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.

A survey was conducted of 103 students and lecturers who had recently participated in chemical engineering design courses concerning their opinions on the use of Generative Artificial Intelligence (Gen-AI) in their capstone design education. Participants were at universities in Europe, the Middle East, North America, and South America, from at least eight different language groups. The survey found little difference in responses between students and lecturers, except for uptake, in which students reported higher rates of familiarity and adoption of Gen-AI tools than instructors. Both groups were net-positive generally on the use of Gen-AI in the classroom, reporting relatively high confidence in the ability to assess results, the general positive benefits of using Gen-AI in their chemical process design education, and the likelihood of using them in the future. However, participants reported that their trust in the results of Gen-AI tools was relatively low.

The Imperial College Integrated Design Project reframes the chemical engineering capstone as a structured educational journey that develops professional competence rather than simply delivering a final technical report. The programme is grounded in four pedagogical pillars-authenticity, integration, impact, and reflection-which align with the graduate attributes required by the Institution of Chemical Engineers. Authenticity is achieved through open-ended problems drawn from industrial partners and emerging research needs; integration connects knowledge from across the curriculum into a coherent systems perspective; impact emphasises user-centred, sustainable solutions; and reflection cultivates metacognitive awareness of decision making and learning from failure. A mentored-autonomy model supports student teams through weekly checkpoints, skills workshops, and access to disciplinary experts. Assessment deliberately balances artefact quality with evidence of process, rewarding reasonin... [more]

Ammonia has received in recent years significant attention as potential carbon free fuel. However, its combustion properties limit its direct application for both providing heat and in power generation through gas turbines. Ammonia cracking is one potential solution to circumvent the problem by producing hydrogen. When using the ammonia in gas turbines, it is possible to heat integrate the endothermic decomposition reaction with the exhaust gas from the gas turbine. Thermodynamic and kinetic limitations have however a major impact on the achievable ammonia conversion. Based on the consideration of these limitations, this paper presents a detailed investigation of key design parameters affecting the overall process efficiency utilizing both an equilibrium reactor model and a reactor model based on detailed kinetics and heat transfer. Ammonia decomposition should occur at sufficiently high pressure to avoid a) the com-pression energy demand for achieving the pressure of the combustion ch... [more]

Flowsheet synthesis is an integral step in process design, entailing the selection of a set of unit operations and their connectivity to convert raw materials to products. Superstructure optimisation represents a promising class of synthesis approaches, allowing for the systematic exploration of the flowsheet design space. Despite this, many superstructure formulations suffer from numerical instabilities, combinatorial explosion, and/or rely on restrictive assumptions on the types of flowsheet alternatives that can be considered. The modified state-operator network (MSON) formalism has recently been proposed to address some of these issues for isobaric flowsheets. The constant-pressure assumption restricts the applicability of the MSON to real process applications as pressure is a key process variable in many unit operations, such as distillation, reaction, and extrusion, and is necessary to elicit flow. In this work, we present the extended MSON (E-MSON) which inherits the numerical s... [more]

In this study, we propose a method to uniquely determine robust operating conditions for simulated moving bed (SMB) chromatography, an essential continuous liquid-phase separation technique in the pharmaceutical industry, in the form of explicit algebraic equations. The proposed method incorporates process robustness-defined as the probability of meeting the target purities under flow-rate uncertainty due to pump errors-without requiring computationally expensive dynamic simulations. In a computational demonstration, the method achieved a joint probability of 0.960 for simultaneously attaining 99.9% purity in both extract and raffinate products.

