Recombinant protein production in Escherichia coli is a widely used system in industry for biopharmaceuticals, enzymes or other proteins. For protein expression, lactose poses as a more favorable and cost-effective induction agent over the common IPTG trigger. It imposes less stress on the cells and is fully metabolizable by the strain used. Therefore, lactose serves as an additional substrate source and adds a degree of freedom through tunable induction levels. To harness this opportunity, a physiological bioprocess model was created, describing the growth and production dynamics of this 2-feed system. Green fluorescent protein is expressed as a model protein in a fed-batch process using glucose as the main substrate and lactose as the digestible inducer. A suitable production kinetic is chosen by fitting a number of models to a collected dataset. The resulting model is used to highlight opportunities for improved process design and control of a 2-stage fed-batch process. It emphasize... [more]

Bioprocesses are inherently multiscale, spanning intracellular metabolism to production-scale reactors. Simulation models that integrate these scales offer potential strategies to study the effect of changing metabolic states and enable efficient integration of biological knowledge gathered from lab-scale experiments. In this study, we demonstrate the potential of such simulation model towards the production of mevalonate, an important pharmaceutical drug compound produced through fermentation of a fungal species Aspergillus terreus. We integrate a genome-scale metabolic model of the organism with a plant-wide simulation model for the bioprocess that encompasses several upstream and downstream unit operations. Through this integration, we identify potential targets for metabolic engineering towards increased product flux and simultaneously estimate the associated oxygen requirements. This framework serves as a foundation for developing digital twins of bioprocesses that bridges strain... [more]

Identifying cost-optimal yet environmentally friendly crystallisation processes in the production of small molecule pharmaceuticals is a highly complex task, since multiple solvent systems often exist which could be used to purify a given drug to a similar standard. It is, however, rarely possible to test each of these solvent systems within a laboratory setting, since this would be time-consuming and incur large material costs. It has, therefore, been suggested that process modelling tools should be used to screen different crystallisation processes available to produce a new drug prior to studying them experimentally - essentially allowing a shortlist of promising process candidates to be created. Indeed, it has been shown by several authors that this sort of work can be conducted without any need to establish the crystallisation kinetics associated with each drug-and-solvent combination considered: the reason being that crystallisation processes may be defined as simple solid-liquid... [more]

Managing cellular resources, especially transcription and translation machinery, constitutes a significant constraint in the effective synthesis of useful bio-compounds from synthetic gene circuits. Although light-based optogenetic control approaches provide precise temporal and spatial control that can balance growth and production. The increased complexity adds on to the competition for cellular resources such as ribosomes and RNA polymerases. The optimization of regulatory elements in bioengineering is a vital task, as the selection of promoters and ribosome binding sites (RBS) directly affects transcriptional initiation rates and translation efficiency, hence influencing resource allocation. The vast number of potential parameter combinations requires systematic approaches to narrow the design space and determine which bioparts most significantly influence system performance. Furthermore, it is crucial to ascertain the uncertainty regarding which bioparts exert the most significant... [more]

This work presents Pareto Front Guided Sampling (PFGS), a model-guided Design of Experiments (DoE) strategy for bioprocess development that makes the exploration-exploitation trade-off explicit and integrates human expertise into experiment selection. Starting from an initial experimental design, PFGS fits a probabilistic surrogate and then proposes new experiments by solving a multi-objective design problem that simultaneously rewards (i) high predicted performance (posterior mean) and (ii) high information gain (posterior uncertainty). Rather than collapsing this trade-off into a single acquisition value, PFGS generates a Pareto set of candidate experiments, that reflect different balances between improvement-seeking and learning. To prevent wasted runs, an automated screening step is performed to remove candidates in (i) low predicted-mean regions unlikely to yield near-optimal performance and (ii) low-uncertainty regions already well explained by the surrogate, concentrating effort... [more]

Computer-aided molecular design (CAMD) is an efficient product design method that is gradually attracting attention at present. It mainly uses data mining technology to extract information from the existing chemical molecular data and use this information to generate potential excellent molecules. However, the key that CAMD can truly provide accurate and reliable results lies in the efficient utilization of chemical data. In this paper, a series of chemical data analysis methods based on molecular similarity are proposed to enhance the data utilization efficiency of CAMD, which mainly includes 3 applications: adaptive modeling, reliability assessment and advanced data preprocessing including molecular recommendation, data consistency test and data augmentation. We propose specific methodology for each application, and use multiple cases to verify the effect. The results show that molecular similarity can help to improve the accuracy of property prediction at the data level, provide qua... [more]

