Antonio José Gonçalves Cruz

Bioelectricity versus bioethanol from sugarcane bagasse: is it worth being flexible?

BackgroundSugarcane is the most efficient crop for production of (1G) ethanol. Additionally, sugarcane bagasse can be used to produce (2G) ethanol. However, the manufacture of 2G ethanol in large scale is not a consolidated process yet. Thus, a detailed economic analysis, based on consistent simulations of the process, is worthwhile. Moreover, both ethanol and electric energy markets have been extremely volatile in Brazil, which suggests that a flexible biorefinery, able to switch between 2G ethanol and electric energy production, could be an option to absorb fluctuations in relative prices. Simulations of three cases were run using the software EMSO: production of 1G ethanol + electric energy, of 1G + 2G ethanol and a flexible biorefinery. Bagasse for 2G ethanol was pretreated with a weak acid solution, followed by enzymatic hydrolysis, while 50% of sugarcane trash (mostly leaves) was used as surplus fuel.ResultsWith maximum diversion of bagasse to 2G ethanol (74% of the total), an increase of 25.8% in ethanol production (reaching 115.2 L/tonne of sugarcane) was achieved. An increase of 21.1% in the current ethanol price would be enough to make all three biorefineries economically viable (11.5% for the 1G + 2G dedicated biorefinery). For 2012 prices, the flexible biorefinery presented a lower Internal Rate of Return (IRR) than the 1G + 2G dedicated biorefinery. The impact of electric energy prices (auction and spot market) and of enzyme costs on the IRR was not as significant as it would be expected.ConclusionsFor current market prices in Brazil, not even production of 1G bioethanol is economically feasible. However, the 1G + 2G dedicated biorefinery is closer to feasibility than the conventional 1G + electric energy industrial plant. Besides, the IRR of the 1G + 2G biorefinery is more sensitive with respect to the price of ethanol, and an increase of 11.5% in this value would be enough to achieve feasibility. The ability of the flexible biorefinery to take advantage of seasonal fluctuations does not make up for its higher investment cost, in the present scenario.

Assessing the production of first and second generation bioethanol from sugarcane through the integration of global optimization and process detailed modeling

Abstract There is a worldwide effort to make economically feasible the use of lignocellulosic biomass for production of biofuels. In sugarcane industry, cane juice (sucrose) is fermented for bioethanol production. Sugarcane bagasse is used as fuel in cogeneration systems, to produce steam and electric power to the plant, and the surplus of electric power may be delivered to the grid. The hydrolysis of bagasse to produce second generation ethanol poses a challenge: how much bagasse can be diverted, since the process must continue energetically self-sufficient. This work presents a computational tool developed within an equation-oriented process simulator that couples the simulation of first and second generation bioethanol production with a global optimization algorithm. The tool was robust, optimizing the steady state process in any economic scenario and for different process configurations. Four case studies are presented, and their implications on process internal demands and on the surplus electrical power are discussed.

Modelling and optimization of the cephalosporin C production bioprocess in a fed-batch bioreactor with invert sugar as substrate

Cephalosporin C production process optimization was studied based on four experiments carried out in an agitated and aerated tank fermentor operated as a fed-batch reactor. The microorganism Cephalosporium acremonium ATCC 48272 (C-10) was cultivated in a synthetic medium containing glucose as major carbon and energy source. The additional medium contained a hydrolyzed sucrose solution as the main carbon and energy source and it was added after the glucose depletion. By manipulating the supplementary feed rate, it was possible to increase antibiotic production. A mathematical model to represent the fed-batch production process was developed. It was observed that the model was applicable under different operation conditions, showing that optimization studies can be made based on this model.

Monitoring bioreactors using principal component analysis: production of penicillin G acylase as a case study

The complexity of biological processes often makes impractical the development of detailed, structured phenomenological models of the cultivation of microorganisms in bioreactors. In this context, data pre-treatment techniques are useful for bioprocess control and fault detection. Among them, principal component analysis (PCA) plays an important role. This work presents a case study of the application of this technique during real experiments, where the enzyme penicillin G acylase (PGA) was produced by Bacillus megaterium ATCC 14945. PGA hydrolyzes penicillin G to yield 6-aminopenicilanic acid (6-APA) and phenyl acetic acid. 6-APA is used to produce semi-synthetic β-lactam antibiotics. A static PCA algorithm was implemented for on-line detection of deviations from the desired process behavior. The experiments were carried out in a 2-L bioreactor. Hotteling’s T2 was the discrimination criterion employed in this multivariable problem and the method showed a high sensibility for fault detection in all real cases that were studied.

Using a medium of free amino acids to produce penicillin g acylase in fed-batch cultivations of Bacillus megaterium ATCC 14945

The production of penicillin G acylase (PGA, an important industrial enzyme) from a wild strain of Bacillus megaterium using a pool of free amino acids as substrate was studied in a bench-scale bioreactor. Experiments carried out in shakers showed that the substitution of casein for free amino acids in the presence of cheese whey was the culture medium that provided the highest productivity. Several cultivations were carried out in a bioreactor operated in either batch or fed-batch mode. Batch runs showed that enzyme production is associated with microorganism growth. The following set of amino acids was preferentially consumed: Ala, Arg, Asp, Gly, Lys, Ser, Thr and Trp. On the other hand, the rates of consumption of His, Ile, Leu, Met, Phe, Pro, Tyr and Val were lower.

