A new method for efficient process development is the direct scale-up from laboratory scale to production scale using mechanistic models [1]. The integration of the Quality by Design approach into this scale-up concept may prove beneficial for a variety of product- and process-related aspects. This paper presents a workflow for the digital twin-supported direct scale-up of processes and process plants, which integrates elements of the Quality by Design methodology. To illustrate the concept, the workflow is implemented for the example of an esterification reaction in a stirred tank reactor. Finally, benefits of the implementation of Quality by Design in the direct scale-up using digital twins regarding the product quality and the process development are discussed as well as its limitations.

In applications involving fine powders and small particles, the accuracy of numerical simulations, particularly those employing the Discrete Element Method (DEM) to predict granular material behavior, can be significantly affected by uncertainties in critical parameters. These uncertainties include the coefficients of restitution for particle-particle and particle-wall collisions, viscous damping coefficients, and other related factors. In this study, we use stochastic expansions based on point-collocation non-intrusive polynomial chaos to perform a sensitivity analysis of a fluidized bed system. We treat four key parameters as random variables; each assigned a specific probability distribution over a designated range. This uncertainty is propagated through high-fidelity Large Eddy Simulation (LES)-DEM simulations to statistically quantify its impact on the results. To effectively explore the four-dimensional parameter space, we analyze a comprehensive database comprising over 1,200 si... [more]

Random signals are crucial in chemical and process engineering, where industrial plants generate big data that can be used for process understanding and decision-making. This makes it necessary to unveil the underlying probability histograms from these signals with a finite number of bins. However, the search for the optimal number of bins is still based on empirical optimisation and general rules of thumb. In this work, we introduce an alternative and general method to unveil probability histograms. Our method employs a novel variable-order QMOM, which adapts automatically based on the relevance of the information contained in the random data. The number of bins used to recover the underlying histogram is found to be proportional to the information entropy, where a search algorithm is developed that generates bins and assigns probabilities to them. The algorithm terminates when no more significant information is available for assignment to the newly created nodes, up to a user-defined... [more]

Liquid-liquid extraction stages often deviate from equilibrium due to factors like insufficient mixing, making accurate efficiency modeling essential for process simulation. This study addresses the limitations of Aspen Plus (AP), which distorts equilibrium calculations by directly multiplying efficiency with the distribution coefficient. A modified Murphree efficiency definition, more suitable for liquid-liquid systems but absent in AP's Extraction Column module, was implemented using Aspen Custom Modeler (ACM). The custom multi-stage extraction column model replaces mole fractions with mole flows to better represent mass transfer and phase interactions, enhancing simulation accuracy when imported into AP. Two test cases validated the custom model's effectiveness. Test Case I, utilizing the UNIQ-RK thermodynamic model, compared the ACM model to AP's built-in module, revealing that the ACM model provides a more realistic representation of extraction processes under varying stage effici... [more]

Rapid and robust simulation of chemical production processes is critical to address core scientific questions related to process design, optimization, and sustainability. Efficiently solving a chemical process, however, remains a challenge due to their highly coupled and nonlinear nature. Graph abstractions of the underlying physical phenomena within unit operations may help identify potential avenues to systematically reformulate the network of equations and enable more robust convergence of flowsheets. To this end, we further refined a flowsheet graph-theoretic abstraction that consists of a mesh of interconnected variable nodes and equation nodes. The new network of equations is formulated at the phenomenological level agnostic to the thermodynamic property package by extending equation formulations widely used to solve multistage equilibrium columns. Decomposition of the graph by phenomena linearizes material and energy balances across the flowsheet by decoupling phenomenological n... [more]

The definition of strategies for operation of process networks is a key research focus in process systems engineering. This challenge is commonly formulated as a numerical constraint satisfaction problem, where most practical algorithms are limited to identifying inner approximations to the feasible operational envelope. Sampling-based approaches so far have only been developed for formulations that required coordinated operation of the units within the network. We propose a decomposition approach that enables decentralized operation for acyclic muti-unit processes by sampling. Our methodology leverages problem structure to decompose unit-wise and deploys surrogate models to couple the resultant subproblems. We demonstrate it on a serial, batch chemical reactor network. In future research, we will extend this framework to consider the presence of uncertain unit parameters robustly.

