There is tremendous interest in using agro-industrial wastes, such as cellulignin, as starting materials for the production of fuels and chemicals. Cellulignin are the solids, which result from the acid hydrolysis of the sugarcane bagasse. The objective of this work was to optimize the enzymatic hydrolysis of the cellulose fraction of cellulignin, and to study its fermentation to ethanol using Saccharomyces cerevisiae. Cellulose conversion was optimized using response surface methods with pH, enzyme loading, solid percentage, and temperature as factor variables. The optimum conditions that maximized the conversion of cellulose to glucose, calculated from the initial dried weight of pretreated cellulignin, (43 degrees C, 2%, and 24.4 FPU/g of pretreated cellulignin) such as the glucose concentration (47 degrees C, 10%, and 25.6 FPU/g of pretreated cellulignin) were found. The desirability function was used to find conditions that optimize both, conversion to glucose and glucose concentration (47 degrees C, 10%, and 25.9 FPU/g of pretreated cellulignin). The resulting enzymatic hydrolyzate was fermented yielding a final ethanol concentration of 30.0 g/L, in only 10 h, and reaching a volumetric productivity of 3.0 g/L x h, which is close to the values obtained in the conventional ethanol fermentation of sugar cane juice (5.0-8.0 g/L x h) in Brazil.

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A mechanistic model was proposed by Gordillo for the representation of lipase production by Candida rugosa, with the bioreactor in batch and fed-batch operation. However, the model was not able to represent the lipolytic activity. The objective of the present study is to propose an efficient hybrid neural-phenomenological model (HNM) for this process. The experimental data used corresponded to fed-batch operation with constant substrate feed rate at 2.8 × 10 −7 ; 5.6 × 10 −7 and 9.7 × 10 −7 kg s −1 . Artificial neural networks (ANNs) were trained to represent the aqueous and intracellular lipase activity and were further associated with a reduced version of the mechanistic model of the proposed HNM. When compared to the experimental data, the HNM exhibited higher accuracy. The HNM can be employed in process monitoring using only on-line measurements of CO 2 and substrate feed rate to infer enzyme activities and also substrate and biomass concentrations.

Representative mathematical modeling is essential for understanding the batch cooling crystallization processes. Efficient process design and operation are relevant to achieving high-quality criteria and minimizing variation between batches. This work first presents the modeling of batch cooling crystallization based on online dynamic image analysis. A flow-through microscope was used to track the temporal evolution of the crystal population. A population balance modeling (PBM) approach, parameter estimation, and validation were obtained for the batch cooling crystallization of potassium sulfate in water. The performed experiments provided new experimental data, giving dynamic information about the crystal size throughout each run. The kinetic model parameters for crystal nucleation and growth were estimated using a hybrid optimization algorithm, followed by the confidence region construction using a more exploratory particle swarm algorithm. In the parameter estimation framework, in addition to solute concentration, the first fourth-order moments computed throughout all experiments were included in the objective function. A linear size-dependent growth rate was found to capture well the dynamics of the potassium sulfate crystal size distribution. The experimental results evidenced that the crystal shape of potassium sulfate is predominantly constant, allowing the adequacy of the developed model. The validated PBM was also employed as a digital twin of the crystallization process to develop a machine-learning-based control for the process. Then, a surrogate model based on a recurrent neural network, called an echo state network (ESN), was applied in a nonlinear model predictive controller approach (ESN-NMPC). The ESN model could predict the moments of the population balance model up to five steps (5 min) forward. The ESN-NMPC achieved the desired control scenarios for the crystal size and its coefficient of variation. Its performance was comparable to the controller that uses the PBM as the internal model (PB-NMPC).

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