Oligonucleotides are short, sequence-defined nucleic acid chains with major therapeutic and diagnostic potential. Their industrial production is currently dominated by solid-phase oligonucleotide synthesis (SPOS), which suffers from mass-transfer limitations, limited scalability, lack of real-time process monitoring, and high process mass intensity. Membrane-enhanced liquid-phase oligonucleotide synthesis (LPOS) has emerged as a scalable alternative, in which oligonucleotide chains are grown on soluble anchors and organic solvent nanofiltration is used (OSN) to remove excess reagents and by-products between each reaction steps. However, diafiltration also introduces a risk of large cumulative product loss over multiple addition cycles, which requires fine-tuning of design and operational strategies in practice. This paper presents the results of a comparative assessment of two LPOS variants with either a single- or dual-stage diafiltration against a state-of-the-art SPOS, within a unif... [more]

Accurate calibration of spectroscopic measurements is essential for reliable real-time monitoring and control of crystallization processes. In this work, calibration strategies for Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy were systematically evaluated for concentration monitoring in batch cooling crystallization of paracetamol in ethanol. Linear regression (LR), Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and symbolic regression (SR) were compared using both peak-based features and full spectral representations. Peak-based models provided a transparent baseline, with peak-area-based models consistently outperforming peak-height-based models. For LR, incorporating multiple absorption bands reduced the mean squared error (MSE) by nearly one order of magnitude compared to single-peak models. Using the same peak-based inputs, SR further improved performance, reducing prediction bias at high concentrations and yieldin... [more]

Continuous pharmaceutical manufacturing (CM) offers improved quality assurance, operational agility, and supply resilience, yet process development remains dominated by expensive trial-and-error experimentation and high-dimensional space exploration. Motivated by ICH Q13, we develop a generative inverse-design framework that maps target product quality to feasible process recipes for an integrated twin-screw wet granulation and segmented fluidized-bed drying line. The framework integrates three components: (i) a Conditional Variational Autoencoder (CVAE) generator that proposes process parameter sets conditioned on desired Critical Quality Attributes (CQAs), (ii) a Gaussian Process (GP) surrogate validator that screens candidates for manufacturing feasibility, and (iii) SHapley Additive exPlanations (SHAP) to interpret the generated designs. Training data were produced from a validated gPROMS digital twin of the Diamond Pilot Plant (DiPP) ConsiGma-25 line, covering liquid -to-solid rat... [more]

Mathematical models play a central role in biopharmaceutical manufacturing, especially within the Quality by Design framework. For these models to be effectively used in optimization tasks, they must be both reliable and capable of delivering results in an affordable computational time. This work proposes a strategy to model aggregate formation during viral inactivation in the context of monoclonal antibody downstream processing. These units often display mixing-sensitive behavior because aggregation kinetics is controlled by local pH, whose spatial heterogeneities arise from titrant addition at a defined feed point. To address this challenge, compartment models (CMs) are employed. This modeling approach captures spatial inhomogeneities within the unit by leveraging flow-exchange information derived from a single steady-state Computational Fluid Dynamics (CFD) simulation involving only the solution of mass, momentum and turbulence equations. Results obtained by comparing compartment mo... [more]

Crystallisation is a promising and scalable alternative to chromatography for biologics purification. However biologics such as proteins and peptides often crystallise only in narrow operating windows, limiting process flexibility. Template-induced crystallisation can lower supersaturation requirements and expand feasible operating ranges, yet the template dependence of nucleation and growth kinetics remains difficult to parametrise mechanistically. To address this, we develop and experimentally validate uncertainty-aware hybrid models for lysozyme crystallisation on hydroxyl- and carboxyl-functionalised silica templates. A mechanistic population-balance model is coupled to a data-driven regressor that maps operating conditions and template variables to effective nucleation and growth rates. We compare a neural network baseline against a structured neural power-law surrogate, which embeds a supersaturation-dependent power-law form. Both hybrid models are trained in-the-loop via differe... [more]

The objective of this work is to investigate strategies for the calibration of crystallization models aimed at predicting particle size distributions (PSDs) of active pharmaceutical ingredients (APIs) when using industrial datasets, which are limited in terms of number or information for the modeling exercise. In this work, the calibration task relies on two kinds of measurements, commonly performed in industrial crystallization practice: offline measurements of PSDs and API solute concentration carried out only at the beginning and at the end of experiments, and online measurements of chord length distributions (CLDs). Particularly, a strategy is proposed to use CLDs data from focused beam reflectance measurement (FBRM) probes as proxies of the PSD, which is the main key performance indicator for the model exercise. Industrial data concerning a seeded batch cooling recrystallization of an API in an organic solvent are used as a case study. The PharmaPy process simulator is used for pa... [more]