Vacuum (pressure) swing adsorption (V(P)SA) has received considerable attention in the past decades. Existing studies typically estimate vacuum pump energy consumption using an approximate constant energy efficiency or an empirical energy efficiency correlation, leading to inaccurate representation of realistic vacuum pump performance. In this paper an enhanced computational approach is proposed for simulation and optimisation of V(P)SA through simultaneous integration of realistic vacuum pump data and adsorption bed fluidisation limits. The computational results show that the developed prediction models accurately represent the actual performance curves of the vacuum pump. Incorporation of the vacuum pump prediction models and fluidisation constraints in V(P)SA optimisation leads to significantly different optimal solutions compared to when these factors are not considered.

By capturing carbon dioxide from biomass flue gases, energy processes with negative carbon footprint are achieved. Among carbon capture methods, the fluidized temperature swing adsorption (TSA) column is a promising low-pressure alternative, but it has been developed on small scales. This work aims to model, simulate and optimize a fluidized TSA multi-stage equilibrium system to obtain a cost estimate and a conceptual design for future process scale up. A mathematical model described adsorption in multiple stages, each with a heat exchanger, coupled to the desorption operation. The model was based on elementary macroscopic molar and energy balances, coupled to pressure drops in a fluidized bed designed to operate close to the minimum fluidization velocity, and coupled to thermodynamics of adsorption equilibrium of a mixture of carbon dioxide and nitrogen in solid sorbents (the Toth equilibrium isotherm was used). The complete fluidized TSA process has been optimized to minimize costs,... [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]

Distillation is not only a widely-used but also an energy-intensive separation process, in which internals such as structured packings play an important role. Increasing mass transfer efficiency by designing improved structured packings in order to provide a large interfacial area while enabling low pressure drop is one promising approach to quickly reduce the energy requirements of vacuum distillation where low pressure drop is important for separation efficiency and thermal stability of the processed media. The current work presents an innovative method to optimize structured packings by means of constrained shape optimization on the basis of computational fluid dynamics simulations to minimize the pressure drop while maintaining a constant specific surface area. To solve the fluid dynamic optimization problem, a gradient-based local optimization algorithm in a continuous adjoint formulation is utilized. The shape optimization is applied for a commonly used Rombobak packing, and test... [more]

Seawater desalination is considered as the only available solution that can cope with the increasing demand for freshwater around the world. Improving the desalination techniques may help to cut off the cost and increase sustainability. In this paper, a mathematical model describing the MED process is developed within gPROMs software. The model includes all the necessary mass and energy balance equations together with thermodynamic and physical properties equations. The model predictions are validated against the actual plant data before using the model for optimizing the process to achieve minimum heat transfer area. For two different operating conditions (summer and winter) and a fixed production demand, the heat transfer area is minimised while optimising different parameters of the MED process. The results showed that a 10.4% reduction in the heat transfer area can be achieved under summer operating conditions and around 26% decrease in the heat transfer area can be met under winte... [more]

The Reverse Osmosis (RO) system has the potential as a vibrant technology to generate high-quality water from brackish water sources. Nevertheless, the progressive growth in water and electricity demands necessitates the development of a sustainable desalination technology. This can be achieved by reducing the specific energy consumption of the process, which would also reduce the environmental footprint. This study proposes the concept of reducing the overall energy consumption of a multistage multi-pass RO system of Arab Potash Company (APC) in Jordan via heating the feed brackish water. The utilisation of waste heat generated from different units of production plant of APC such as steam condensate supplied to a heat exchanger is a feasible technique to heat brackish water entering the RO system. To systematically assess the contribution of water temperature on the performance metrics including specific energy use, a generic model of RO system is developed. Model based simulation is... [more]

Microfluidic mixing has gained popularity in the Pharmaceutical Industry due to its application in the field of Nano-based Drug Delivery Systems (DDS). The flow conditions in Microfluidic mixers enable very efficient mixing conditions, which are crucial for the production of Nanoparticles by Flash Nanoprecipitation (FNP), as it enables reproducible production of particles with low-size variability. Mixer geometry is one of the most determinant factors, as it largely determines the flow patterns and the degree of contact between the two mixing streams. In this paper, a shape optimization methodology using Computational Fluid Dynamics (CFD) and Bayesian optimization is applied to the toroidal micromixer design, considering three different operating conditions. It consists of first defining a geometry solution space and then using Multi-Objective Bayesian optimization to explore the different designs. Mixer performance is evaluated with CFD simulations and two objective functions are cons... [more]

