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.

The accurate prediction of pure component properties has consistently been a critical issue in fields such as chemical engineering, biomedicine, and environmental science. In recent years, end-to-end deep learning methods have shown significant improvement over traditional machine learning approaches. This is due to their ability to automatically learn task-relevant representations from raw molecular data. In addition to accurate property prediction, researchers have increasingly focused on how specific fragment structures influence molecular properties. However, existing fragmentation methods based on predefined rules and group libraries struggle to capture novel molecular structures, which hampers the development of new materials and drugs. To address these challenges, this work proposes a hierarchical molecular fragmentation method. This method can automatically segment molecules into multiple fragments containing key functional groups. Then a three-branch graph attention network wa... [more]

This paper examines the mutually beneficial relationship between biomimetics and modelling and simulation tools, showing how each can enhance the other. Through a literature review and a detailed use case on anaerobic digestion, the study highlights how the complexity, multiscale organisation, and functional richness of biological systems challenge current modelling capabilities. By analysing the contributions of modelling and simulation to product development, such as early performance validation, rapid and lowcost iteration, and multicriteria evaluation, the paper questions whether integrating modelling and simulation tools to biomimetics would bring similar benefits to the design process. Several hypotheses are formulated regarding the potential contributions of modelling and simulation to biomimetics, particularly the improvement of biological system understanding through advanced visualisation and the assessment of functional viability using parametric modelling. Integrating such... [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]

Multiscale models provide a powerful framework to link bioprocess operation conditions with polymer microstructure, yet their predictive capability for polymer attributes such as chain length distributions (CLDs) remains limited. In this work, an advanced multiscale modeling framework for the microbial production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) in Cupriavidus necator is presented, targeting the quantitative prediction of polymer microstructure. The model consistently integrates a structured macroscopic kinetic description of substrate uptake, biomass growth, and copolymer accumulation with an explicitly formulated microscopic polymerization model resolving initiation, propagation, termination, and depolymerization reactions of living and dead chains. A central contribution of this study is the quantitative calibration of the polymerization kinetics based on experimental size-exclusion chromatography (SEC) data. Polymerization rate constants were identified by fit... [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]

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]

Flexible, electrified systems for chemical and energy production are promising alternatives to traditional, hydrocarbon-based processes. Flexible systems have the potential to reduce costs and emissions, but the interconnection between design and operation makes these systems challenging to implement. We use an operation-informed design framework to model a flexible, electrified ammonia synthesis system. We examine the levelized cost and carbon intensity of ammonia in response to different grid emissions (0-420 kg/MWh). We find levelized costs from 700-1200 $/ton-NH3 and observe non-monotonicity in carbon-intensity with respect to grid emissions. We rationalize this trend as a design transition from large, grid-reliant systems to smaller, flexible designs that are grid independent. We then study how synergies in demand and unit-operation flexibility can lower both the price and carbon-intensity of ammonia production. We find that for seasonal, or yearly demand (rather than hourly), a f... [more]

Conventional urea production is a centralized and fossilintensive process associated with significant greenhousegas (GHG) emissions and limited flexibility for deep decarbonization. As an alternative, the Integrated COnversion of NItrate and Carbonate steams (ICONIC) project is developing innovative electrochemical urea (eurea), via the co-electroreduction of nitrogen and carbon sources using renewable power. While recent research advances in electrocatalysis have demonstrated promising Faradaic efficiencies (FE) toward urea, the design of electrochemical systems involves inherent tradeoffs between key performance indicators (KPIs) such as current density, cell voltage, and FE. Crucially, the implications of electrolyzerlevel performance on plantlevel economics and environmental impacts remain poorly understood. To address this gap, we integrate process modelling with technoeconomic and lifecycle assessment (TEA-LCA) to evaluate the trade-offs of KPIs from a process systems per... [more]

Supplemental information for the article "Multi-Scale Design for Clean Energy Systems: Industrial Electrification and Flexible Operation of Ammonia Synthesis", which has been submitted to 36th European Symposium on Computer Aided Process Engineering. The document includes parametric data and model information.

