The pharmaceutical industry is a highly specialized field where strict quality control and accelerated time-to-market are essential for maintaining competitive advantage. Spherical crystallization has emerged as a promising approach in pharmaceutical manufacturing, offering significant potential to reduce equipment and operating costs, enhancing drug bioavailability, and facilitating compliance with product quality regulations. Emulsions, as an enabling technology for spherical crystallization, present unique advantages. However, the quality of spherical crystallization products derived from emulsions is significantly influenced by the intricate interactions between crystallization phenomena, formulation variables, and solution hydrodynamics. These complexities pose substantial challenges in determining optimal operational conditions to achieve the desired product characteristics. In this study, Bayesian optimization (BO) is employed to refine and optimize the operational conditions fo... [more]

The promising properties of lipid nanoparticles (LNPs) as drug carriers have been attracting significant attention in the field of drug delivery. However, further research is still required for a better understanding of their integration in the pharmaceutical industry. The Quality by Design (QbD) approach aims at ensuring the safety and efficiency in the development of new drugs, through an holistic, risk-based approach that gathers all sources of knowledge available about the system under analysis. One key resource of the QbD framework is the rich toolkit of Design of Experiments (DOE), to deepen the understanding of how the synthesis of LNPs by microfluidics can be effectively conducted and controlled. This study aimed to explore and understand the effectiveness of different DOE strategies, through an in silico study focused on the impact of factors related to the LNPs synthesis, namely the molar ratio of each lipid component in the lipidic mixture and the N/P ratio, while also consi... [more]

Understanding the complex dynamics of continuous processes in pharmaceutical manufacturing is essential to ensure product quality across the production line. This paper presents a data-driven modeling approach using Sparse Identification of Nonlinear Dynamics with Control (SINDYc) to capture the dynamics of a continuous direct compression (CDC) tableting line. By incorporating delayed control inputs into the candidate function library, the model effectively captures deviations from steady state in response to dynamic changes. The proposed model was developed by finding a balance between accuracy and sparsity, with focus on the ability to generalize to a wide range of operating conditions.

Reactive crystallization (RC) is a chemical process in which the reaction yields a crystalline product. It is used in various industries such as pharmaceutical manufacturing or water purification. In some cases, RC is the only feasible process pathway, such as the precipitation of certain ionic solids from solution. In other cases, a reaction can become a RC by changing the reaction environment to a solvent with low product-solubility. Despite the utility and prevalence of RC, it is not often emphasized in process design software. There are RC models that simulate the inner reactions and dynamics of a RC, but each has limiting assumptions, and are difficult to integrate with the rest of a process-line simulation. This modeling gap complicates RC process design and limits the exploration of the possible benefits to using RC as well as the ability to optimize a system that relies on it. To fill this gap, we built an open-source, customizable model that can be integrated with other unit o... [more]

This work introduces a Python-based interface that couples cradle-to-gate Life Cycle Assessment (LCA) with advanced process simulations in continuous biomanufacturing, resulting in dynamic process inventories and thus to dynamic LCA (dLCA). The open-source Brightway2.5 framework is used to dynamically track environmental inventories of the foreground process and LCA indicators (e.g. damage to ecosystems according to ReCiPE 2016) from the v3.10 cut-off ecoinvent database. The framework is applied to KTB1, a dynamic MATLAB–Simulink benchmark model of continuous Lovastatin production. 580 data points are computed across four different 24-hour scenarios. The difference between the hourly and the averaged foreground scenario is between 20-30%; a more pronounced deviation is observed when both background and foreground are averaged. The dLCA framework precisely identifies optimal periods for cleaner electricity usage, enabling future work on direct environmental feedback into process control... [more]

Emerging sectors within the biopharmaceutical industry are undergoing rapid scale-up due to the market boom of gene therapies and vaccine platform technologies. Manufacturers are pressured to orchestrate resources and plan investments under future demand uncertainty and, critically, an early-stage process uncertainty for platforms still under development. In this work, a multi-product multi-stage stochastic optimization problem integrating demand uncertainty is presented and augmented with a worst-case optimization approach with respect to process uncertainty. Results focus on a comparison between fixed equipment facilities and modular technologies, highlighting an inherent flexibility of the latter option due to shorter recourse actions for capacity scale-out. The impact of process uncertainty integration is quantified. With more conservative decisions taken in first-stages of the time horizon, expected costs result lower for modular single-use equipment. This suggests that capacity a... [more]