This work aims to systematically generate intensified process designs by integrating reinforcement learning (RL)-driven process synthesis and phenomena-based modeling via Generalized Modular Framework (GMF). Rather than considering flowsheet synthesis with conventional unit-operations, GMF utilizes fundamental building blocks, also known as mass and heat exchange modules, to describe the physiochemical phenomena and to enhance novel process discovery. At its core are driving forces which characterize the mass transfer feasibility based on the total change in Gibbs free energy of the system. RL is integrated with this phenomena-based modeling strategy to drive flowsheet generation by exploring much of the total action space and minimizing pre-postulation of stream connections. All possible inlets, outlets, and interconnections between modules are contained in a stream matrix. Deep Q-Network is used as the RL agent, which contains a multi-layer convolution neural network followed by a mu... [more]

Although solvent-based crystallization is widely adopted for separation and purification of crystalline pharmaceutical products, solvent choice and utilisation critically influence product quality, manufacturing cost, and the environmental performance of the pharmaceutical process. Escalating demands to reduce process mass intensity (PMI), together with increasing vulnerabilities in the supply chains, necessitate the development of more efficient and resilient process designs, incorporating solvent and active pharmaceutical ingredient (API) recycling. The conceptual design of crystallization processes offers a viable route to identify flowsheets with substantially reduced solvent consumption. In this paper we present a computer-aided molecular and process design (CAMPD) formulation to explore the benefits of solvent/API recycle for two processes/APIs: (i) a continuous cooling crystallization process for mefenamic acid (MA) employing a binary solvent mixture and (ii) a batch cooling cry... [more]

Heat Pumps (HPs) can play a vital role in the decarbonization of heating in industry. The performance of a HP strongly depends on the refrigerant, the working fluid within the HP. In order to maximize HP performance, systematic selection of the refrigerant is key. Refrigerant choice affects the very feasibility of employing a HP to deliver heating to a process. A flexible and robust method is required to select refrigerants that are the best fit for a given heating application. A computer-aided molecular & process design (CAMPD) method is developed to design the optimal refrigerant that is tailored to process needs. The method is applied to three case studies across which the HP performance objectives and constraints, and heat source and heat sink temperatures are varied. In addition, the design of refrigerants with low (<150) global warming potentials and zero ozone depletion potentials is investigated. For all applications across all case studies, the CAMPD approach successfully iden... [more]

Grid-connected electrolyzers with intermediate hydrogen storage offer significant potential for reducing electricity costs through flexible operation under dynamic pricing. A threshold-based scheduling optimization approach is developed that derives interpretable on/off production rules from electricity price signals. The method identifies local price thresholds separating high-price from low-price periods, yielding binary production schedules. Adaptive horizon partitioning-subdividing the scheduling horizon when constant thresholds become infeasible-is combined with a receding horizon strategy that implements only a portion of each optimized schedule before re-optimization. This procedure enables systematic investigation of how characteristics of Integrated Electrolyzer-Storage Systems (IESS) influence cost reduction potential while maintaining computational tractability for both offline analysis and online implementation. A case study applying the approach to historical German electr... [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.

Decarbonizing ammonia by replacing grey with green hydrogen directly affects the operation of the Haber-Bosch (HB) process. When directly coupled to green hydrogen production from renewable energy, the HB process should be able operate flexibly to match variable hydrogen supply. This study presents a structured approach for designing a load-flexible HB plant, supported by a rigorous process model. First, we screen 2, 000 designs at high (100%) and low (10%) hydrogen loads to assess operability. Only 1, 100 designs are feasible for both loads, underscoring the need to account for multivariable interactions during design. Next, we assess the economic feasibility of a base design, comparing HB operation under constant and flexible loads. Flexible operation reduces the levelized cost of ammonia (LCOA) by about 5.8%, primarily by lowering green hydrogen production costs. This cost reduction results from downregulating hydrogen production during periods of high electricity prices. By contras... [more]

Pharmaceutical and fine chemical industries face strong pressure to shorten time-to-market while maintaining compliance with complex regulatory frameworks. These conflicting demands require rapid process design, validation, and scale-up. Modular production plants standardized in VDI 2776 and VDI/VDE/NAMUR 2658 have emerged as a promising strategy to shorten engineering and validation efforts. The Product-Process-Resource (PPR) philosophy represents a key approach to efficient data management in modular plant engineering. It enables the separation of different flexibility dimensions into distinct, relevant aspects that can ideally be exchanged or modified independently. To realize this principle in practical applications, formalized information models and ontologies serve as a key enabler for structuring and managing semantic data. This work investigates several information models and ontologies for the process engineering domain regarding their suitability to support separation accordi... [more]

