The increasing complexity of industrial biotechnology demands advanced modeling techniques capable of capturing the intricate dynamics of bioreactors. Traditional regression-based and empirical methods often fall short when confronted with the highly nonlinear behavior and limited experimental data characteristic of bioprocesses. Addressing these challenges requires a more intelligent approach—one that leverages domain knowledge to model complex bioprocess dynamics effectively, even with sparse data, while maintaining interpretability and robustness. In this study, we introduce a process-informed, data-driven methodology for modeling the dynamics of industrial bioreactors, leveraging the capabilities of the rising field of Scientific Machine Learning (SciML). Our approach leverages Physics-Informed Neural Networks (PINNs) to seamlessly integrate domain knowledge encoded in physical laws with sparse experimental data and deep learning techniques, enabling precise simulation and modeling... [more]

Accurate mechanistic models provide valuable physical insight and are crucial for efficient process scale-up and optimisation, but their identification requires lengthy experimental data collection, model construction, validation and discrimination. Traditional black-box machine learning transfer methods leverage prior knowledge but lack interpretability and physical insights. To address this, we propose a novel approach using artificial neural network feature attribution to automatically locate corrections and symbolic regression to make structural modifications to an inaccurate or low-fidelity mechanistic model. In a comprehensive in-silico case study, the framework adapted a kinetic model from one biochemical system to a different but related one, enhancing predictive accuracy. Integrated within an iterative model-based design of experiments routine, it minimised the number of new experiments required. The study also discusses the impact of the inductive bias trade-off and alternati... [more]

The optimization of the power conversion system, responsible for thermal-to-electrical energy conversion, for a pulsed fusion power plant is presented. A spherical tokamak is modelled as three heat sources, all pulsed, with different stream temperatures and available amounts of heat. A thermal energy storage system is considered in the design to compensate for the lack of thermal power during a dwell. Thermal storage enables continued power generation during a dwell and can avoid thermal transients in sensitive components like turbomachines. Multiple lower grade heat sources are integrated into the process through parallel preheating trains. The evaluation of a dynamic model of the power conversion system is used to define an objective function with multiple criteria. A bi-objective optimization problem is defined to investigate the trade-off between the size of the thermal energy storage system and the variability in turbine power output during a dwell. The set of non-dominated design... [more]

Solar Thermal Power Plants (STPPs) use solar energy for large-scale electricity production but face significant operational challenges. These include variations in solar radiation, cloud cover, electricity demand fluctuations, and the need for frequent shutdowns if energy storage is inadequate. Deciding an optimal STPP operating conditions is challenging due to these factors. While revenue maximization has been used as an objective in existing literature, current models are often static and fail to capture the dynamic nature of STPPs. In contrast, this work proposes a dynamic model-based revenue optimization approach that accounts for plant dynamics and operational constraints, such as solar radiation variability and changing electricity demand. The objective function is designed to maximize revenue while considering power generation and fluctuating electricity prices. A simulation model of 1 MWe hybrid solar thermal power plant in Gurgaon, India, featuring two solar fields—Parabolic T... [more]

The transition to a circular economy (CE) requires agents in circular supply chain (SC) networks to take a variety of different initiatives, many of which are dynamic in nature. We use a system dynamics (SD)-based approach to develop a generic framework for dynamic modeling of CE networks and propose a prototypical circular SC network by combining dynamic models for five actors: a manufacturer, consumer, material recovery facility (MRF), recycling facility, and the Earth. We apply this framework to the supply chain for Polyethylene Terephthalate (PET) plastic packaging by considering different scenarios over a 65-year time horizon in the US. We include both "slow-down-the-loop" initiatives (i.e., those that extend product use time through demand reduction or reuse) and "close-the-loop" initiatives (i.e., those that reintroduce product to the supply chain through recycling) by the consumer, as well as sorting and recycling capacity expansion. We find that, given the current recycling in... [more]

Demographic shifts and increased automation in chemical plants are reducing the experience and skill levels of plant operators. Therefore, BASF has implemented Operator Training Simulators (OTS) to allow operators to practice and improve their skills in this safe and controlled environment. The OTS consists of a dynamic model of the process, a control system and safety logics. This paper describes the learnings from using OTS at BASF, where they are used to train operators in process understanding, optimization, procedural training, and disturbance handling. Benefits include reduced training costs, minimized risks and improved efficiency. Also organizational guidelines are provided to ensure that the mentioned benefits are realized in industrial practice. Additionally, high-accuracy OTS models support HAZOP, debottlenecking, and optimization studies.

