Advancing student understanding of process design requires a balanced integration of theoretical knowledge with real-world industrial applications. This study introduces system design thinking-based learning through an engineering clinic approach that bridges the gap between classroom concepts and chemical engineering practice. Using an industrial multiproduct oil pipeline operation as a case study, students are exposed to real-world industrial systems, identify bottlenecks in a process, draw similarities between systems at different scales, and implement control strategies to address a practical industrial problem. In this study, we highlight the collaborative efforts between faculty, students, and industry partners to provide experiential learning in process design, modeling, and control to address the challenge of minimizing product loss during flushing operations in multiproduct petroleum pipeline systems.

Recurrent neural nets (RNNs) are now commonly used for the surrogate modeling of process systems, leading to better control and faster real-time optimization. However, when trained with small training data sets, most experiments show that RNNs exhibit poor generalization abilities outside the range of the training data space. Nonetheless, recent advances in deep learning research have shown that certain characteristics of the loss landscape of trained models, such as the flatness around the local minimum, tend to relate to generalization ability. This paper investigates this phenomenon for the case of RNN surrogates of the well-known Three Tanks case study, which is representative of many continuous processes. We trained a total of 200 LSTMs (long short-term memory networks) differing in initialization, architecture, and training dynamics on the same data of 500 samples. The number of model parameters ranges from 238 to 11, 353. We estimated the loss curvature of each trained model usi... [more]

This study investigates control strategies for the flexible operation of heat pump-assisted distillation processes, focusing on the heat integrated distillation column configuration. The methanol/water separation system was selected as a case study and modelled to achieve 99.9 wt% AA-grade methanol purity. A limiting piece of equipment for flexible operation of heat pump assisted distillation is the compressor. To assess its impact on flexible operation, dynamic simulations in Aspen Dynamics were conducted for two heat integrated distillation column control strategies: one using fixed compressor duty and one using variable compressor duty. The control performance for a 20% throughput disturbance, as well as for a 50% turndown ratio scenario was investigated. Results show that fixed-duty operation maintains robust stability and rapid disturbance recovery even at 50% turndown, while variable-duty operation delivers higher efficiency for moderate load changes but cannot sustain low-load s... [more]

Today, beverage production plants are planned and designed from the material-handling context as a packaged-goods production facility, not as a process plant. Therefore, a lot of potential for optimization exists. This paper presents a new approach to the design of beverage production plants according to the design of process plants. A component library for the simple creation of beverage production plant process models is developed. All steps in the plant design process can be accelerated and automated to be used for the high number of existing and new installations around the world. As first use case an energy optimization upgrade for existing Carbonated-Soft-Drink production lines is described to save cooling and heating energy in warm climates.

Nonlinear model predictive control (NMPC) can substantially improve performance and constraint handling for (bio)chemical processes, but its adoption is still limited by the effort required to build maintainable first-principles models and to implement efficient dynamic optimization-based controllers. This paper presents GlycoPy, an open-source, equation-oriented and object-oriented Python framework that supports hierarchical model construction and integrated workflows for simulation, parameter estimation, dynamic optimization, and NMPC. The case study of the monoclonal antibody glycosylation process based on a multiscale model demonstrates the capability of GlycoPy.

Power-to-ammonia (P2A) provides a carbon-free alternative to conventional ammonia production by replacing fossil-based feedstocks with electrolytic hydrogen and nitrogen from air separation. For decentralized P2A systems, pressure swing adsorption (PSA) offers a flexible alternative to cryogenic air separation. However, its industrial implementations are largely proprietary, and open, first-principles models capable of simulating its cyclic, nonlinear transport are scarce in literature. This work presents a first-principles, dynamic, one-dimensional model of a PSA superstructure for nitrogen generation, formulated with thermodynamically consistent equations of state, coupling multicomponent mass, energy, and momentum balances with kinetically limited adsorption on carbon molecular sieves. The resulting system of partial differential-algebraic equations is semi-discretized using the finite volume method, integrated using diagonally implicit Runge-Kutta methods, and cyclic steady states... [more]