Process systems are negatively impacted by manufacturing uncertainties, and increasingly by unknown-unknown disruptive events. To this effect, systems need to be designed with the inherent flexibility and resilience to overcome the impacts of uncertainties and disruptions respectively as it is more challenging to retrofit existing systems with such capabilities. To this end, we propose a methodology based on flexibility analysis to systematically explore the feasibility of design alternatives under parameter uncertainty and discrete disruption scenarios simultaneously. Multi-parametric programming is utilized to generate explicit relationships between design decisions and the resulting system’s ability to maintain feasible operations under uncertainty and disruptive events. We capture this ability by introducing the Combined Flexibility-Resilience Index (CFRI), which describes the likelihood that the system is feasible under the relevant uncertainty and disruption sets. With explicit f... [more]

By 2050 around 12% of cumulative emissions reductions will come from Carbon Capture, Utilisation and Storage (CCUS) making it an essential component in the path towards net zero [1]. Focus will initially be on the retrofitting of fossil fuel power plants, which will shift to hard-to-decarbonise industries such as iron, steel, and concrete [1]. Such industries are often grouped together in industrial clusters. Comprising both large and small point sources concentrated over a defined geographical area, industrial clusters offer an opportunity to maximise the impact of CCUS whilst also improving economic feasibility [2]. The North Sea Port (NSP) cluster an example of this. Within the NSP cluster an initial set of five emitters are to join a capture, conditioning, and transport network by 2030. From there other emitters within the area will be able to join incrementally to 2050 [3]. However, the emitters who join and the timing of their connection will have a significant effect on the evo... [more]

Deriving accurate cost projections associated with producing hydrogen within the context of an energy-export paradigm is a challenging feat due to non-deterministic nature of weather systems. Many research efforts employ deterministic models to estimate costs, which could be biased by the innate ability of these models to ‘see the future’. To this end we present the findings of a multistage stochastic model of hydrogen production for energy export (using liquid hydrogen or ammonia as energy vectors), the findings of which are compared to that of a deterministic programme. Our modelling found that the deterministic model consistently underestimated the price relative to the non-deterministic approach by $ 0.08 – 0.10 kg-1(H2) (when exposed to the exact same amount of weather data) and saw a standard deviation 40% higher when modelling the same time horizon. In addition to comparing modelling paradigms, different grid-operating strategies were explored in their ability to mitigate three... [more]

In this work, a Claus reaction furnace was analyzed in a sulfur recovery unit (SRU) of the Abadan Oil Refinery where the combustion operating temperature is important since it ensures optimal performance in the reactor, this study focused on temperature of control of 1400, 1500 and 1600 K and excess air of 10, 20 and 30% to improve the reaction yield and H2S conversion. The CFD simulation was carried out in Ansys Fluent in transitory state and in 3 dimensions, considering turbulence model ? -e standard, energy model with transport by convention and mass transport with chemical reaction using the Arrhenius Finite – rate/Eddy dissipation model for a Kinetic model of destruction of acid gases H2S and CO2, obtaining a good approximation with experimental results of industrial process of the Abadan Oil Refinery, Iran. The percentage difference between experimental and simulated results varies between 0.5 to 5 % depending on species. The temperature of 1600 K and with excess air of 30% was t... [more]

Multi-component batch distillation, wherein multi-component mixtures are separated using a single column, is a crucial separation technique in the chemical industry. Traditionally, the components are separated in the descending order of volatility (direct sequence). Similar to continuous distillation, a specific separation sequence can optimize batch distillation. This work aims to generate such optimal sequence for a batch distillation in a computationally efficient manner. Specifically, the proposed approach extends the marginal vapor rate method, which is used for sequencing continuous distillation to multi-cut batch separation. The approach addresses challenges arising due to dynamic nature of batch distillation. The proposed methodology is validated using simulation case studies.