Dynamic or Programmable Catalysis is an innovative strategy to improve heterogeneous catalysis processes by modulating the binding energies (BE) of adsorbates on a catalytic surface. The technique enables the periodic favoring of different reaction steps, overcoming limitations imposed by the Sabatier Principle and allowing for higher overall reaction rates, otherwise unattainable. Previously, we implemented a simultaneous simulation approach using the algebraic modeling language Pyomo and the solver IPOPT to obtain cyclic steady state results for a unimolecular reactive system with up to four-order of magnitude increases in computational performance compared to the previously reported sequential approach. The flexibility of the method allowed for the investigation of the influence of forcing signal parameters on system behavior and provided a framework for waveform design. In this study, we use a hybrid framework that combines the sequential and the simultaneous simulation approaches... [more]

This study presents an economic analysis of a blue H2-N2 production system, using a novel intensified reformer system with a hydrogen production efficiency of 80%. The system’s ability to produce both high-purity H2 and N2 creates opportunities for small-scale blue H2 and distributed ammonia production. The system consists of three identical, optimized fixed-bed reforming reactors, a heat recovery system, and shift reactors. A dynamic model was developed to simulate three small-scale H2 production systems: 2.8 tpd, 7.1 tpd, and 17.1 tpd, enabling an evaluation of their economic viability. The results indicate that the cost of H2 production ranges from 2.7 to 3.1 USD/kgH2. Sensitivity analysis reveals that natural gas and CO2 transportation costs have a significant impact on the variability of H2 price. This research provides valuable insights into the economic feasibility of small-scale blue hydrogen production, offering a pathway to support the broader adoption of hydrogen technologie... [more]

Hydrogen-powered heavy-duty trucks have a high potential to significantly reduce CO2 emissions in the transportation sector. Therefore, efficient hydrogen storage onboard vehicles is a key enabler for sustainable transportation, as achieving high storage densities and extended driving ranges is essential for the competitiveness of hydrogen-powered trucks. Cryo-compressed hydrogen (CcH2), stored at cryogenic temperatures and high pressures, emerges as a promising solution. This study presents a comprehensive dynamic thermodynamic model that is capable of simulating the tank system across all operating conditions and, therefore, enables thermodynamic analysis of drive cycles. The core of the model is a differential-algebraic equation system that describes the thermodynamic state of the hydrogen in the tank. Additionally, surrogate models based on artificial neural networks are applied to efficiently describe quasi-steady-state heat exchangers integrated into the tank system. Several use... [more]

Power-to-Ammonia technology offers sustainable pathways for energy storage and chemical production, with fixed-bed reactors being critical components for efficient synthesis. Understanding reactor dynamics under varying conditions is essential for optimizing these systems, particularly when integrated with intermittent renewable energy sources. This study aims to develop and validate a 2D axisymmetric CFD model for analysing the dynamic response of a ruthenium-catalysed ammonia synthesis reactor to thermal perturbations. The model incorporates detailed reaction kinetics, multicomponent mass transport, and heat transfer mechanisms to predict system behaviour under transient conditions. Results reveal that a step increase in wall temperature from 400°C to 430°C enhances NH3 concentration by 136% (from 2.2 to 5.1 vol.%), with rapid system stabilization achieved within 0.5 seconds. The thermals response maintains consistent heat transfer patterns, exhibiting ~400K differentials between inl... [more]

This study proposes a model-based approach utilizing a hybrid population balance model (PBM) to optimize temperature profiles for minimizing agglomeration and enhancing crystal growth. The PBM incorporates key mechanisms—nucleation, growth, dissolution, agglomeration, and deagglomeration—and is ap-plied to the crystallization of an industrial active pharmaceutical ingredient (API), Compound K. Parameters were estimated through prior design of experiments (DoE) and refined via additional thermocycle experiments. In-silico DoE simulations demonstrate that the hybrid PBM outperforms traditional methods in assessing process performance under agglomeration-prone conditions. Results confirm that thermocycles effectively reduce agglomeration and promote bulk crystal formation, though their efficiency plateaus be-yond a certain cycle number. This model-based approach provides a more robust strategy for agglomeration control compared to conventional methods, offering valuable insights for indus... [more]

This model uses the CHEMCAD unit operation Batch Column together with tools for data reconciliation and optimization. Some experimental data is included.
It is used as a template in the course Green Processes at Berlin University of Applied Science (BHT), where students use it to regress measured data from lab experiments and to design an optimal process.