The climate crisis and dependence on fossil fuels make the transition to renewable energy sources imperative, with biomass standing out for promoting decarbonization and circular economy. In this context, fluidized bed gasification emerges as an efficient route for converting waste into syngas, applicable to power and hydrogen generation. Given the variability of real operating conditions, dynamic models are essential to represent coupled fluid dynamic, thermal, and kinetic phenomena over time. In this work, a dynamic phenomenological model was developed using a lumped 0D approach, in which the reactor is divided into two interacting zones represented as continuous stirred-tank reactors (CSTRs): a dense bed, where drying, devolatilization, and heterogeneous reactions occur, and a freeboard, dominated by homogeneous gas-phase reactions. The model was validated against experimental data from a bubbling fluidized bed gasifier, showing good agreement for major syngas species (CO and H2, me... [more]

Biological systems are governed by coupled interactions between intracellular metabolism and bioreactor operation that span multiple time scales. Constraint-based metabolic models are widely used to describe intracellular metabolism, but repeatedly solving the optimization problem at each time step in dynamic models introduces numerical challenges related to infeasibility and computational efficiency. This work presents a multi-scale modeling framework that integrates genome-scale, constraint-based metabolic models with dynamic bioreactor simulations. Intracellular metabolism is described using positive flux variables in a parsimonious flux balance analysis, and the resulting embedded optimization problem is replaced by a neural network surrogate. The surrogate provides a smooth approximation of the embedded optimization mapping and eliminates repeated linear program solves during simulation. The approach is demonstrated for fed-batch fermentation of Escherichia coli, in which the surr... [more]

Fouling is a persistent issue in industrial heat-transfer equipment, increasing energy demand and reducing efficiency. This is also true in the dairy industry where falling-film evaporators are central to powder production. Most dynamic models, however, neglect gradual fouling, limiting predictive accuracy during extended operation. As a result, model-based control can become unreliable when fouling becomes significant. The dynamic models by Bojnourd et al. [1] are widely used but assume clean-surface operation. While this captures short-term thermal behavior, it cannot represent the progressive decline in heat-transfer performance caused by fouling. Díaz-Ovalle et al. [2] introduced a fouling-layer model that explicitly describes the growth of a fouling deposit over time. Building on this concept, the present work incorporates a simple dynamic fouling model for falling-film evaporators and validates it using industrial data from a four-effect evaporator using thermal vapor recompressi... [more]

The growing integration of renewable energy sources such as solar and wind power has intensified the demand for advanced energy storage technologies. Lithium-air (Li-O2) batteries are particularly attractive due to their exceptionally high theoretical specific energy, which surpasses that of the conventional lithium-ion system. However, their practical application is hindered by poor reversibility during discharge, primarily due to the formation and decomposition of lithium peroxide (Li2O2), which causes cathode passivation and capacity fading. Since the electrochemical performance of Li-O2 batteries is strongly influenced by the morphology, size, and spatial distribution of Li2O2 crystals, understanding the mechanisms governing their nucleation and growth is critical. To address this challenge, this work proposes a computational framework based on population balance modeling (PBM) to describe Li2O2 crystallization dynamics during battery discharge. The framework integrates population,... [more]

The zero-pollution ambition of the European Union requires improvements in wastewater treatment to meet increasingly stringent regulations at achievable cost. One promising approach consists in model-based optimal control. However, wastewater treatment plants involve highly nonlinear and time-varying processes, making existing mechanistic models such as the Benchmark Simulation Model no.1 (BSM1) challenging for direct use in online control. Therefore, this study explores a hybrid modeling approach using the Hammerstein-Wiener (HW) structure. The proposed model combines a mechanistic steady-state model, derived from BSM1, with a data-driven approximation of the system dynamics, incorporating low-order linear dynamic models. In this work, the HW model was used as a surrogate for BSM1. The HW surrogate model attained coefficients of determination (R2) often exceeding 0.95 across key water quality indicators, such as total nitrogen and ammonium concentration. This accuracy was found to be... [more]

This work presents a preliminary application of a developing methodology for assessing the operational flexibility of ammonia synthesis loops. Part of this methodology involves systematic dynamic testing in the synthesis loop. In the present case, a synthesis loop equipped with a two-bed, quench-cooled converter operating under variable feed conditions was considered. A high-fidelity model of the converter was developed in gPROMS Process using two-dimensional reactor models from its fixed-bed catalytic reactor library, and a synthesis loop configuration was modeled and designed in the same environment. A series of dynamic tests varied the make-up flow rate across four disturbance amplitudes (±25% and ±50%) and four disturbance durations (10 s, 600 s, 1800 s, 3600 s). The results showed that disturbances of ±50% magnitude led to the violation of one process operational constraint. These findings enable the construction of a preliminary operational map of this system, providing an initia... [more]