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

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]

The rising levels of carbon dioxide (CO2) in the atmosphere significantly contribute to climate change, highlighting the need for effective CO2 mitigation strategies. While capturing and storing CO2 is important, converting it into useful products offers additional environmental and economic benefits. One promising method is the reverse water gas shift (RWGS) reaction, which transforms CO2 into carbon monoxide (CO). Membrane reactors (MR), which integrate selective membranes with equilibrium limited chemical reactions, have the potential to intensify processes based on the RWGS reaction. In such reactors, by-products like water are removed in-situ from the reaction zone, effectively shifting the reaction equilibrium to favor higher CO2 conversion. This study develops a comprehensive multi-scale mathematical model for RWGS membrane reactors. We integrate the microscale permeance model (for LTA-4A membrane) with the RWGS MR unit scale and the system’s scale models. The effectiveness of a... [more]

Mathematical modelling and simulation are pivotal components in process systems engineering. Focusing on polymerization process systems, identifying microscopic properties of polymers is highly sought after for advancing kinetic comprehension and facilitating industrial applications. Among various computational methods predicting polymeric properties microscopically, kinetic Monte Carlo (kMC) offers a stochastic framework to characterize individual polymer chains and track dynamic system evolution, providing mechanistic insights into complex polymerization kinetics. In this study, an accurately accelerated Superbasin-aided kMC model is developed for enhancing the kinetic understanding of the advanced photo-iniferter RAFT (PI-RAFT) polymerization. The contribution is twofold, presenting advancements in both the mathematical modelling techniques for complex dynamic process systems and the mechanistic understanding of photo-induced polymerizations. Leveraging the increased computational p... [more]

Critical minerals are essential to the future of clean energy, especially energy storage, electric vehicles, and advanced electronics. In this paper, we argue that process systems engineering (PSE) paradigms provide essential frameworks for enhancing the sustainability and efficiency of critical mineral processing pathways. As a concrete example, we review challenges and opportunities across material-to-infrastructure scales for process intensification (PI) with membranes. Within critical mineral processing, there is a need to reduce environmental impact, especially concerning chemical reagent usage. Feed concentrations and product demand variability require flexible, intensified processes. Further, unique feedstocks require unique processes (i.e., no one-size-fits-all recycling or refining system exists). Membrane materials span a vast design space that allows significant optimization. Therefore, there is a need to rapidly identify the best opportunities for membrane implementation, t... [more]

To mitigate the effects of catastrophic failure while maintaining resource and production efficiencies, energy systems need to be designed for resilience and sustainability. Conventional approaches such as redundancies through backup processes or inventory stockpiles demand high capital investment and resource allocation. In addition, responding to unexpected “black swan” events requires that systems have the agility to transform and adapt rapidly. To develop targeted solutions that protect the system efficiently, the supply chain network needs to be considered as an integrated multi-scale system incorporating every component from individual process units all the way to the whole network. This approach can be readily integrated with analogous multiscale approaches for sustainability, safety, and intensification. In this work, we bring together classical supply chain resilience with process systems engineering to leverage the multi-scale nature of energy systems for developing resilienc... [more]

Increasing wind and solar electricity generation in power systems increases temporal variability in electricity prices which incentivizes the development of flexible processes for electricity generation and electricity-based fuels/chemicals production. Here, we develop a computational framework for the integrated design and optimization of multi-product processes interacting with the grid under time-varying electricity prices. Our analysis focuses on the case study of nuclear-based hydrogen (H2) and electricity generation, involving nuclear power plants (NPP) producing high temperature heat and electricity coupled with a high temperature steam electrolyzers (HTSE) for H2 production. The ability to co-produce H2 along with nuclear is widely seen as critical to improving the economics of nuclear energy technologies. To that end, our model focuses on evaluating the least-cost design and operations of the NPP-HTSE system while accounting for: a) power consumption variation with current den... [more]

The energy transition is driven both by the motivation to decarbonize as well as the decrease in cost of low carbon technology. Net-carbon neutrality over the lifetime of technology use can neither be quantitatively assessed nor realized without accounting for the flows of carbon comprehensively from cradle to grave. Sources of emission are disparate with contributions from resource procurement, process establishment and function, and material refining. The synergies between the constituent value chains are especially apparent in the mobility transition which involves (i) power generation, storage and dispatch, (ii) synthesis of polymeric materials, (iii) manufacturing of vehicles and establishment of infrastructure. Decision-making frameworks that can coordinate these aspects and provide cooperative sustainable solutions are needed. To this end, we present a multiscale modeling and optimization framework for the simultaneous resolution of the material and energy value chains. A case... [more]

Computer-aided Molecular and Process Design (CAMPD) is an equation-oriented multi-scale decision making framework for designing both materials (molecules) and processes for separation, reaction, and reactive separation whenever material choice significantly impacts process performance. The inherent nonlinearity and nonconvexity in CAMPD optimization models, introduced through the property and process models, pose challenges to state-of-the-art solvers. Recently, quantum computing (QC) has shown promise for solving complex optimization problems, especially those involving discrete decisions. This motivates us to explore the potential usage of quantum optimization techniques for solving CAMPD problems. We have developed a technique for directly solving a class of mixed integer nonlinear programs using QC. Our approach represents both continuous and integer design decisions by a set of binary variables through encoding schemes. This transformation allows to reformulate certain types of CA... [more]