This model uses standard CHEMCAD unit operations and thermodynamic models to simulate the separation of ethanol and water from a fermenter broth.
It is used as a template in the course Green Processes at Berlin University of Applied Science (BHT), where students use it to regress measured data from lab experiments and to design an optimal process.

To match the growing demand for biomethane production, anaerobic digestors need an optimal and time-varying adaptation of the input diet. Dynamic co-digestion constitutes a hard challenge for the limited instrumentation and control equipment typically installed aboard full-scale plants. The development of prediction models is foreseen to support process (optimal) design and control. In this work, a rigorous framework is applied to take full-scale applicability into account while dealing with the design and training of both high-fidelity and control-oriented first-principle/grey-box models, intended to be exploited for real-time optimization and actual process control respectively.

Designing a digital twin will be crucial in developing automation-based future manufacturing systems. The design of digital twins involves data-driven modelling of individual manufacturing units and interactions between the various entities. The goals of future manufacturing units such as zero waste at the plant scale can be formulated as a model-based optimal control problem by identifying the necessary state, control inputs, and manipulated variables. The fundamental assumption of any model-based control scheme is the availability of a “reasonable model”, and hence, assessing the goodness of the model in terms of stability and sensitivity around the optimal parameter value becomes imperative. This work analyses the data-driven model of an acetaminophen production plant obtained from SINDy, a nonlinear system identification algorithm using sparse identification techniques. Initially, we linearize the system around optimal parameter values and use local stability analysis to assess the... [more]

Antibody therapies are important in treating life-threatening ailments such as cancer and autoimmune diseases. Purity of the antibody is essential for successful applications and Protein A selective resin extraction is the standard step for antibody recovery. Unfortunately, such resins can cost up to 30% of the total cost of antibody production. Hence, the optimal design of this purification step becomes a critical factor in downstream processing to minimize the size of the column needed. An accurate predictive model, as a digital twin representing the purification process, is necessary where changes in the flow rates and the inlet concentrations are modeled via the Method of Moments. The system uncertainties are captured by including the stochastic Ito process model of Brownian motion with drift. Pontryagin’s Maximum Principle under uncertainty is then applied to predict the flowrate control strategy for optimized resin use, column design, and efficient capturing of the antibodies. In... [more]

Machine learning presents opportunities to improve the scale-specific accuracy of mechanistic models in a data-driven manner. Here we demonstrate the use of a machine learning technique called Sparse Identification of Nonlinear Dynamics (SINDy) to improve a simple mechanistic model of algal growth. Time-series measurements of the microalga Chlorella Vulgaris were generated under controlled photobioreactor conditions at the University of Technology Sydney. A simple mechanistic growth model based on intensity of light and temperature was integrated over time and compared to the time-series data. While the mechanistic model broadly captured the overall growth trend, discrepancies remained between the model and data due to the model's simplicity and non-ideal behavior of real-world measurement. SINDy was applied to model the residual error by identifying an error derivative correction term. Addition of this SINDy-informed error dynamics term shows improvement to model accuracy while maint... [more]

Within an Industry 4.0 framework, a variety of new considerations are of increasing importance, such as securing processes against cyberattacks on the control systems or utilizing advances in image processing for image-based control. These new technologies impact relationships between process design and control. In this work, we discuss some of these potential relationships, beginning with a discussion of side channel attacks and what they suggest about ways of evaluating plant design and instrumentation selection, along with controller and security schemes, particularly as more data is collected and there is a move toward an industrial Internet of Things. Next, we highlight how the 3D computer graphics software tool set Blender can be utilized to analyze a variety of considerations related to ensuring safety of plant operation and facilitating the design of assemblies with image-based sensing.

The (bio-)pharmaceutical industry is facing crossroads in an effort to ramp up its global capacity, while working to meet net-zero targets and to ensure continuous drug supply. Beyond geopolitical challenges faced worldwide, (bio-)pharmaceutical processes have been historically very complex to design, optimise and integrate in a global distribution network that is resilient and adaptable to changes. In this paper we offer a perspective of how Process Systems Engineering (PSE) tools can support and advance (bio-)pharma practices with an outlook towards 3-fold sustainability. The latter is considering three main pillars, namely social (drug supply), economical and environmental sustainability. We discuss PSE contributions that have revolutionised process design in this space, as well as the optimisation of distributions networks in pharmaceuticals. We do this by means of example cases: one on model-based unit operation design and a second one on sustainable supply chain networks in the... [more]

