Modelling of industrial-scale bioreactors using the particle lifeline approach

Data Availability: Data have been made available in a repository, details are in the data availability statement.Supplementary material is available online at https://www.sciencedirect.com/science/article/pii/S1369703X23001845?via%3Dihub#sec0030 .Copyright © 2023 The Author(s). A key factor in improving the performance of large-scale bioreactors is understanding the conditions experienced by the cells inside the reactor. This can be challenging due to the practical difficulties involved, hence there is increasing use of simulation to quantify the environmental conditions found in large-scale bioreactors. In this work we have used the particle lifeline approach to quantify the effect of the reactor design on the conditions experienced by two very commonly used industrial organisms (Escherichia coli and Saccharomyces cerevisiae). It was found that the cells in the stirred tank reactor tended to experience longer fluctuations of both starvation and overflow metabolism when compared with those in the bubble column, this behaviour being caused by differences in mixing between the two reactor designs. It was found that a significant (60%) fraction of the population in the stirred tank reactors experienced starvation conditions for a large fraction (>70%) of the time, with exposure to such conditions being likely to affect the cellular metabolism. Results from this work provide a detailed insight into the conditions experienced inside industrial-scale bioreactors operated at realistic conditions. Such data can be leveraged to optimise large-scale reactor designs as well as for the development of scale-down systems.Technical University of Denmark and Novozymes A/S

Development of dynamic compartment models for industrial aerobic fed-batch fermentation processes

Inhomogeneities in key cultivation variables (e.g., substrate and oxygen concentrations) have been shown to affect key process metrics in large-scale bioreactors. Being able to understand these gradients is hence of key interest from both an industrial and academic perspective. One of the main shortcomings of current modelling approaches is that volume change is not considered. Volume increase is a key feature of fed-batch fermentation processes. Existing models are restricted to simulating snapshots (hundreds of seconds) of industrial processes, which can last several weeks. This study presents a novel methodology that overcomes this limitation by constructing dynamic compartment models for the simulation of fed-batch fermentation processes. This strategy is applied to an industrial aerobic fed-batch fermentation process (40–90 m3) with Saccharomyces cerevisiae. First, it has been validated numerically that the compartmentalization strategy used captures the mixing performance and fluid dynamics. This was done by comparing the mixing times and the local concentration profiles of snapshot fermentation process simulations calculated with both CFD and compartment models. Subsequently, simulations of the entire process have been performed using the dynamic compartment model with kinetics. The simulation allows the spatio-temporal characterization of all process variables (e.g., glucose and DO concentrations), as well as the quantification of the metabolic regimes that the cells experience over time. This strategy enables the rapid characterization and assessment of the impact of gradients on process performance in industrial (aerobic) fed-batch fermentation processes and can be readily generalized to any type of bioreactor and microorganism.Technical University of Denmark; Novozymes A/S

Calibration of a complex activated sludge model for the full-scale wastewater treatment plant

In this study, the results of the calibration of the complex activated sludge model implemented in BioWin software for the full-scale wastewater treatment plant are presented. Within the calibration of the model, sensitivity analysis of its parameters and the fractions of carbonaceous substrate were performed. In the steady-state and dynamic calibrations, a successful agreement between the measured and simulated values of the output variables was achieved. Sensitivity analysis revealed that upon the calculations of normalized sensitivity coefficient (Si,j) 17 (steady-state) or 19 (dynamic conditions) kinetic and stoichiometric parameters are sensitive. Most of them are associated with growth and decay of ordinary heterotrophic organisms and phosphorus accumulating organisms. The rankings of ten most sensitive parameters established on the basis of the calculations of the mean square sensitivity measure (δjmsqr) indicate that irrespective of the fact, whether the steady-state or dynamic calibration was performed, there is an agreement in the sensitivity of parameters

Improving the Prediction of Phosphate Dynamics in Biotechnological Processes: A Case Study Based on Antibiotic Production Using Streptomyces coelicolor

The objective of this study is to demonstrate that the accurate mathematical description of phosphate dynamics requires a considerable, but unavoidable, degree of complexity when modelling biotechnological systems. As an example, a model predicting antibiotic production using Streptomyces coelicolor is chosen which had difficulties explaining the phosphate dynamics. The model is enhanced by the implementation of an advanced speciation model and a multiple mineral precipitation framework. Furthermore, a model describing intracellular polyphosphate accumulation and consumption is developed and implemented. Based on the conducted work the improved process model is capable of predicting the phosphate dynamics (RMSE≤ 52h: -90 %, RAD≤ 52h: -96 %) very accurately in comparison to the original implementation, where biomass growth was the only phosphate sink. The description of most other variables was improved by a knowledge-based re-estimation of selected parameters as well. This work contributes to the existing process knowledge of biotechnological systems in general and especially to the antibiotic production with S. coelicolor, which emphasizes the necessity of combining physico-chemical and biological processes to accurately describe phosphate dynamics

Improving the Prediction of Phosphate Dynamics in Biotechnological Processes: A Case Study Based on Antibiotic Production Using Streptomyces coelicolor

The objective of this study is to demonstrate that the accurate mathematical description of phosphate dynamics requires a considerable, but unavoidable, degree of complexity when modelling biotechnological systems. As an example, a model predicting antibiotic production using Streptomyces coelicolor is chosen which had difficulties explaining the phosphate dynamics. The model is enhanced by the implementation of an advanced speciation model and a multiple mineral precipitation framework. Furthermore, a model describing intracellular polyphosphate accumulation and consumption is developed and implemented. Based on the conducted work the improved process model is capable of predicting the phosphate dynamics (RMSE≤ 52h: -90 %, RAD≤ 52h: -96 %) very accurately in comparison to the original implementation, where biomass growth was the only phosphate sink. The description of most other variables was improved by a knowledge-based re-estimation of selected parameters as well. This work contributes to the existing process knowledge of biotechnological systems in general and especially to the antibiotic production with S. coelicolor, which emphasizes the necessity of combining physico-chemical and biological processes to accurately describe phosphate dynamics

Achieving value from process intensification through better process control

The continual economic drive to achieve improved process efficiencies has made process integration and intensification a main stay in process industries ranging from petrochemicals to biotechnology. However, from a process control viewpoint these integrated and intensified processes are much harder to control due to complex process dynamics and/or reduced degrees of freedom. As such, in many process industries the realized efficiency gain through integration and intensification is diminished. The objective of this article is to highlight some of the lessons learnt by the authors during their involvement in controlling intensified processes in different process industries. To this end two industrial troubleshooting case studies of a side-draw distillation column and a divided wall column are presented together with actual problems the facilities faced and how the solutions developed enabled them to be remedied within industrial limitations. This is followed by an analysis of the current process integration and intensification drive of dairy and bioprocesses. Finally the lessons learnt in these diverse process industries are summarized and its implication for process control discussed

Extension of the IWA/COST simulation benchmark to include expert reasoning for system performance evaluation

In this paper the development of an extension module to the IWA/COST simulation benchmark to include expert reasoning is presented. This module enables the detection of suitable conditions for the development of settling problems of biological origin (filamentous bulking, foaming and rising sludge) when applying activated sludge control strategies to the simulation benchmark. Firstly, a flow diagram is proposed for each settling problem, and secondly, the outcome of its application is shown. Results of the benchmark for two evaluated control strategies illustrate that, once applied to the simulation outputs, this module provides supplementary criteria for plant performance assessment. Therefore, simulated control strategies can be evaluated in a more realistic framework, and results can be recognised as more realistic and satisfactory from the point of view of operators and real facilities