Marcelo Perencin de Arruda Ribeiro

Monitoring of the cellulosic ethanol fermentation process by near-infrared spectroscopy.

Rapid, efficient, and low-cost technologies for monitoring the fermentation process during second generation (2G) or cellulosic ethanol production are essential for the successful implementation of this process at the commercial scale. Here, the use of near-infrared (NIR) spectroscopy associated with partial least squares (PLS) regression was investigated as a tool for monitoring the production of 2G ethanol from lignocellulosic sugarcane residues including bagasse, straw, and tops. The spectral data was based on a set of 103 alcoholic fermentation samples. Models based on different pre-processing techniques were evaluated. The best root mean square error of prediction (RMSEP) values obtained in the external validation were around 3.02 g/L for ethanol and 6.60 g/L for glucose. The findings showed that the PLS-NIR methodology was efficient in accurately predicting the glucose and ethanol concentrations during the production of 2G ethanol, demonstrating potential for use in monitoring and control of large-scale industrial processes.

Variational calculus (optimal control) applied to the optimization of the enzymatic synthesis of ampicillin

In this work, optimal control techniques were used to optimize the feed of reactants during the enzymatic synthesis of ampicillin in a semi-batch reactor. Simulation results showed that a semi-batch integrated reactor (with product crystallization) might achieve 88% 6-APA (6-aminepenicillanic acid) conversion and 92% of PGME (phenylglycine methyl ester) yield, with a productivity between 3.5 and 5.5 mM min-1.

Fast Determination of the Composition of Pretreated Sugarcane Bagasse Using Near-Infrared Spectroscopy

The chemical composition of pretreated sugarcane bagasse (SCB), in terms of cellulose, hemicellulose and lignin, was analyzed using a fast near-infrared spectroscopy (NIR) technique. Spectra of four types of SCB, prepared using ammonia, hydrothermal, organosolv, and sodium hydroxide pretreatments, were correlated with results of classical chemical analyses using partial least squares (PLS) regression. In a novel approach, isolation of the components used to prepare synthetic samples of SCB permitted assessment of their influence on the model. Inclusion of the synthetic samples did not improve the performance of the model, due to structural differences such as chemical bonding and physical interactions between the components. For natural pretreated samples, the PLS technique showed good predictive capacity in the ranges (%, w/w) of 47.2–89.4 (cellulose), 0.2–27.0 (hemicellulose), and 2.1–30.0 (lignin) with low root-mean-square error values of 4.1, 3.8, and 3.5, respectively, and coefficient of determination higher than 0.80, demonstrating the suitability of using different pretreated samples in the same calibration model.

Fast spectroscopic monitoring of inhibitors in the 2G ethanol process

One of the main challenges of second generation (2G) ethanol production is the high quantities of phenolic compounds and furan derivatives generated in the pretreatment of the lignocellulosic biomass, which inhibit the enzymatic hydrolysis and fermentation steps. Fast monitoring of these inhibitory compounds could provide better control of the pretreatment, hydrolysis, and fermentation processes by enabling the implementation of strategic process control actions. We investigated the feasibility of monitoring these inhibitory compounds by ultraviolet-visible (UV-Vis) spectroscopy associated with partial least squares (PLS) regression. Hydroxymethylfurfural, furfural, vanillin, and ferulic and p-coumaric acids generated during different severities of liquid hot water pretreatment of sugarcane bagasse were quantified with highly accuracy. In cross-validation (leave-one-out), the PLS-UV-Vis method presented root mean square error of prediction (RMSECV) of around only 5.0%. The results demonstrated that the monitoring performance achieved with PLS-UV-Vis could support future studies of optimization and control protocols for application in industrial processes.

Retro-Techno-Economic-Environmental Analysis (RTEEA) from the cradle: a new approach for process development

Abstract The chance of a successful industrial implementation of innovative or unconventional processes can be greatly enhanced if the analysis of economic feasibility and environmental impacts is performed from the beginning. In this study, Retro-Techno-Economic Analysis was expanded into Retro-Techno-Economic-Environmental Analysis (RTEEA), combining Life Cycle Analysis (LCA) metrics with economic ones, in order to define regions of feasible operation for the process. In addition, the selection of key process variables was done through global sensitivity analysis (GSA). RTEEA was applied to the case study, the production of succinic acid (SAc) from sugarcane sucrose. Greenhouse gases emissions (GHG, in kg CO 2 eq./kg SAc) and the process Net Present Value (NPV) were chosen as performance metrics. For mapping the GSA response surface, Latin hypercube sampling was used. Sensitivity analysis pointed out that GHG is only influenced by yeast selectivity while NPV was more sensitive to concentration, productivity and yeast selectivity (99.6% of explained variance). The feasible region is bounded by the limits in yeast selectivity and by the infinite productivity curve (obtained assuming instantaneous reaction). The methodology was able to identify the main process variables that influence the process economic and environmental performance, derive their threshold values, and make explicit their relations.