Catalyst deactivation significantly affects reactor performance, process efficiency, and economic viability in chemical processes. The precise estimation of kinetic and deactivation parameters in transient tubular reactors is essential but remains challenging due to strong parameter correlations, nonlinear dynamics, and limited prior knowledge of parameter values. Model-based Design of Experiments techniques for improving parameter precision (MBDoE-PP) has been shown to enhance parameter identifiability by optimally designing informative transient experiments even when catalyst deactivation occurs. However, MBDoE-PP is highly sensitive to parameter misspecification and can lead to suboptimal or infeasible solutions under model uncertainty, in which is the typical case in reaction systems exhibiting catalyst deactivation. In this work, a robust MBDoE framework for parameter precision (RMBDoE-PP) is proposed to explicitly account for processmodel parameter mismatch (PMPM) during the expe... [more]

This work presents the dynamic optimization of glucose feed in cell cultivation considering the balance between productivity and impurity generation. We first developed a mechanistic model considering cell growth promotion by glucose and cell growth inhibition by osmolarity for a newly developed, high-productivity CHO-MK cell line. For model development, fed-batch cultivation experiments were conducted at a 250 mL scale under three different glucose feeding profiles. Results from a single-objective dynamic optimization, using the glucose feed profile as a design variable, were compared to those from multi-objective problem settings with varying weights assigned to productivity and final impurity concentrations. Simulation results suggested different glucose feed profiles depending on the priority given to the mAb and impurities, where the main difference was in the generated viable cell density profiles. Productivity-focused profiles employed a low-high-intermediate feeding strategy, i... [more]

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]

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]

Direct Air Capture (DAC) is a negative emissions technology whose performance is inherently linked to ambient conditions, which directly affect its primary feed stream (air). A common simplification in DAC model simulations is the use of fixed weather conditions, which can bias the predicted performance under weather variability. In response, this study quantifies the impact of local meteorological variability and temporal weather aggregation on the performance of DAC units. Building on a previously developed and validated 1D mechanistic model of a fixed-bed Steam-assisted Temperature Vacuum Swing Adsorption (S-TVSA) DAC process, we simulate its operation using weather data from the Met Office station at Buchan (UK), near the Saint Fergus terminal - a strategic hub for Carbon Capture and Storage (CCS) activities in Scotland. A two-branch methodological framework is developed combining optimization and forward simulations. Operating conditions are optimized using a multi-objective genet... [more]

The attached table shows the raw experimental data used for Figure 2 in the conference paper.

The climate crisis and dependence on fossil fuels make the transition to renewable energy sources imperative, with biomass standing out for promoting decarbonization and circular economy. In this context, fluidized bed gasification emerges as an efficient route for converting waste into syngas, applicable to power and hydrogen generation. Given the variability of real operating conditions, dynamic models are essential to represent coupled fluid dynamic, thermal, and kinetic phenomena over time. In this work, a dynamic phenomenological model was developed using a lumped 0D ap-proach, in which the reactor is divided into two interacting zones represented as continuous stirred-tank reactors (CSTRs): a dense bed, where drying, devolatilization, and heterogeneous re-actions occur, and a freeboard, dominated by homogeneous gas-phase reactions. The model was validated against experimental data from a bubbling fluidized bed gasifier, showing good agree-ment for major syngas species (CO and H₂,... [more]

Digital supplementary material for the ESCAPE 36 submission "Modeling and Simulation of Nitrogen Generation by Pressure Swing Adsorption for Power-to-Ammonia". Contains relevant model parameters from the literature.

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.

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]

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.

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]

Hybrid modelling utilizes advantageous aspects of both mechanistic (white box) and data-driven (black box) modelling. Combining the physical interpretability of kinetic modelling with the power of a data-driven Artificial Neural Network (ANN) yields a hybrid (grey box) model with superior accuracy when compared to a traditional mechanistic model, while requiring less data than a purely data-driven model. This study demonstrates the construction a hybrid model with transfer learning for the predictive modelling and optimization of a high-cell-density microalgal fermentation process for lutein production. Dynamic optimization was conducted to identify a feeding strategy that maximized final lutein production. The results were then experimentally validated. Overall, this work presents a novel digital twin application that can be easily adapted to general bioprocesses for model predictive control and process optimization.