Fast spectroscopic monitoring of inhibitors in the 2G ethanol process

Weight interpretation of artificial neural network model for analysis of rice (Oryza sativa L.) with near-infrared spectroscopy

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ANN model was build based on NIR spectra and nutrient values of 110 rice samples.

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Good correlation between ANN predicted and experimental nutrient values observed.

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Scientific interpretation of weights agreed well with previously reported results.

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Interpretation of weights was also in good agreement with conventional PLS analysis.

Prediction models for major nutrients of rice were built using near-infrared (NIR) spectral data based on the artificial neural network (ANN). Scientific interpretation of the weight values was proposed and performed to understand the wavenumbers contributing to the prediction of nutrients. NIR spectra were acquired from 110 rice samples. Carbohydrate and moisture contents were predicted with values for the determination coefficient, relative root mean square error, range error ratio, and residual prediction deviation of 0.98, 0.11 %, 44, and 7.3, and 0.97, 0.80 %, 27, and 5.8, respectively. The results agreed well with ones reported in the previous studies and acquired by the conventional partial least squares (PLS)-variable importance in projection method. This study demonstrates that the combination of NIR and ANN is a powerful and accurate tool to monitor nutrients of rice and scientific interpretation of weights can be performed to overcome black box nature of the ANN.

Multivariable advanced nonlinear controller for bioethanol production in a non-isothermal fermentation bioreactor

Design for fermentation bioreactor controllers is challenged by the nonlinear process kinetics and the lack of online measurements for key variables. This work developed a multi-input, multi-output advanced nonlinear control structure for a continuous, non-isothermal, constant volume fermentation bioreactor. Utilizing feedback linearization control for the bioreactor feed to regulate glucose concentration, and backstepping control for the cooling jacket feed to regulate reactor temperature. A developed novel estimator for biomass concentration was incorporated to provide online estimates for the unmeasurable state variable. Simulation results showed the control structure ability in efficiently establishing a combination of dynamic and fixed set points, despite disturbances in the bioreactor feed temperature and glucose concentration. Expanded bioreactor control authority increased operational flexibility and enhanced the potential for performance improvements. This work illustrated the effectiveness of feedback linearization and backstepping control in designing controllers for biological systems with nonlinear dynamics, complex interactions, and input disturbances.

An innovative spectroscopic approach for qualitative and quantitative evaluation of Mb-CO from myoglobin carbonylation reaction through chemometrics methods

In this work, an innovative approach using K-means and multivariate curve resolution-purity based algorithm (MCR-Purity) for the evaluation and quantification of carboxymyoglobin (Mb-CO) formation from Deoxy-Myoglobin (Deoxy-Mb) was presented. Through a multilevel multifactor experimental design, samples with different concentrations of Mb-CO were created. The UV-Vis spectra of these samples were submitted to K-means analysis, finding 3 clusters. The mean spectra of the clusters were extracted and it was possible to detect 2 totally differentiable groups through peaks 423 and 434 nm, which are wavelengths related to the Mb-CO and Deoxy-Mb components, respectively. The spectral data were subjected to MCR-Purity analysis. The MCR-Purity result successfully described the analyzed reaction, explaining more than 99.9% of the variance (R²) with a LOF of 1.43%. Then, a predictive model of MbCO was created through the linear relationship between MCR-Purity contributions and known concentrations of MbCO. The performance parameters of the created predictive model were R²CV = 0.98, RMSECV = 0.58 and RPDcv = 7.8 for the training set, and R²P = 0.98, RMSEP = 0.7 and RPDp = 6.8 for the test set. Thus, the predictive model presented an excellent performance considering that the Mb-CO variation is comprised between 0 and 21 µM. Therefore, these results demonstrate that the application of the proposed strategy to the analysis of spectral data presenting overlapping bands is feasible and robust.