The increased use of pharmaceuticals globally over the past two decades has contributed to an increase in unused pharmaceuticals and a corresponding surge in pharmaceutical waste. Thus, there is an impetus for the development of processes for the recovery of the active pharmaceutical ingredients (APIs) from these unused drugs. This study introduces a decision framework for solvent selection to enable the recovery of APIs using a general separation train where cooling crystallization is the final step. The framework is designed to base solvent selection not just on the solubilities of the formulation contents but also considers the overall recovery that can be achieved in the process. In addition, the environmental sustainability of the framework is analyzed using the process mass intensity metric (PMI). The effectiveness of this framework is demonstrated by using paracetamol (PA) as a model API in a formulation consisting of five of the excipients commonly found in PA formulations. The... [more]

Choosing suitable solvents for crystallisation processes can be a challenging task when developing new pharmaceuticals, given the vast number of candidates available. To streamline this task, however, process modelling tools can be used to systematically probe the behaviour of different crystallisation setups entirely in-silico. In fact, it is possible to couple thermodynamic models with basic solid-liquid equilibria (SLE) principles to determine the impact of key process variables (e.g., temperature and solvent choice) on process performance, prior to conducting lab-scale experiments. In light of this, in this study we have used thermodynamic computational modelling tools (implemented within MATLAB®) to evaluate the cost and environmental impact of different batch crystallisation processes that may be used to manufacture flurbiprofen – a non-steroidal anti-inflammatory drug (NSAID) that can be used to treat various forms of arthritis. To complete this work, we have used the Apelblat e... [more]

Biomacromolecules have intricate crystallisation behaviour due to their size and many interactions in solution and can often only crystallise in narrow ranges of experimental conditions. High solute concentrations are needed for crystal nucleation and growth, exceeding those eluted upstream and therefore preventing the adoption of crystallisation in downstream separation steps. By promoting molecular aggregation and nucleation via a lowered energy barrier, heterogeneous surfaces or templates can relax the supersaturation requirements and widen the crystallisation operating space. Though templates are promising candidates for process optimisation, their experimental testing has generally been limited to small-volume experiments, and quantification of their impact on process intensification and quality metrics at higher volumes remains unexplored. To address the knowledge gap, a model-based investigation of template-induced protein crystallisation systems through evaluation of key metric... [more]

This work presents a kinetic model of drug substance synthesis considering slug flow characteristics in Stevens oxidation. The developed model is also applied to determine the feasible range of the process parameters. Flow experiments were conducted to obtain kinetic data, varying the inner diameter, temperature, and residence time. A kinetic model was developed for the change in concentrations of the starting material, products, and catalysis. In the kinetic model, slug flow was considered by including a volumetric mass transfer coefficient during this flow. In the initial experiments, early-stage kinetic data were insufficient, conducting additional experiments at shorter residence times. Furthermore, the initial model could not reproduce the residual of the starting material, introducing the oxidant consumption that inhibits the starting material consumption and improving the initial model. The improved model could reproduce experimental results and demonstrated that, as the inner d... [more]

This work presents a hybrid-modeling approach to monoclonal antibody (mAb) production processes design using automated bioreactor equipment. Experimental data covering a reasonable yet broad range of cultivation conditions was collected by the equipment. Using the data, a model applicable to a wide range of cultivation conditions was developed. In the modeling, a data-driven model was applied to describe complicated/unknown phenomena that could not be captured by previously proposed mechanistic models. In the hybrid model, while maintaining the mass balance of the mechanistic model, coefficients of the equations were estimated with random forest regression. Overall, the model could describe the dynamic concentration profiles of product mAb and quality-relevant impurities depending on the media/glucose feeding conditions. The model was then applied to determine an optimal condition that maximized product mAb concentration and satisfied the impurity constraints. The work can further supp... [more]

Maintaining sufficient amounts of dissolved oxygen throughout a microbial cultivation is a classic control task in bioprocess engineering to avoid negative effects onto cell physiology and productivity. But traditional PID-based algorithms struggle when faced with pulsed substrate additions and the resulting sudden surge of oxygen uptake. In this work a nonlinear MPC is employed and compared to a PID setup for the cultivation of an E. coli strain exposed to intermittent feeding. Both controllers are tuned for a fast pulse response combined with efficient and robust control action. Their performance was tested in-silico with isolated feed pulses, as well as throughout a full cultivation run. Further, the effects of parameter uncertainty were investigated to assess the impact of a model-plant mismatch. The results showed that the predictive nature of the MPC is well suited for maintaining the dissolved oxygen levels above a threshold and outperforms the PID in almost all investigated sim... [more]

This study addresses the challenge of predicting final properties in batch processes with highly uneven durations, using the ibuprofen production process as a case study. Novel methodologies are proposed and compared against traditional regression algorithms, which rely on batch trajectory synchronization as a pre-processing step. The performance of each method is evaluated using established metrics. The data for this study were generated using Aspen Plus V12 simulation software, focused on batch reactors. To handle the unequal-length trajectories in batch processes, this research constructs a dual-transformer deep neural network with multi-head attention and layer normalization mechanism to extract shared information from the high-dimensional, uneven-length manipulated variable profiles into latent space, generating equal-dimensional latent codes. As an alternative strategy for representation learning, a dual-autoencoder framework is also employed to achieve equal-dimensional represen... [more]