A kinetic model for hydrothermal pretreatment of sugarcane straw

This work presents kinetic models of cellulose and hemicellulose extraction during hydrothermal pretreatment of sugarcane straw. Biomass was treated under conditions of 180, 195, and 210°C, using a solid/liquid ratio of 1:10 (w/v). In this study, cellobiose, glucose, formic acid and hydroxymethylfurfural (from cellulosic fraction) and xylose, arabinose, acetic acid, glucuronic acid and furfural (from hemicellulosic fraction) were taken into account in the kinetic parameters determination. The global search algorithm Simulated Annealing was used to fit the models. At 195°C/15min, 85% of hemicellulose and 21% of cellulose removal was reached. For the confidence regions, it was observed that it can be broad, which is coherent with the fact that the parameters are highly correlated. Kinetic models proposed for both cellulosic and hemicellulosic fractions degradation fitted well to the experimental data.

A hybrid feedforward neural network model for the cephalosporin C production process

At present, direct on-line measurements of key bioprocess variables as biomass, substrate and product concentrations is a difficult task. Many of the available hardware sensors are either expensive or lack reliability and robustness. To overcome this problem, indirect estimation techniques have been studied during the last decade. Inference algorithms rely either on phenomenological or on empirical models. Recently, hybrid models that combine these two approaches have received great attention. In this work, a hybrid neural network algorithm was applied to a fermentative process. Mass balance equations were coupled to a feedforward neural network (FNN). The FNN was used to estimate cellular growth and product formation rates, which are inserted into the mass balance equations. On-line data of cephalosporin C fed-batch fermentation were used. The measured variables employed by the inference algorithm were the contents of CO2 and O2 in the effluent gas. The fairly good results obtained encourage further studies to use this approach in the development of process control algorithms.

A fuzzy logic algorithm for identification of the harvesting threshold during PGA production by Bacillus megaterium

Abstract - Penicillin G acylase (PGA) is an important enzyme used as biocatalyst in the production of semisynthetic β-lactam antibiotics. Many microorganisms produce this enzyme and recombinant Escherichia coli has been preferred use for industrial applications. Bacillus megaterium is one of the microorganisms that excretes this enzyme into the medium. As a consequence, separation and purification steps are simplified. On-line measurement of enzyme activity during cultivation using in-situ sensors is a difficult task in the industrial environment due to the lack of robust and inexpensive instrumentation. This work presents the results of a fuzzy logic algorithm used to determine the moment of maximum enzyme concentration during Bacillus megaterium cultivations in an aerated and stirred, automated lab-scale bioreactor. The fuzzy algorithm was written in Fortran, compiled as a dynamic link library and implemented on a platform developed in MS-Visual Basic. Data were exchanged in real time between the platform and the supervisory system, which was coupled to the bioreactor. It was possible to determine the moment at which maximum enzyme activity was reached in several bioreactor assays. At this point, the end of the process was indicated to the operator. The results illustrate the importance of using reliable computational intelligence-based algorithms in biochemical reactions.

A hybrid neural network algorithm for on-line state inference that accounts for differences in inoculum of Cephalosporium acremonium in fed-batch fermentors.

One serious difficulty in modeling a fermentative process is the forecasting of the duration of the lag phase. The usual approach to model biochemical reactors relies on first-principles, unstructured mathematical models. These models are not able to take into account changes in the process response caused by different incubation times or by repeated fedbatches. To overcome this problem, we have proposed a hybrid neural network algorithm. Feedforward neural networks were used to estimate rates of cell growth, substrate consumption, and product formation from on-line measurements during cephalosporin C production. These rates were included in the mass balance equations to estimate key process variables: concentrations of cells, substrate, and product. Data from fed-batch fermentation runs in a stirred aerated bioreactor employing the microorganism Cephalosporium acremonium ATCC 48272 were used. On-line measurements strongly related to the mass and activity of the cells used. They include carbon dioxide and oxygen concentrations in the exhausted gas. Good results were obtained using this approach.

Cooperative Weblab: A Tool for Cooperative Learning in Chemical Engineering in a Global Environment

Abstract Weblabs are defined as a set of web-based physical laboratories that allows remote access and control in real time. Since the last decade, Weblabs are being more and more employed in many educational institutions around the world. In Sao Paulo state (Brazil) a “Cluster of Weblabs in Chemical and Biochemical Process Engineering” was implemented. This project has received a grant from FAPESP (State of Sao Paulo Research Agency). The experimental setups are physically placed in laboratories in Sao Paulo, Sao Carlos, Ribeirao Preto and Campinas, that are up to 250 km apart. This paper presents the implementation of two Cooperative Weblab (CW) experiments. The main concept behind the CW is to develop a set of experiments for undergraduate students that should be performed by “mixed teams” located in different institutions at the same time. Collaboration is achieved by gathering participants into working groups that are asked to simultaneously solve a technical problem, for which a weblab experiment is available. This procedure emulates challenges that will frequently take place in their future professional lives. Each group is supervised by a local instructor. Video conference software is used along the experiments (the institutions are connected through a high speed network, named KyaTera). All long the experiments, the students are encouraged to interact, exchange information and opinions on the phenomena that take place during the experimental practice. At the end of the session, the data collected are shared by the students and they produce a unique report for both groups. The use of this approach fosters interest in students. It is clear that the CW offer real benefits to chemical engineering education. It is now necessary to spread the dimension of the cluster and to increase the interaction with groups from other countries.