This study mainly investigated the nonlinear vibration performance of a rotor-casing coupling system containing a bolted flange connection. The dynamic equations of the coupling system were developed while considering the radial stiffness of the bolted flange structure, which contained a spigot, squirrel cage with ball bearing, and rotor-casing coupling vibration. To study the influence of the disk casing fixed-point rubbing fault on the coupling system’s nonlinear dynamic performance, an analytical model of the nonlinear impact forces was established, which considered the contact and vibration responses of the rotor and casing. The frictional force was obtained based on the Coulomb friction law. The iterative analysis of motion equations was performed utilizing the Newmark method. Then, the nonlinear dynamic behaviors of the coupled systems were examined using data, including a bifurcation diagram, spectrum plot, greatest Lyapunov exponents, etc. The effects of rubbing fault on the dy... [more]

This study addresses the use of former gas storage facilities as short-term storage for renewable energy through power-to-gas (PtG) technology in Germany. Three test cases with coupled thermal-hydromechanical (THM) modelling were conducted to evaluate short-term injection and production schedules. The operating rates were controlled by the upper and lower limits of the wellbore pressure. The maximum difference in pore pressure and effective stress was 0.6 MPa in all cases. Fault reactivation analysis was performed on the THM models to estimate fault stability. The critical pore pressure for safe reservoir operation was determined to be 1.25 times the original pore pressure, corresponding to a WBHP value of 20.25 MPa. The upper limit of the gas injection rate for safe storage operation was estimated to be between 100,000 and 150,000 m3/day. The thermal stresses were found to be negligible for short-term cases. The storage capacity of PtG technology was reported to be up to 1,322,400 kWh... [more]

Solar thermal plants are often considered as a convenient and environmentally friendly way to supply heat to buildings or low temperature industrial processes. Some modelling techniques are required to assess the dynamic behaviour of solar thermal plants in order to control them correctly. This aspect is reinforced while large plants are considered. Indeed, some atmospheric conditions, such as local clouds, could have significant influence on the outlet temperature of the solar field. A common modelling approach to assess the heat transport in pipes is the one-dimensional finite volume method. However, previous work shows limitations in the assessment of the temperatures and in the computational time required for simulating large pipe networks. In this contribution, a previous alternative method developed and validated in a district heating network is used and extended to a solar thermal plant considering the thermal solar gain and the inertia of the pipes. The present contribution int... [more]

Liquefied Natural Gas (LNG) is a crucial resource to reduce the environmental impact of fossil-fueled vehicles, especially with regards to maritime transport, where LNG is increasingly used for ship bunkering. The present paper gives insights on how the installation of LNG tanks inside harbors can be capitalized to increase the energy efficiency of port cities and reduce GHG emissions. To this purpose, a novel integrated energy system is introduced. The Boil Off Gas (BOG) from LNG tanks is exploited in a combined plant, where heat and power are produced by a regenerated gas turbine cycle; at the same time, cold exergy from LNG regasification contributes to an increase in the efficiency of a vapor compression refrigeration cycle. In the paper, the integrated energy system is simulated by means of dynamic modeling under daily variable working conditions. Results confirm that the model is stable and able to determine the time behavior of the integrated plant. Energy saving is evaluated, a... [more]

When performing AC/DC-DC/AC power conversions, multilevel converters provide several advantages as compared to classical two-level converters. This paper deals with the dynamic modeling, control, and robustness assessment of multilevel flying-capacitor converters. The dynamic model is derived using the Power-Oriented Graphs modeling technique, which provides the user with block schemes that are directly implementable in the Matlab/Simulink environment by employing standard Simulink libraries. The performed robustness assessment has led to the proposal of a divergence index, which allows for evaluating the voltage balancing capability of the converter using different voltage vector configurations for the extended operation of the converter, namely when the number of output voltage levels is increased for a given number of capacitors. A new variable-step control algorithm is then proposed. The variable-step control algorithm safely enables the converter extended operation, which prevents... [more]

This paper presents an experimentally validated, dynamic model of a hybrid liquid desiccant system. For this purpose, we developed new components for the air-solution contactors, which are of the non-adiabatic falling-film type with horizontal tubes (made of improved polypropylene) and the solution tanks. We also provide new experimental correlations for both the tube-solution heat transfer coefficient and the mass transfer coefficient on the airside as a function of the air velocity. To validate the model, the results obtained from the dynamic simulations were compared with those obtained by monitoring a demonstration unit installed in a sports center in Taipei (Taiwan). Once validated, the model was used to perform a sensitivity analysis at different operational conditions, such as the inlet water temperatures in the air-solution contactors and the LiCl mass fraction at which the system operates. The results of the sensitivity analysis were used to optimize the seasonal performance i... [more]