Traditional centralised manufacturing offers efficient economies and broad market reach but faces increasing limitations with the rise of complex products requiring rapid localised delivery and greater supply chain resilience. The logistics demands of hospital-compounded therapies expose vulnerabilities in existing infrastructure, accentuating the need for rigorous evaluation of alternative paradigms. This study investigates the comparative performance of centralised and decentralised pharmaceutical manufacturing models, applying an agent-based simulation framework designed for specialised or time-sensitive drug product orders. The work implements an agent-based simulation to model both centralised and decentralised scenarios using key structural, resource, and demand parameters identified within the supply chain ecosystem. Comparison criteria include labour requirements, sustainability (as measured by environmental emissions and operational efficiency), and end-to-end supply chain lea... [more]

Mathematical models are increasingly used in pharmaceutical process development within quality-by-design (QbD) frameworks to reduce experimental effort and enable rational process design. However, model validation is still often based on deterministic performance indicators, which do not explicitly account for experimental variability, measurement noise, and model uncertainty. This work proposes an uncertainty-aware framework for model validation in pharmaceutical processes that quantifies predictive reliability in probabilistic terms, consistent with regulatory concepts. The framework explicitly integrates uncertainty in operating conditions, measurements, and model parameters, and evaluates model performance based on the probability that prediction satisfy predefined acceptance criteria rather than on single-point accuracy indicators. An in-silico case study of crystallization was performed to demonstrate the approach, where synthetic experimental data with controlled uncertainty wer... [more]

Ensuring reliable operations of modern power systems under uncertainty remains a key challenge, particularly due to the non-convex nature of Alternating Current (AC) power flow equations and the presence of high-impact disturbances from load and renewable generation fluctuations. In this work, we address the robust AC Optimal Power Flow (AC OPF) problem by developing a robust spatial branch-and-bound (RsBB) algorithm. Robustness is achieved by identifying worst-case uncertainty realizations and iteratively incorporating robust cuts to eliminate constraint violations. To accelerate convergence and tighten bounds, Optimization-Based Bound Tightening (OBBT) and Feasibility-Based Bound Tightening (FBBT) techniques are integrated into the framework. The proposed method yields global robust solutions with certified optimality gaps below 0.01% across standard PGLib test cases.

Wine consumers are increasingly health- and environmentally conscious. At the same time, white wine and rosé drinkers favour freshness and varietal aromas, which requires low-temperature regimes that extend fermentation time and increase energy demand. Additionally, global warming accelerates grape ripening which increases alcohol level in wine. To reduce cost and alcohol levels while maintaining quality, predictive tools that forecast how fermentation conditions impact fermentation time, and primary and secondary metabolite concentrations, can provide practical benefits to wineries by expediting oenological decisions-making and in turn reducing energy demand. Additionally, literature highlights static models in smart manufacturing suffer from performance degradation with data drift. In light of this, we successfully developed and evaluated pipelines for the automated design and training of three ML methods - support vector regression, random forest and artificial neural networks - to... [more]

Resilience is becoming a top priority across industrial sectors, with increasing pressures to assess it systematically. In this work, we present an optimization-based framework for proactive design and planning under uncertainty of multi-product manufacturing networks, and testing of the reactive strategies available to withstand unforeseen disruptions. Specifically, the design problem is formulated as a two-stage stochastic optimization, integrating multi-period planning and scheduling, aimed towards mitigation against uncertainty. Designs are then fixed and tested through simulated outcomes from out-of-sample uncertainty distributions, with feasibility of operation monitored through the time-to-recover post disruption. Infeasibility triggers a scenario-update procedure via ??-means clustering, whereby critical uncertainty information based on simulated outcomes is integrated in the proactive planning step, including low-probability high-impact scenarios. Modular and non-modular desig... [more]

Although rare, unexpected events such as financial crises, geopolitical conflicts, and pandemics have reshaped reality in recent years. Despite their strong potential to affect the energy transition, such events are still largely overlooked in energy planning studies. Ignoring them can lead to poorly informed decisions that may jeopardize the transition. Identifying early-stage decisions that remain robust under unexpected events is therefore essential. To address this challenge, EnergyScope Pathway, a whole-energy system model with limited foresight, is applied to Belgium. To increase the likelihood of a successful transition, the Modeling to Generate Alternatives approach is used to diversify early-stage decisions in 2035. These alternatives are allowed to be up to 10% more expensive than the cost-optimal solution. However, the large number of alternative designs is difficult to navigate for decision makers. To address this, a decision-support framework based on the Multi-Armed Bandi... [more]

Stochastic programming (SP) is a powerful framework for decision-making under uncertainty, but its practical adoption in industry is often hindered by the difficulty in understanding the causal relationships that drive optimal solutions. In the two-stage SP, strategic first-stage decisions are coupled with operational second-stage recourse decisions. When the number of scenarios under consideration is large, understanding the direct link between the uncertainty realization and optimal recourse strategy becomes computationally and cognitively demanding. Common approaches to improve interpretability include trained classification trees or scenario reduction, replacing the large scenario set with a representative subset. This is often achieved through post-hoc clustering (e.g., k-means) based on uncertainty realizations or optimal recourse decisions. While useful, these methods only provide a statistical approximation of the solution space and may fail to reveal the underlying structural... [more]

