall use of sugarcane for ethanol production and CHP. For the evaluation of the fullest utilization of sugarcane and its by-products, CTBE (Brazilian Bioethanol Science and Technology Laboratory) has developed the Virtual Sugarcane Biorefinery (VSB), a comprehensive assessment framework to evaluate a sustainability standpoint (economic, environmental, and social), different biorefinery alternatives. This chapter reviews the important insights made into bioethanol production in Brazil. Technical configuration for 1G and 2G ethanol production and sustainability of ethanol (economic and environmental assessment) have also been discussed.

Use of the VSB to Assess Biorefinery Strategies

The VSB has been used to assess several configurations of sugarcane biorefineries, including first- and second-generation facilities with alternative features, such as harvesting extension with other crops and integrated production of sugar, ethanol, electricity, and chemicals like n-butanol, as well as thermochemical routes. In this chapter, in-depth examples of selected routes are provided in order to illustrate the different methods that can be applied for the sustainability impact assessment, as well as some results obtained with the VSB

A LabVIEW-based intelligent system for monitoring of bioprocesses

Abstract The application presented in this study illustrates the usefulness of an automated monitoring system carried out in LabVIEW environment. The results obtained have show that it is possible to infer into a real-time basis the key variables in bioethanol fermentation using pH, turbidity, CO 2 flow rate and temperature on line measurements and a MLP-based Software Sensor. On-line monitoring system provided accurate online predictions of the concentrations during the fermentation process even when the secondary variables measurements were noisy. This study also will illustrate the usefulness of an automated monitoring system carried out in graphical programming environment.

Bioethanol Production Optimization: A Thermodynamic Analysis

In this work, the phase equilibrium of binary mixtures for bioethanol production by continuous extractive process was studied. The process is composed of four interlinked units: fermentor, centrifuge, cell treatment unit, and flash vessel (ethanol-congener separation unit). A proposal for modeling the vapor–liquid equilibrium in binary mixtures found in the flash vessel has been considered. This approach uses the Predictive Soave–Redlich–Kwong equation of state, with original and modified molecular parameters. The congeners considered were acetic acid, acetaldehyde, furfural, methanol, and 1-pentanol. The results show that the introduction of new molecular parameters r and q in the UNIFAC model gives more accurate predictions for the concentration of the congener in the gas phase for binary and ternary systems.