Unveiling the potential of specific growth rate control in fed-batch fermentation: bridging the gap between product quantity and quality

During the epoch of sustainable development, leveraging cellular systems for production of diverse chemicals via fermentation has garnered attention. Industrial fermentation, extending beyond strain efficiency and optimal conditions, necessitates a profound understanding of microorganism growth characteristics. Specific growth rate (SGR) is designated as a key variable due to its influence on cellular physiology, product synthesis rates and end-product quality. Despite its significance, the lack of real-time measurements and robust control systems hampers SGR control strategy implementation. The narrative in this contribution delves into the challenges associated with the SGR control and presents perspectives on various control strategies, integration of soft-sensors for real-time measurement and control of SGR. The discussion highlights practical and simple SGR control schemes, suggesting their seamless integration into industrial fermenters. Recommendations provided aim to propose new algorithms accommodating mechanistic and data-driven modelling for enhanced progress in industrial fermentation in the context of sustainable bioprocessing.

Handheld near infrared spectrometer and machine learning methods applied to the monitoring of multiple process stages in industrial sugar production

This work aimed to evaluate the performance of a handheld NIR spectrometer in developing calibration models to quantify brix and pol at various stages of an industrial sugar production process. Because of sample variability, collected over two harvesting seasons, NIR measurements were acquired either in transmittance or diffuse reflectance. For modelling purpose, partial least squares (PLS), also combined with variable selection techniques, and support vector machine regression (SVR) were investigated. SVR was applied to handle non-linearities within the data. In general, results illustrated the best performance of SVR, that yielded lower root mean square error of prediction (RMSEP) values for brix and pol for spectra acquisition in transmittance (0.59 and 0.69%w/w) and using diffuse reflectance (1.44 and 2.44%w/w), respectively. Results from models using spectra collected in transmittance were comparable to those reported in other works where benchtop instruments were used, highlighting the cheaper and simpler employment of the portable spectrometer.

Soft-sensors application for automated feeding control in high-throughput mammalian cell cultures

The ever-increasing demand for biopharmaceuticals has created the need for improving the overall productivity of culture processes. One such operational concept that is considered is fed-batch operations as opposed to batch operations. However, optimal fed-batch operations require complete knowledge of the cell culture to optimize the culture conditions and the nutrients feeding. For example, when using high-throughput small-scale bioreactors to test multiple clones that do not behave the same, depletion or overfeeding of some key components can occur if the feeding strategy is not individually optimized. Over the recent years, various solutions for real-time measuring of the main cell culture metabolites have been proposed. Still, the complexity in the implementation of these techniques has limited their use. Soft-sensors present an opportunity to overcome these limitations by indirectly estimate these variables in real-time. This manuscript details the development of a new soft-sensor based fed-batch strategy to maintain substrate concentration (glucose and glutamine) at optimal levels in small-scale multi parallel CHO cultures. Two alternatives to the standard feeding strategy were tested: an OUR soft-sensor-based strategy for glucose and glutamine (Strategy 1) and a dual OUR for glutamine and CO₂ /alkali addition for glucose soft-sensor strategy (Strategy 2). The results demonstrated the applicability of the OUR soft-sensor based strategy to optimize glucose and glutamine feedings, which yielded a 21% increase in final viable cell density (VCD) and a 31% in erythropoietin (EPO) titer compared with the reference one. However, CO2/alkali addition soft-sensor suffered from insufficient data to relate alkali addition with glucose consumption. As a result, the culture was overfed with glucose resulting in a 4% increase on final VCD, but a 9% decrease in final titer compared to the Reference Strategy. This article is protected by copyright. All rights reserved.

A novel two-step adaptive multioutput semisupervised soft sensor with applications in wastewater treatment

To make full use of unlabeled data for soft-sensor modelling and to address the coexistence of a large number of hard-to-measure variable issues, this study proposed a novel two-step adaptive heterogeneous co-training multioutput model. First, unlabeled data with the highest confidence were selected to optimize the model. Then, the proposed model co-trained Gaussian process regression (GPR) and least squares support vector machine (LSSVM) algorithms with two sets of independent labeled data. Second, at each step of the model update, the Kalman filter (KF) worked together with a moving window (MW) to strengthen the model to address process dynamics. Finally, the proposed model was demonstrated by a simulated wastewater treatment platform, BSM1, and a real sewage treatment plant. The root-mean-square error (RMSE) and root-mean sum of squares of the diagonal (RMSSD) were obviously reduced, and the correlation coefficient (R) and correlation coefficient (RR) reached 0.8 in both case studies. The results suggest that the proposed model can significantly improve prediction performance.

Application of process system engineering tools to the fed-batch production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) from a vinasses-molasses Mixture

Fed-batch production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) copolymer using vinasses-molasses mixture is carried out in this work by implementing different process systems engineering tools. Two fed-batch strategies are tested experimentally at 5 L scale, considering only offline information: (1) offline optimizing control and (2) exponential feeding. Application of these strategies showed that different feeding profiles result in different dynamic behaviour, influencing both, yield and biopolymer properties. As offline-based feeding strategies do not consider information of the culture status, they cannot deal with uncertainties. Therefore, a closed loop control strategy was implemented, which uses biomass and substrate information predicted online by soft-sensors. Results demonstrated the technical feasibility to produce biopolymer using a 75/25%vol. vinasses-molasses mixture. Successful implementation of the soft-sensor-based control strategy was evidenced at pilot plant scale, where sugar concentration was kept almost constant for 14 h, while obtaining the desired copolymer. Thus, proposed control strategy could be of interest at industrial-scale.

Propagation of measurement accuracy to biomass soft-sensor estimation and control quality

In biopharmaceutical process development and manufacturing, the online measurement of biomass and derived specific turnover rates is a central task to physiologically monitor and control the process. However, hard-type sensors such as dielectric spectroscopy, broth fluorescence, or permittivity measurement harbor various disadvantages. Therefore, soft-sensors, which use measurements of the off-gas stream and substrate feed to reconcile turnover rates and provide an online estimate of the biomass formation, are smart alternatives. For the reconciliation procedure, mass and energy balances are used together with accuracy estimations of measured conversion rates, which were so far arbitrarily chosen and static over the entire process. In this contribution, we present a novel strategy within the soft-sensor framework (named adaptive soft-sensor) to propagate uncertainties from measurements to conversion rates and demonstrate the benefits: For industrially relevant conditions, hereby the error of the resulting estimated biomass formation rate and specific substrate consumption rate could be decreased by 43 and 64 %, respectively, compared to traditional soft-sensor approaches. Moreover, we present a generic workflow to determine the required raw signal accuracy to obtain predefined accuracies of soft-sensor estimations. Thereby, appropriate measurement devices and maintenance intervals can be selected. Furthermore, using this workflow, we demonstrate that the estimation accuracy of the soft-sensor can be additionally and substantially increased.

A comparative study of soft sensor design for lipid estimation of microalgal photobioreactor system with experimental validation

This study examines the applicability of various nonlinear estimators for online estimation of the lipid concentration in microalgae cultivation system. Lipid is a useful bio-product that has many applications including biofuels and bioactives. However, the improvement of lipid productivity using real-time monitoring and control with experimental validation is limited because measurement of lipid in microalgae is a difficult and time-consuming task. In this study, estimation of lipid concentration from other measurable sources such as biomass or glucose sensor was studied. Extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) were compared in various cases for their applicability to photobioreactor systems. Furthermore, simulation studies to identify appropriate types of sensors for estimating lipid were also performed. Based on the case studies, the most effective case was validated with experimental data and found that UKF and PF with time-varying system noise covariance is effective for microalgal photobioreactor system.