Computer-Aided Process Engineering (CAPE) has transformed how we analyze, design, and optimize energy processes. Yet, even advanced models rest on uncertain ground: their reliability depends on how well future operating environments are described-environments that are dynamic, complex, and deeply uncertain. In practice, uncertainty is often reduced to local parameter variations, driven by limited data, computational burden, and overconservative robust formulations. This narrow treatment creates a false sense of confidence: Designs that perform well in theory often fail in real-world operation. In a century marked by economic, climatic, and technological volatility, designing under uncertainty is no longer optional; it is essential.We have developed approaches that place uncertainty at the core of energy process modeling and design. This paper provides an overview of these methods and how uncertainty can be explicitly represented, quantified, and embedded into the design process.We pres... [more]

This work develops a high-spatial resolution multi-stage stochastic programming (MS) model for power grid capacity expansion that co-optimizes generation, transmission, and energy storage system investments under uncertainty. Traditional two-stage stochastic programming (TS) models determine all investments in a single stage, limiting their ability to adapt to changing conditions such as evolving capital costs, policies, or supply chain disruptions. In contrast, the proposed MS formulation introduces sequential decision stages where partial information is revealed over time, allowing for adaptive, scenario-contingent investments. We compare TS and MS formulations using a modified IEEE 24-bus case study to quantify the Value of the Multistage Solution, which measures the economic benefit of allowing investment decisions to adapt over time as uncertainty is progressively resolved. Results show that while MS models are computationally more challenging, they achieve lower expected costs an... [more]

Thermal management and heat transfer optimization remain central challenges in next-generation concentrated solar power (CSP) systems employing solid particles for thermal energy storage and heat transfer. Conventional particle receiver concepts, such as fluidized beds and falling particle curtains are constrained by limited particle-wall contact, flow instabilities, and restricted operating temperature. This work presents a combined computational and experimental investigation of a gravity-driven dense-phase moving packed bed receiver featuring a flat conduit geometry and sub-millimeter particles. A multiphase modeling framework is developed and validated against pressure-drop measurements and particle velocity data obtained from dedicated experimental setups. The validated model is subsequently used to quantify dense-flow stability and thermal performance under indirect heating conditions. Results demonstrate stable dense-phase operation with particle volume fractions of approximatel... [more]

Hydrogen is increasingly recognized as a vital energy carrier for a sustainable future. Among the various methods for hydrogen production, alkaline water electrolysis (AWE) stands out as a well-established and commercially viable option. However, their more effective deployment requires more advanced, portable, and scalable designs. This study explores systematic model-based shape optimization of the next generation AWE based on computational fluid dynamic (CFD) aimed to enhance the hydrodynamics and electrochemical performance. Several design geometries and arrangements were proposed including flow baffles to enhance hydrodynamic and facilitate detachment of oxygen and hydrogen bubbles. The findings indicate that the optimal design and location of the baffles improve fluid mixing and enhance bubble detachment, resulting in a more uniform electrolyte distribution and decreased concentration polarization. Several key performance indicators were considered to analyse the performance of p... [more]

The manufacturing industry is the second largest emitter of CO2, with the cement industry being one of the main contributors (7-8 % of the global emissions). Carbon capture and utilization (CCU) technologies are promising decarbonization solutions for the cement industry, addressing both fossil fuel-related (40 %) and process-derived emissions (60 %). Within a cement plant, producing synthetic natural gas (SNG) from captured CO2 is particularly suitable, as it is sufficient to fully replace solid fuels in the rotary kiln. On the other hand, the use of zero-carbon fuels, such as green ammonia, is also recognized as a promising approach for decarbonization. In this work, a superstructure was developed to explore alternative routes for producing SNG and green ammonia from CO2 and N2 in cement plant flue gas, respectively. The routes were modelled in Aspen Plus® V14, and their economic viability was assessed. Currently, the most promising route, at a cost of 109 €/tonne of flue gas, involv... [more]

Industrial decarbonization has heightened interest in electrifying major chemical processes, but existing planning methods typically assume fixed electricity prices and overlook how industrial power use affects the grid. This work introduces a grid-aware optimization framework that captures two-way interactions between industrial electricity usage and the power flows within the grid. We use the DC Optimal Power Flow (DC-OPF) model to generate Locational Marginal Prices across refinery demand levels and embed a surrogate reflecting the relationship between the power demand and the prices into an operational optimization problem for a partially electrified refinery. The surrogate model is embedded within the optimization problem using disjunctive reformulations and off-the-shelf packages such as OMLT (Optimization and Machine Learning Toolkit). In a case study considering an oil refinery with installed electric boilers, electrolyzers, H2 storage, and post-combustion carbon capture infras... [more]