Promoting the co-utilisation of glucose and xylose in lignocellulosic ethanol fermentations using a data-driven feed-back controller

BACKGROUND

The diauxic growth of Saccharomyces cerevisiae on glucose and xylose during cellulose-to-ethanol processes extends the duration of the fermentation and reduces productivity. Despite the remarkable advances in strain engineering, the co-consumption of glucose and xylose is still limited due to catabolite repression. This work addresses this challenge by developing a closed-loop controller that is capable of maintaining the glucose concentration at a steady set-point during fed-batch fermentation. The suggested controller uses a data-driven model to measure the concentration of glucose from 'real-time' spectroscopic data. The concentration of glucose is then automatically controlled using a control scheme that consists of a proportional, integral, differential (PID) algorithm and a supervisory layer that manipulates the feed-rates to the reactor accounting for the changing dynamics of fermentation.

RESULTS

The PID parameters and the supervisory layer were progressively improved throughout four fed-batch lignocellulosic-to-ethanol fermentations to attain a robust controller able of maintaining the glucose concentration at the pre-defined set-points. The results showed an increased co-consumption of glucose and xylose that resulted in volumetric productivities that are 20-33% higher than the reference batch processes. It was also observed that fermentations operated at a glucose concentration of 10 g/L were faster than those operated at 4 g/L, indicating that there is an optimal glucose concentration that maximises the overall productivity.

CONCLUSIONS

Promoting the simultaneous consumption of glucose and xylose in S. cerevisiae is critical to increase the productivity of lignocellulosic ethanol processes, but also challenging due to the strong catabolite repression of glucose on the uptake of xylose. Operating the fermentation at low concentrations of glucose allows reducing the effects of the catabolite repression to promote the co-consumption of the two carbon sources. However, S. cerevisiae is very sensitive to changes in the glucose concentration and deviations from a set-point result in notable productivity losses. The controller structure developed and implemented in this work illustrates how combining data-driven measurements of the glucose concentration and a robust yet effective PID-based supervisory control allowed tight control of the concentration of glucose to adjust it to the metabolic requirements of the cell culture that can unlock tangible gains in productivities.

Application of green analytical chemistry to a green chemistry process: Magnetic resonance and Raman spectroscopic process monitoring of continuous ethanolic fermentation

Compact ¹ H NMR and Raman spectrometers were used for real-time process monitoring of alcoholic fermentation in a continuous flow reactor. Yeast cells catalyzing the sucrose conversion were immobilized in alginate beads floating in the reactor. The spectrometers proved to be robust and could be easily attached to the reaction apparatus. As environmentally friendly analysis methods, ¹ H NMR and Raman spectroscopy were selected to match the resource- and energy-saving process. Analyses took only a few seconds to minutes compared to chromatographic procedures and were, therefore, suitable for real-time control realized as a feedback loop. Both compact spectrometers were successfully implemented online. Raman spectroscopy allowed for faster spectral acquisition and higher quantitative precision, NMR yielded more resolved signals thus higher specificity. By using the software Matlab for automated data loading and processing, relevant parameters such as the ethanol, glycerol, and sugar content could be easily obtained. The subsequent multivariate data analysis using partial linear least-squares regression type 2 enabled the quantitative monitoring of all reactants within a single model in real time.

Optimization of the sugar hydrothermal extraction process from olive cake using neuro-fuzzy models

The optimization of the hydrothermal extraction step in the biorefinery scheme in order to obtain sugars and antioxidants from olive cake was carried out. This process using water and low temperatures for the olive cake has not been previously studied by other authors. Central Composite Design of experiments was carried out. Four variables and three levels for each variable were tested: temperature (50, 70 and 90 °C) time (60, 120, 180 min), average particle diameter (0.42, 0.96 and 1.5 mm) and solid/liquid ratio (0.15, 0.20, 0.25). The main aim was to understand the relationship between the process variables and the yield of extraction of glucose, xylose, polyphenols and oligomers. The variables which most influenced the process were particle diameter and solid/liquid ratio. The experimental values were adjusted to a classical polynomial model and to a neuro-fuzzy system. The neuro-fuzzy demonstrated to be much more accurate when predicting the experimental values.