Large quantities of used cooking oil (UCO) are produced globally, primarily in densely populated urban centers. Although UCO is highly heterogeneous due to degradation during cooking, it still contains a significant fraction of triacylglycerols (TG) that could be used as raw materials in oleochemical biorefineries. A major challenge in reintegrating this residue into productive cycles is the presence of free fatty acids (FFA), which can affect subsequent catalytic or enzymatic transformations. Conventional processes for FFA removal are energy-intensive, require alkaline feedstocks, and generate problematic residues. To overcome these issues, alcoholic extraction of FFA is considered a promising pretreatment for UCO, enabling the extraction of FFA for subsequent esterification. In this regard, liquid-liquid film contactors (LLFC) have shown potential to intensify FFA extraction because they operate under mild conditions and at laminar flow regime, reducing energy consumption and enhanci... [more]

Extended Dynamic Mode Decomposition (EDMD) and its variant, the pqEDMD, which uses a p-q-quasi norm reduction of polynomial basis functions, are attractive tools to derive linear operators approximating the dynamic behavior of nonlinear systems. This study highlights how this methodology can be applied to data-driven modeling and control of bioprocesses by discussing the selection of several ingredients of the method, such as the polynomial basis, order, data sampling, and preparation for training and testing, and ultimately, the exploitation of the model in linear model predictive control.

The scale-up of processes with complex fluid flow presents significant challenges in process engineering, particularly in fermentation. Computational fluid dynamics (CFD) is a crucial tool for accurately modelling the hydrodynamic environment in bioreactors and understanding the effects of scale-up. This study utilizes Higher Order SVD (HOSVD), which is the multidimensional extension of Singular Value Decomposition (SVD), to identify the dominant structures (modes) of fluid flow in CFD data of fermentation process simulations. Similarly to Proper Orthogonal Decomposition (POD), also based on SVD, this method can be used to identify the dominant structures of fluid flow, and additionally explore the scale parameter space. As a first test case, we examined five scales of a reciprocally shaken flask bioreactor, from 125 mL to 10 L, specified using basic empirical scale-up rules. Results indicate a common set of spatial modes across all scales, thus confirming that the scale-up method assu... [more]

The Fourth Industrial Revolution (Industry 4.0) brings a new chapter at dairy sector. Dairy 4.0 technologies are based on Big Data Analysis, Internet of Things, Robotics and Machine Learning. The usage of smart technologies to processing and analyzing complicated massive data has a significant impact in automation, optimization, functional costs and innovation. Artificial Intelligence tools are applied from dairy farms and production lines – including packaging- to supply chain. The aim of this paper is to demonstrate the most used applications of Machine Learning in dairy production so as to enhance the sustainability and the quality of dairy products. The most significant Machine Learning applications integrate machine vision, smart environmental sensors, activity collars, thermal imaging cameras, and digitized supply chain systems to facilitate inventory management. Challenges like milk adulteration, animal diseases, mastitis, traceability and supply chain losses are also addressed... [more]

Polyhydroxyalkanoates (PHAs) are microbial bioplastics that are fully biodegradable, biocompatible and can be produced by renewable feedstocks through fermentation. These are all desirable attributes for the replacement of current fossil-based plastics. Strong mathematical models describing bioprocesses are invaluable tools that can be used for enhancing bioprocess understanding as well as optimization. In this study, polyhydroxybutyrate (PHB), by Cupriavidus necator DSM 545 was produced using glycerol and ammonium sulphate (AS) as the sole carbon and nitrogen sources, respectively. In addition, a kinetic bioprocess model was developed. The kinetic parameters of the model were calibrated with five fermentation experiments with different initial conditions (e.g. variable glycerol and AS concentrations) in order to properly establish the inhibition regions and provide a generalized model as much as possible. The model was successfully validated by three independent experiments, two with... [more]

This study provides standardized models for the chemical characterization of complex streams, ensuring the necessary adaptations while considering the differences in biomass types and forms. Several datasets are compiled and examined to establish a valid representation of the mixture, according to industry accepted standards and laboratory protocols. For reliable property estimation, correlations of key biomass properties are obtained from both computational models and experimental measurements. Existing data are used to create datasets for the biomass and the biocrude streams. This model builds upon existing knowledge and data technologies with emphasis on hydrothermal liquefaction (HTL). The proposed approach shows potential as a starting point for the design and modelling of more biorefinery-associated technologies. Sludge and pine wood are used as case studies for biomass feedstocks. Two biocrude samples are employed for biocrude characterization. The performance of the developed o... [more]

