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

Integration of electronic nose, electronic tongue, and colorimeter in combination with chemometrics for monitoring the fermentation process of Tremella fuciformis

This study proposes an efficient method for monitoring the submerged fermentation process of Tremella fuciformis (T. fuciformis) by integrating electronic nose (e-nose), electronic tongue (e-tongue), and colorimeter sensors using a data fusion strategy. Chemometrics was employed to establish qualitative identification and quantitative prediction models. The Pearson correlation analysis was applied to extract features from the e-nose and tongue sensor arrays. The optimal sensor arrays for monitoring the submerged fermentation process of T. fuciformis were obtained, and four different data fusion methods were developed by incorporating the colorimeter data features. To achieve qualitative identification, the physicochemical data and principal component analysis (PCA) results were utilized to determine three stages of the fermentation process. The fusion signal based on full features proved to be the optimal data fusion method, exhibiting the highest accuracy across different models. Notably, random forest (RF) was shown to be the most accurate pattern recognition method in this paper. For quantitative prediction, partial least squares regression (PLSR) and support vector regression (SVR) were employed to predict the sugar content and dry cell weight during fermentation. The best respective predictive R2 values for reducing sugar, tremella polysaccharide and dry cell weight were found to be 0.965, 0.988, and 0.970. Furthermore, due to its ability to capture nonlinear data relationships, SVR had superior performance in prediction modeling than PLSR. The results demonstrated that the combination of electronic sensor fusion signals and chemometrics provided a promising method for effectively monitoring T. fuciformis fermentation.

Adapted feeding strategies in fed-batch fermentation improve sugar delivery and ethanol productivity

Bioethanol is a renewable fuel widely used in road transportation and is generally regarded as a clean energy source. Although fermentation is one of the major processes in bioethanol production, studies on improving its efficiency through operational design are limited, especially compared to other steps (pretreatment and hydrolysis/saccharification). In this study, two adapted feeding strategies, in which feed medium addition (sugar delivery) was adjusted to increase the supply of fermentable sugar, were developed to improve ethanol productivity in 5-L fed-batch fermentation by Saccharomyces cerevisiae. Specifically, a linear adapted feeding strategy was established based on changes in cell biomass, and an exponential adapted feeding strategy was developed based on cell biomass accumulation. By implementing these two feeding strategies, the overall ethanol productivity reached 0.88± 0.04 and 0.87± 0.06 g/L/h, respectively. This corresponded to ~20% increases in ethanol productivity compared to fixed pulsed feeding operations. Additionally, there was no residual glucose at the end of fermentation, and final ethanol content reached 95± 3 g/L under the linear adapted operation and 104± 3 g/L under the exponential adapted feeding strategy. No statistical difference was observed in the overall ethanol yield (ethanol-to-sugar ratio) between fixed and adapted feeding strategies (~91%). These results demonstrate that sugar delivery controlled by adapted feeding strategies was more efficient than fixed feeding operations, leading to higher ethanol productivity. Overall, this study provides novel adapted feeding strategies to improve sugar delivery and ethanol productivity. Integration into the current practices of the ethanol industry could improve productivity and reduce production costs of fermentation processes.

Production of volatile fatty acids by anaerobic digestion of biowastes: Techno-economic and life cycle assessments

Production of volatile fatty acids from food waste and lignocellulosic materials has potential to avoid emissions from their production from petrochemicals and provide valuable feedstocks. Techno-economic and life cycle assessments of using food waste and grass to produce volatile fatty acids through anaerobic digestion have been conducted. Uncertainty and sensitivity analysis for both assessments were done to enable a robust forecast of key-aspects of the technology deployment at industrial scale. Results show low environmental impact of volatile fatty acid with food wastes being the most beneficial feedstock with global warming potential varying from -0.21 to 0.01 CO2 eq./kg of product. Food wastes had the greatest economic benefit with a breakeven selling price of 1.11-1.94 GBP/kg (1.22-2.33 USD) of volatile fatty acids in the product solution determined through sensitivity analysis. Anaerobic digestion of wastes is therefore a promising alternative to traditional volatile fatty acid production routes, providing economic and environmental benefits.

Analysis of classical techniques precision on the measurement of cellulose moisture gain/loss

The precision of the four classical techniques (Karl-Fischer titration, (thermo)gravimetric method, Fourier-transform infrared (FT-IR) and near infrared (NIR) spectroscopies) commonly used in the analysis of cellulose moisture absorption/desorption has been deeply investigated regarding the reproducibility of these processes. Based on multiple repeated experiments, cellulose water content values obtained with Karl-Fischer titration and (thermo)gravimetric method were plotted as a function of time. Then, the cautious peak-by-peak analysis of the absorbance and wavenumber shifts visible in IR spectra has been carried out. The collected data was described using boxplots that provided valuable information on the experimental points spread. It has been successfully proven that gravimetric methods allow for precise drawing of moisture absorption and desorption curves, while Karl-Fischer titration, ATR FT-IR and NIR techniques provide the possibility of the moisture absorption/desorption processes description by linear mathematical models (R2 >90%). Therefore, this study provides a systematic comparison between various analytical methods.

