Rubens Maciel

Kinetics of Ethanol Fermentation with High Biomass Concentration Considering the Effect of Temperature

A model of ethanol fermentation considering the effect of temperature was developed and validated. Experiments were performed in a temperature range from 28 to 40°C in continuous mode with total cell recycling using a tangential microfiltration system. The developed model considered substrate, product and biomass inhibition, as well as an active cell phase (viable) and an inactive (dead) phase. The kinetic parameters were described as functions of temperature.

Factorial design and simulation for the optimization and determination of control structures for an extractive alcoholic fermentation.

The design, optimization and control of an extractive alcoholic fermentation were studied. The fermentation process was coupled to a vacuum flash vessel that extracted part of the ethanol. Response surface analysis was used in combination with modelling and simulation to determine the operational conditions that maximize yield and productivity. The concepts of factorial design were used in the study of the dynamic behaviour of the process, which was used to determine the best control structures for the process. A good choice of the operational conditions was important to enable efficient control of the process. The performance of a DMC (Dynamic Matrix Control) algorithm was studied to control the extractive process.

Simulated Dynamics and Control of an Extractive Alcoholic Fermentation

In this study, we investigated the dynamics of a computer simulation of a continuous alcoholic fermentation process combined with a flash column under vacuum. The alcohol was partially extracted in order to maintain its concentration at about 40 kg/m3 in the fermentor. The mathematical model of the fermentation was developed for industrial conditions and considers the effect of the temperature on the kinetic parameters. The performance of the dynamic matrix control algorithm, single input single output and multiple input multiple output, for the control of the extractive process was studied. The concepts of factorial design were used in a simulation study to determine the best control structures for the process.

An Assessment of Brazilian Government Initiatives and Policies for the Promotion of Biofuels Through Research, Commercialization, and Private Investment Support

This chapter describes some of the scientific and technological achievements that have contributed to develop sugarcane bioenergy as a major contributor to the Brazilian energy matrix. Today, modern bioenergy plays a key role in the Brazilian economy, with 18 % of Brazilian energy usage coming from sugarcane, with ethanol used as fuel and bagasse to generate electricity. This chapter also discusses the Brazilian biodiesel opportunities and biofuels for aviation, which hold promise for the future. The long-term role played by the Brazilian government in promoting biofuels is considered as a key factor to success, particularly with sugarcane ethanol. Government-funded research agencies have played a strategic role in consolidating knowledge and human capacity to maintain leadership in the bioenergy sector. Brazil presents exceptional conditions to expand bioenergy industry (ethanol, biodiesel and biofuels for aviation) and also bioelectricity and green chemistry. To this end it is necessary to create conditions for the increase of the private R&D expenditures, as well as governmental actions to train human resources in the area of bioenergy. With new research centers, graduate programs have the potential to contribute to increasing competence at all stages of bioenergy development.

Process analysis and optimization mapping through design of experiments and its application to a polymerization process

The technique of experimental design is used on an ethylene polymerization process model in order to map the feasible optimal region as preliminary information for process optimization. Through the use of this statistical tool, together with a detailed deterministic model validated with industrial data, it is possible to identify the most relevant variables to be considered as degrees of freedom for the optimization and also to acquire significant process knowledge, which is valuable not only for future explicit optimization but also for current operational practice. The responses evaluated by the experimental design approach include the objective function and the constraints of the optimization, which also consider the polymer properties. A Plackett-Burman design with 16 trials is first carried out in order to identify the most important inlet variables. This reduces the number of decision variables, hence the complexity of the optimization model. In order to carry out a deeper investigation of the process, complete factorial designs are further implemented. They provide valuable process knowledge because interaction effects, including highly non-linear interactions between the variables, are treated methodically and are easily observed.

Optimization of a Large Scale Industrial Reactor Towards Tailor Made Polymers Using Genetic Algorithm

Abstract This paper presents a computational procedure for producing tailor made polymer resins, satisfying customers’ needs while operating with maximum profit. The case study is an industrial large-scale polymerization reactor. The molecular properties considered are melt index (MI), which measures the molecular weight distribution, and stress exponent (SE), which is related to polydispersity. An economic objective function is associated to a deterministic mathematical model and the resulting optimization problem is solved by genetic algorithm (GA), a stochastic method. The GA parameters for both binary and real codifications are tuned by means of the design of experiments. Attempting to achieve the global optimum, a hybrid method, which introduces process knowledge into GA random initial population, is proposed. The binary codification performs better than the real GA, especially with hybridization. Results show that the GA can satisfactorily predict tailor made polymer resins with profits up to 25% higher than the industrial practice.

Genetic Algorithm for Tailored Production of Polymer Resins

Abstract This study presents an optimization model based on genetic algorithm (GA) for the production of target polymer resins. Unlike most contributions in open literature, this study takes into account an economic optimization criterion while satisfying the desired polymer properties through constraints at the end of the reactor. The case study is the ethylene coordination polymerization, which takes place in a CSTR (continuous stirred tank reactor). Due to the high non-linearity of the process model, a stochastic optimization method based on GA is used. Several simulations were carried out and the results show that the GA is able to determine the optimal operating conditions satisfactorily. Binary codification proved to be more robust than real codification.