In this contribution, a thermodynamic model-based approach for the optimal design of a solid-state hydrogen storage and release system utilizing the reversible iron oxide/iron thermochemical redox mechanism is presented. Existing storage processes using this mechanism face significant limitations, including low hydrogen conversion, high energy input requirements, limited storage density, and slow charging/discharging kinetics. To address these challenges, a custom thermodynamic model using NIST thermochemistry data is developed, enabling an in-depth analysis of redox reaction equilibria under different conditions. Unlike previous studies, this approach integrates a multi-objective optimization framework that explicitly balances competing objectives: maximizing hydrogen yield while minimizing thermal energy demand. By systematically identifying optimal trade-offs, the study provides new insights into improving process efficiency and reactor design for thermochemical hydrogen storage. Th... [more]

A simple mathematical model of the co-precipitation of Ni-Mn-Co hydroxides is developed and applied to investigate the effect of pH, initial concentration of ammonia in the solution, concentration of the ammonia feed, nucleation rate constant and exponent, growth rate constant and growth exponent over the model output. The model is shown to produce a correct representation of the precipitation variables, and the general trends obtained for different sets of parameters are found in agreement with results presented elsewhere. A sensitivity analysis is carried out and the sensitivity indices are calculated. It is found that pH, initial concentration of ammonia and growth rate constant are the input parameters with the most relevant effect over the model input.

The pharmaceutical and specialty chemical industries are challenged with the requirement of faster time-to-process to meet market demands. Here, Modular Plants made up of predesigned process equipment assemblies (PEAs) are advantageous. Moreover, equipment-based Digital Twins of these modules can further reduce the time-to-process when combined with methods such as Quality by Design (QbD) and model-based design of experiments (MBDoE) to reduce uncertainty. This paper presents a lab scale-based workflow using an equipment-based Digital Twin, which applies QbD and MbDoE methods to identify the Design Space in the lab scale which can be transferred to production scale equipment as part of a Digital Twin based workflow for scale-up in Modular Plants.

Climate and environmental problems caused by increasing CO2 concentration in the atmosphere make the direct air capture (DAC) technology having great prospects for development. As the widely used solvent in carbon capture based on chemical absorption processes, MEA still fails to address the issues of high energy consumption and high costs when used in DAC process. In this study, piperazine (PZ) was used as the new solvent for DAC process. The new configuration was simulated in Aspen Plus® V11 and the model was validated through experimental data and model comparison. It is followed by investigation of the potential for energy efficiency and cost reduction. The standard DAC-PZ configuration could reduce the reboiler duty from 10.7 GJ/tCO2 to 8.9 GJ/tCO2 for DAC-MEA process. Economic analysis will be carried out through Aspen Process Economic Analyzer®. Further analysis (e.g. sensitivity analysis for different parameters and optimisation) will be performed to further reduce the energy c... [more]

Bipolar membrane electrodialysis are receiving the attention of the research community in the last years because they can help the electrification and the spread of direct air capture systems. In this work, a mathematical model of a bipolar membrane electrodialysis cell for carbon dioxide recovery is carried out in order to find the most significant parameters on efficiency through a global sensitivity analysis. The electrochemical cell can be integrated into an absorption column capturing carbon dioxide from the air. Results show that the most important parameter over all investigated figures of merit (specific energy consumption, costs, carbon dioxide desorption efficiency, potassium transport number, removal ratio of potassium cation and carbon) is the potassium cation concentration in the rich solution feeding the cell. A trade-off between energy efficiency, process speed and operational cost is suggested. Future research should be conducted in order to apply the global sensitivity... [more]

This study focuses on enhancing heat and shaft power integration within existing nitric acid production processes to optimize waste heat recovery and identify opportunities to improve process efficiency. A digital twin of the operational plant is utilized, which features a dual-stage pressure nitric acid production process with a capacity of 50 tons/h of HNO3 (100% equivalent). The authors conducted a simultaneous analysis of the thermal energy potential and the expansion capacity of tail gases to effectively fulfil the primary process's heating, cooling, and power requirements while increasing steam generation through waste heat recovery, all without compromising plant throughput. The proposed process modifications lead to a 23.8% reduction in cooling water usage and a 35.6% decrease in CO2 equivalent emissions while achieving a 13.1% increase in steam generation. These utility savings culminate in a 10.2% enhancement in plant throughput.