Rapid detection of chemical oxygen demand, pH value, total nitrogen, total phosphorus, and ammonia nitrogen in biogas slurry by near infrared spectroscopy

Effective treatment of sewage requires accurate measurement of important water quality parameters, such as chemical oxygen demand (COD), pH value, total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH3-N). Traditional detection techniques can result in secondary contamination and are time- and labor-intensive. Near infrared spectroscopy was used in this study to create a model of these parameters of pig manure anaerobic fermentation sewage. The models' viability for quickly estimating the aforementioned water quality characteristics was reviewed, and the models' performance in predicting the results of several samples (biogas slurry, supernatant, and biogas residue) was contrasted. By analyzing the near infrared spectrograms with a spectral range of 4000 cm-1 and 12 500 cm-1 and using second derivative (SD), Savitzky-Golay smoothing (SG) and standard normal variable (SNV) to preprocess the spectra, partial least squares (PLS) was selected to establish the prediction model. The results showed that the effect of the NIR model constructed from the supernatant was better than that of biogas slurry and biogas residue. The determination coefficients for COD, pH value, NH3-N and TN were 0.69, 0.87, 0.81, and 0.94, respectively. This study could provide reference for on-line monitoring of wastewater in the future.

On-line monitoring of total sugar during kombucha fermentation process by near-infrared spectroscopy: Comparison of linear and non-linear multiple calibration methods

Kombucha is widely recognized for its health benefits, and it facilitates high-quality transformation and utilization of tea during the fermentation process. Implementing on-line monitoring for the kombucha production process is crucial to promote the valuable utilization of low-quality tea residue. Near-infrared (NIR) spectroscopy, together with partial least squares (PLS), backpropagation neural network (BPANN), and their combination (PLS-BPANN), were utilized in this study to monitor the total sugar of kombucha. In all, 16 mathematical models were constructed and assessed. The results demonstrate that the PLS-BPANN model is superior to all others, with a determination coefficient (R²p) of 0.9437 and a root mean square error of prediction (RMSEP) of 0.8600 g/L and a good verification effect. The results suggest that NIR coupled with PLS-BPANN can be used as a non-destructive and on-line technique to monitor total sugar changes.

Application of near-infrared spectroscopy technology in the complex fermentation system to achieve high-efficiency production

The fermentation process is dynamically changing, and the metabolic status can be grasped through real-time monitoring of environmental parameters. In this study, a real-time and on-line monitoring experiment platform for substrates and products detection was developed based on non-contact type near-infrared (NIR) spectroscopy technology. The prediction models for monitoring the fermentation process of lactic acid, sophorolipids (SLs) and sodium gluconate (SG) were established based on partial least-squares regression and internal cross-validation methods. Through fermentation verification, the accuracy and precision of the NIR model for the complex fermentation environments, different rheological properties (uniform system and multi-phase inhomogeneous system) and different parameter types (substrate, product and nutrients) have good applicability, and R2 was greater than 0.98, exhibiting a good linear relationship. The root mean square error of prediction shows that the model has high credibility. Through the control of appropriate glucose concentration in SG fermentation as well as glucose and oil concentrations SLs fermentation by NIR model, the titers of SG and SLs were increased to 11.8% and 26.8%, respectively. Although high cost of NIR spectrometer is a key issue for its wide application in an industrial scale. This work provides a basis for the application of NIR spectroscopy in complex fermentation systems.

Real-time and on-line monitoring of ethanol fermentation process by viable cell sensor and electronic nose

In this study, introduction of a viable cell sensor and electronic nose into ethanol fermentation was investigated, which could be used in real-time and on-line monitoring of the amount of living cells and product content, respectively. Compared to the conventional off-line biomass determination, the capacitance value exhibited a completely consistent trend with colony forming units, indicating that the capacitance value could reflect the living cells in the fermentation broth. On the other hand, in comparison to the results of off-line determination by high-performance liquid chromatography, the ethanol concentration measured by electronic nose presented an excellent consistency, so as to realize the on-line monitoring during the whole process. On this basis, a dynamic feeding strategy of glucose guided by the changes of living cells and ethanol content was developed. And consequently, the ethanol concentration, productivity and yield were enhanced by 15.4%, 15.9% and 9.0%, respectively. The advanced sensors adopted herein to monitor the key parameters of ethanol fermentation process could be readily extended to an industrial scale and other similar fermentation processes.

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.

Improved bioethanol productivity through gas flow rate-driven self-cycling fermentation

BACKGROUND

The growth of the cellulosic ethanol industry is currently impeded by high production costs. One possible solution is to improve the performance of fermentation itself, which has great potential to improve the economics of the entire production process. Here, we demonstrated significantly improved productivity through application of an advanced fermentation approach, named self-cycling fermentation (SCF), for cellulosic ethanol production.

RESULTS

The flow rate of outlet gas from the fermenter was used as a real-time monitoring parameter to drive the cycling of the ethanol fermentation process. Then, long-term operation of SCF under anaerobic conditions was improved by the addition of ergosterol and fatty acids, which stabilized operation and reduced fermentation time. Finally, an automated SCF system was successfully operated for 21 cycles, with robust behavior and stable ethanol production. SCF maintained similar ethanol titers to batch operation while significantly reducing fermentation and down times. This led to significant improvements in ethanol volumetric productivity (the amount of ethanol produced by a cycle per working volume per cycle time)-ranging from 37.5 to 75.3%, depending on the cycle number, and in annual ethanol productivity (the amount of ethanol that can be produced each year at large scale)-reaching 75.8 ± 2.9%. Improved flocculation, with potential advantages for biomass removal and reduction in downstream costs, was also observed.

CONCLUSION

Our successful demonstration of SCF could help reduce production costs for the cellulosic ethanol industry through improved productivity and automated operation.

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.

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.