Soybean is a highly valuable global commodity due to its versatility and numerous derivative products. During harvest, all non-seed materials become “straw”. Currently, this waste is primarily used for low-value purposes such as animal feed, landfilling, and incineration. To address this, the present work proposes a conceptual biorefinery aimed at converting soybean straw into higher-value products. The study began with data collection to identify potential conversion routes. Based on this information, a superstructure was developed, comprising seven conversion routes: four thermochemical routes (pyrolysis, combustion, hydrothermal gasification, and liquefaction), two biological routes (fermentation and anaerobic fermentation), and one chemical route (alkaline extraction). Each process was evaluated based on product yields, conversion times, and associated capital and operating costs. Using this data, an MILP (Mixed-Integer Linear Programming) optimization model was built in Pyomo usin... [more]

The global reliance on fossil-based solvents has driven the search for sustainable alternatives. This study employs the IBSS® CAMD tool to evaluate building blocks derived, directly or indirectly, from agricultural residues - specifically orange and coffee wastes-, to replace toluene in furfural extraction. A three-stage methodology was implemented: (1) identification of potential building blocks from residues, (2) multi-objective optimization using genetic algorithms and group contribution models for properties calculation, and (3) analysis of the resulting candidates based on performance indicators. A total of 13 families were evaluated, generating millions of candidates. Target properties included minimization of Hansen Solubility Parameters (HSP) distance, boiling point above 250°C, melting point below 10°C, flash point above 61°C, and octanol-water partition coefficient (log(kow)) below 3. The most promising candidates were derivatives of glycerol (performance: 0.9986), limonene (... [more]

Renewable energy and energy efficiency are increasingly recognised as crucial for creating new economic opportunities and mitigating environmental impacts. Anaerobic digestion (AD) transforms organic materials into a clean, renewable energy source. Co-digestion of various organic wastes and energy crops addresses the disadvantages of single-substrate digestion, increasing production flexibility yet adding process complexity and sensitivity. This study employs a two-pronged approach to optimise biogas production while considering global warming potential: a nonlinear programming (NLP) model for dynamic system economic optimisation with a model predictive control (MPC) strategy for precise temperature regulation within the digester. The NLP model integrates a combined heat and power (CHP) system to leverage dynamic electricity, heat, and gas prices, accounting for physical and economic parameters such as biomethane potential, chemical oxygen demand, and substrate density. A cardinal temp... [more]

The use of metabolic networks is extremely valuable for design and optimisation of bioprocesses as they provide great insight into cellular metabolism. Within bioprocess optimisation, they have enabled better (economic) objective performance through more accurate network-based models. However, one of the drawbacks of using metabolic networks is their underdeterminacy, leading to non-unique flux distributions. Flux Balance Analysis (FBA) reduces this issue by making assumptions on the behaviour of the cell. However, for metabolic networks of higher complexity, can still struggle with underdeterminacy. Metabolic network reduction can remove or greatly reduce this effect but can be difficult, especially when data is limited. Structural analysis of the metabolic network through Elementary Flux Modes (EFM) or Extreme Pathways (EP) can help locate the relevant information within the network. This work presents a metabolic network reduction approach based on the EPs that best explain a small... [more]

Global biomanufacturing is projected to expand rapidly in the coming decade due to advancements in DNA sequencing and manipulation. However, the complexity of cellular behaviour introduces difficulty in modelling and optimizing biomanufacturing processes. Phenomenological models that represent the physics of the system in empirical equations suffer from poor robustness, while their machine learning (ML) counterparts suffer from poor extrapolative capability. On the other hand, hybrid models allow us to leverage both physical constraints and the flexibility of ML. This work describes a new approach for hybrid modeling that integrates the time-variant parameter estimation and ML model training into a singular step. We implement this approach on a proposed scheme for the cell-mediated conversion of a lignin derivative into a bioplastic precursor and show that our integrated hybrid model outperforms the traditional two-step hybrid, phenomenological, and ML model counterparts. Lastly, we de... [more]

Most industrial fermentations in food and drink use a single, high purity sugar as a substrate. These pure substrates are more expensive and less sustainable than mixed substrates, that can be derived from agricultural byproducts such as straw. However, use of mixed substrates in fermentation leads to challenging modelling and parameter estimation problems, particularly when much academic research, intended to inform industrial applications, uses batch fermentations, while large-scale fermentation is usually continuous, thanks to its cost and productivity advantages. Our findings highlight key challenges in using batch-derived experimental data to inform models of the continuous fermentation processes used at industrial scale. Extrapolating from data obtained in batch to continuous fermentation is risky, as models with near-equivalent data-fit and predictions in a batch context give very different predictions for continuous culture. For continuous fermentations to switch to mixed subst... [more]