This paper discusses the symbiotic relationship between technology research and development (R&D) and energy transition modeling. On the one hand, energy system modeling has a noteworthy history of providing macroscopic views and critical insights concerning the role that myriad technologies may play in the future energy system. On the other hand, R&D can lead to both incremental and disruptive technological advances that can shape energy transition planning. In this work, we focus on the bidirectional flow of information between the two with a particular focus on highlighting the potential role of carbon capture, storage, and sequestration technology.

Sustainability encompasses many wicked problems involving complex interdependencies across social, natural, and engineered systems. We argue holistic multiscale modeling and decision-support frameworks are needed to address multifaceted interdisciplinary aspects of these wicked problems. This review highlights three emerging research areas for artificial intelligence (AI) and machine learning (ML) in molecular-to-systems engineering for sustainability: (1) molecular discovery and materials design, (2) automation and self-driving laboratories, (3) process and systems-of-systems optimization. Recent advances in AI and ML are highlighted in four contemporary application areas in chemical engineering design: (1) equitable energy systems, (2) decarbonizing the power sector, (3) circular economies for critical materials, and (4) next-generation heating and cooling. These examples illustrate how AI and ML enable more sophisticated interdisciplinary multiscale models, faster optimization algor... [more]

The production of hydrogen-based dense energy carriers (DECs) has been proposed as a combined solution for the storage and dispatch of power generated through intermittent renewables. Frameworks that model and optimize the production, storage, and dispatch of generated energy are important for data-driven decision making in the energy systems space. The proposed multiperiod framework considers the evolution of technology costs under different levels of promotion through research and targeted policies, using the year 2021 as a baseline. Furthermore, carbon credits are included as proposed by the 45Q tax amendment for the capture, sequestration, and utilization of carbon. The implementation of the mixed-integer linear programming (MILP) framework is illustrated through computational case studies to meet set hydrogen demands. The trade-offs between different technology pathways and contributions to system expenditure are elucidated, and promising configurations and technology niches are i... [more]

The growing demand for various types of paper highlights the importance of optimizing the kraft pulping process to achieve desired paper properties. This work proposes a novel multiscale model to optimize the kraft pulping process and obtain desired paper properties. The model combines mass and energy balance equations with a layered kinetic Monte Carlo (kMC) algorithm to predict the degradation of wood chips, the depolymerization of cellulose, and the spatio-temporal evolution of the Kappa number and cellulose degree of polymerization (DP). A surrogate LSTM-ANN model is trained on data generated from the multiscale model under different operating conditions, dealing with both time-varying and time-invariant inputs, and an LSTM-ANN-based model predictive controller is designed to achieve desired set-point values of the Kappa number and cellulose DP while considering process constraints. The results show that the LSTM-ANN-based controller is able to drive the process to desired set-poin... [more]

Solid Oxide Cells (SOCs) can work efficiently in reversible operation, allowing the energy storage as hydrogen in power to gas application and providing requested electricity in gas to power application. They can easily switch from fuel cell to electrolyzer mode in order to guarantee the production of electricity, heat or directly hydrogen as fuel depending on energy demand and utilization. The proposed modeling is able to calculate effectively SOC performance in both operating modes, basing on the same electrochemical equations and system parameters, just setting the current density direction. The identified kinetic core is implemented in different simulation tools as a function of the scale under study. When the analysis mainly focuses on the kinetics affecting the global performance of small-sized single cells, a 0D code written in Fortran and then executed in Aspen Plus is used. When larger-scale single or stacked cells are considered and local maps of the main physicochemical prop... [more]

Molecular dynamics simulations (MDs) are carried out for predicting platinum Proton Exchange Membrane (PEM) fuel cell nanocatalyst growth on a model carbon electrode. The aim is to provide a one-shot simulation of the entire multistep process of deposition in the context of plasma sputtering, from sputtering of the target catalyst/transport to the electrode substrate/deposition on the porous electrode. The plasma processing reactor is reduced to nanoscale dimensions for tractable MDs using scale reduction of the plasma phase and requesting identical collision numbers in experiments and the simulation box. The present simulations reproduce the role of plasma pressure for the plasma phase growth of nanocatalysts (here, platinum).