Recent hardware developments within industrial digitalization make more evident the need of high‐fidelity models to exploit process system engineering potential in biomanufacturing. Hybrid models, which combine first‐principles (white‐box) and data‐driven (black‐box) approaches, aim to leverage the strengths of both methodologies to mitigate their individual limitations to provide high predictive power. This article explores the integration of hybrid modeling techniques in bioprocess engineering, emphasizing their potential to facilitate product development, accelerate experimental design, enhance process monitoring, drive optimization and catalyze the implementation of digitalization strategies to achieve a more robust and sustainable production in the framework of the goals set by the UN 2030 agenda. In this review, we analyze 270 publications from the early 1990s to 2024, extracting application domains, hybrid‐model architectures, implementation software, and nonparametric output structures. Our findings highlight marked improvements in predictive accuracy, and extrapolation capabilities while revealing persistent challenges in integrating mechanistic and empirical components. This review on hybrid modeling implementations shows their potential to enhance accuracy, calibration, and extrapolation capabilities, highly required for bioprocesses. This work underscores the importance of integrating mechanistic and data‐driven approaches to improve the flexibility and interpretability of models in complex engineering systems, offering significant implications for more sustainable and innovative bioprocesses.

Monte Carlo methods offer a fast, cost-effective approach for modeling environmental systems influenced by random variability. This study applied them to three abiotic cases: (I) water quality in a lentic surface water source, (II) sizing of a homogenization chamber for solid waste treatment, and (III) removal of atmospheric particulate matter by rain. Deterministic models produced wide and inconsistent estimates: BOD₅ concentrations from 5.28 to 19.81 mg/L (275% relative difference), chamber volumes from 24.12 to 116.53 m³, and particulate matter reductions with up to 60 µg/m³ per month variation. Monte Carlo simulations, by contrast, captured system variability and provided more robust outputs: a design value of 94.84 m³ for the homogenization chamber, narrower ranges for BOD₅, and realistic distributions of atmospheric PM concentrations. Results show that reliance on average values introduces strong biases and mathematical incompatibilities, while the Monte Carlo approach yields quantitative predictions that are both accurate and operationally useful. This confirms its relevance as a practical tool for analyzing and designing environmental systems under uncertainty.

To advance the progress of membrane materials and process development, accurate prediction of membrane performance has to be efficiently optimized. Such optimization is challenging when sustainable materials such as hydrophilic biopolymers from renewable resources are used on mixed matrix membrane (MMM) fabrication. The predictions of MMM using phenomenological modified Maxwell models are often locally dependent with high uncertainty levels. In this work, we systematically developed a hybrid model architecture using a delta model design pattern to enhance the prediction of the permeability of chitosan-based MMM for CO2/N2 separation and gain insight in the design of new sustainable membrane separation processes. The parametric model baseline is focused on the modified Maxwell equation including temperature dependence, involving the estimation of two interpretable parameters: void thickness (i.e. the interaction between the components of the membrane matrix) and the rigidification of the dispersed phase in the continuous matrix, with increasing level of complexity. A sequential combination of global and local optimization methods, yielded the best parametric model performance, reducing error by up to an order of magnitude. Then, a multilayer perceptron neural network (MLPNN) is trained using the output error of the parametric model and the introduction of additional experimental input variables. The hybrid model significantly improves the accuracy of gas permeability predictions while keeping interpretability by clarifying input-output correlations of properties not captured by the Maxwell model. This way, this approach can be used to understand the structure-functional relationship of novel membranes and facilitate the development of sustainable membranes and membrane processes.

The global transition toward sustainable energy has intensified research into biofuels, with bioprocess optimization playing a central role in achieving decarbonization goals. Biobutanol, in particular, is a high-value molecule for sustainable fuel applications due to its superior energy density and compatibility with existing infrastructure. However, model-based optimization of its production is hindered by traditional semi-structured kinetic models that often suffer from limited predictive robustness. To address this challenge, within this study we developed a hybrid modeling framework for Clostridium saccharoperbutylacetonicum that integrates mechanistic mass-balance equations with Gaussian Processes (GPs) aiming to describe the biobutanol formation rate. Here, we investigate the effect of data normalization techniques on hybrid model’s prediction capabilities comparing min-max normalization, z-score normalization, and no transformation. For each data treatment strategy, 8, 000 hybrid models were trained using experimental fermentation datasets, and ensemble predictors were constructed to mitigate variability and enhance generalization. Results demonstrate that hybrid models significantly outperform parametric baselines, with the ensemble approach leveling the performance differences across all pretreatment techniques. Ensembles achieved validation RMSE reductions of approximately 10.1% to 10.4%, which are two times higher than the best individual models regardless of the scaling method used. Despite similar RMSE reductions, prediction envelopes varied along data transformations revealing tradeoffs between uncertainty and coverage. Notably, targeted hybridization of the butanol reaction rate propagated performance improvements to other states, such as glucose and biomass. These findings suggest that for hybrid modeling, ensemble design provides greater benefits for robustness than any data normalization technique.

Mechanical properties such as viscoelastic, rheological, and textural properties, particularly adhesiveness and cohesiveness, play a significant role in the optimization of handling, processing and drying of dewatered sludge. However, there is no currently standardized methodology for quantifying these properties, leading researchers to adopt various techniques based on economic feasibility, equipment availability, and research objectives. This review systematically compiles and analyzes the most relevant experimental methodologies for evaluating the mechanical properties of dewatered sewage sludge (DSS). This work presents a general overview of the composition and structure of DSS, followed by a comprehensive bibliometric analysis that revealed literature gaps (particularly in adhesiveness evaluation) and identified regulatory frameworks that incremented interest in researching DSS’ properties in the broader push for sustainability. Consequently, a discussion of the experimental set-ups, data treatment and visualization, advantages and disadvantages, and mathematical models with their physical significance is presented for the different techniques identified. The methods analyzed include oscillatory rheology, penetrometry (cylinder and cone), uniaxial compression tests (TPA), shear testing (e.g., Jenike shear test, Atterberg limit test, direct box shear test, triaxial shear test, unconfined shear test, and vane shear test), tomography and slump tests. By comparing these techniques, this study provides insights into the most appropriate approaches for sludge characterization, emphasizing the need for a unified framework to standardize the evaluation of viscoelastic and textural properties in DSS.

Response surface methodology (RSM) has been a cornerstone for experimental bioprocess optimization, yet its reliance on polynomial equations often fails to capture complex nonlinearities and requires extensive experimental data to attempt capturing them. This study evaluates the enhancement of RSM through machine learning (ML) techniques, specifically Gaussian Processes and Support Vector Machines, across three biological systems: succinic acid fermentation, biobutanol production, and continuous ethanol cultivation. To mitigate the “black-box” nature of ML, explainability measures such as Mutual Information (MI) and Local Interpretable Model-agnostic Explanations (LIME) were integrated to assess feature importance and ensure biological relevance. Results demonstrate that ML models consistently outperform traditional polynomials in predictive accuracy, even when having access to less experimental data for calibration and a reduced space of features, particularly on validation data sets where errors were significantly reduced (e.g., reaching MAPE lower than 1% in simulated experimental scenarios). Furthermore, the application of Model-Based Design of Experiments using Latin Hypercube Designs achieved optimal conditions with up to 50% fewer experimental runs compared to traditional factorial approaches. These findings suggest that coupling ML with explainability tools improves the reliability of finding optimal conditions for bioprocess development while also significantly enhancing operational efficiency by reducing experimental runs, time, and costs.