Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges

Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an "optimal" model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.

Dynamic modelling of tetrazolium-based microbial toxicity assay-a parametric proxy of traditional dose-response relationship

Microbial toxicity of test substances in tetrazolium assay is often quantified while referring to their IC₅₀ values. However, the implication of such an estimate is very limited and can differ across studies depending on prevailing test conditions. In this work, a factorial design-based end-point microbial toxicity assay was performed, which suggests a significant interaction (P= 0.041) between inoculum and tetrazolium dose on formazan production. Subsequently, a dynamic model framework was utilized to capture the nonlinearities in biomass, substrate, formazan profiles and to project the toxicant inhibition parameter as a robust alternative to IC₅₀ value. Microbial growth, glucose uptake and formazan production in the presence or absence of toxicant (Cu²⁺) from designed batch experiments were used for sequential estimation of model parameters, and their confidence intervals. A logistic growth model with multiplicative inhibition terms for formazan content and toxicant concentration fits the experimental data reasonably well (R²>0.96). Dynamic relative sensitivity analysis revealed that both microbial growth and formazan production profiles were sensitive to toxicant inhibition parameter. The modelling framework not only provides a better insight into the underlying toxic effect but also offers a stable toxicity index for the test substances that can be extended to design a versatile, robust in vitro assay system.

Special Issue on “Process Modelling and Simulation”

Collecting and highlighting novel developments that address existing as well as forthcoming challenges in the field of process modelling and simulation was the motivation for proposing this special issue on “Process Modelling and Simulation” in the journal Processes [...]

Monitoring Chemical Processes Using Judicious Fusion of Multi-Rate Sensor Data

With the emergence of Industry 4.0, also known as the fourth industrial revolution, an increasing number of hardware and software sensors have been implemented in chemical production processes for monitoring key variables related to product quality and process safety. The accuracy of individual sensors can be easily impaired by a variety of factors. To improve process monitoring accuracy and reliability, a sensor fusion scheme based on Bayesian inference is proposed. The proposed method is capable of combining multi-rate sensor data and eliminating the spurious signals. The efficacy of the method has been verified using a process implemented at the Dow Chemical Company. The sensor fusion approach has improved the process monitoring reliability, quantified by the rates of correctly identified impurity alarms, as compared to the case of using an individual sensor.

Strategic Framework for Parameterization of Cell Culture Models

Global Sensitivity Analysis (GSA) is a technique that numerically evaluates the significance of model parameters with the aim of reducing the number of parameters that need to be estimated accurately from experimental data. In the work presented herein, we explore different methods and criteria in the sensitivity analysis of a recently developed mathematical model to describe Chinese hamster ovary (CHO) cell metabolism in order to establish a strategic, transferable framework for parameterizing mechanistic cell culture models. For that reason, several types of GSA employing different sampling methods (Sobol’, Pseudo-random and Scrambled-Sobol’), parameter deviations (10%, 30% and 50%) and sensitivity index significance thresholds (0.05, 0.1 and 0.2) were examined. The results were evaluated according to the goodness of fit between the simulation results and experimental data from fed-batch CHO cell cultures. Then, the predictive capability of the model was tested against four different feeding experiments. Parameter value deviation levels proved not to have a significant effect on the results of the sensitivity analysis, while the Sobol’ and Scrambled-Sobol’ sampling methods and a 0.1 significance threshold were found to be the optimum settings. The resulting framework was finally used to calibrate the model for another CHO cell line, resulting in a good overall fit. The results of this work set the basis for the use of a single mechanistic metabolic model that can be easily adapted through the proposed sensitivity analysis method to the behavior of different cell lines and therefore minimize the experimental cost of model development.