The increasing demand for energy, water, and chemical products signals the need for more sustainable and efficient process design methodologies. Traditional methods for conceptual process design constrains the exploration of novel and intensified process alternatives, as they rely on prior knowledge in defining the design space. Previous studies employing bottom-up approaches, such as phenomena building blocks (PBBs), suggest that the synthesis of complex bottom-up flowsheets remains computationally challenging and is thus limited to the synthesis of individual units of operation. This work proposes a bottom-up, data-driven framework for process synthesis and intensification based on phenomena building blocks (PBBs), in which process flowsheets are constructed from their underlying physical and chemical phenomena rather than conventional units of operation. The proposed framework introduces a phenomena-based text representation and data collection module. Furthermore, a sequence traini... [more]

Artificial neural networks (ANNs) have emerged as powerful surrogate models in process design and optimisation, capable of capturing complex nonlinear process behaviour while significantly reducing computational cost compared to detailed first-principles simulations. However, ANN prediction errors in safety-critical applications can lead to suboptimal or vulnerable designs, necessitating rigorous treatment of approximation uncertainties. While probabilistic approaches exist for surrogate-based decision making, risk-averse contexts that require formal robustness guarantees face a fundamental challenge: the nonconvex nature of ANN-embedded models hinders the employment of standard robust optimisation methods. To this end, in this work we explore the global robust optimisation of process design problems with embedded ANNs. A robust spatial branch-and-bound (RsBB) algorithm to achieve global optimality is proposed while enforcing constraint satisfaction across all uncertainty realisations.... [more]

The use of equipment digital twins of standardized, multi-purpose units can accelerate process development and reduce experimental effort. Experimental data are essential not only for identifying critical process parameters and enabling model-based methods within a Quality by Design framework, but also for constructing and validating the simulation models that describe digital twin behavior. To achieve high-fidelity and robust predictive models, structured concepts are required to manage metadata and process-, product-, and resource-specific information exchanged between physical and digital twins. Electronic lab notebooks (ELNs), which contextualize experimental data, must therefore be structured and standardized to ensure interoperability and seamless data exchange. For integration into digital twin workflows and process transfer between equipment instances of the same category, ELNs must comply with FAIR (Findable, Accessible, Interoperable, Reusable) data principles. This work prop... [more]

Machine learning (ML) has attracted growing interest in process systems engineering for its potential in process design, synthesis, and optimization. By learning complex patterns from data, ML methods complement traditional first-principles modelling and heuristic approaches, particularly for conceptual process design and the exploration of alternatives. Although current text-based representations capture unit-level connectivity, they lack a holistic view of process intent, equipment hierarchy, and contextual information to guide learning and inference. Consequently, models trained on such linear token sequences tend to reproduce syntactic structure rather than underlying process reasoning, thus limiting interpretability and user control. In this work, we introduce a contextual framework for representing process flowsheet information in ML models that embeds process engineering logic directly into the model inputs. The approach combines a structured, text-based representation of proces... [more]

Modular process simulators are widely used in industry due to their robust and detailed unit operation models. However, their application to gradient-based process optimization remains challenging, as these simulators are typically treated as black boxes, limiting access to internal equations and derivatives. As a result, finite difference methods are commonly employed for gradient estimation, despite their sensitivity to numerical noise and poor scalability. While previous studies have demonstrated the benefits of analytical derivatives in modular simulators, these approaches have largely relied on tangent differentiation modes. This work proposes a non-intrusive methodology that couples analytical derivatives with the adjoint mode of automatic differentiation to efficiently compute gradients for process optimization in modular simulators. The approach preserves the robustness of existing simulation tools by performing simulations normally to convergence, followed by external adjoint-... [more]

In this work, we present quantum reinforcement learning algorithms for process flowsheet synthesis. Particularly, we discuss the implementation of encoding strategies to improve the algorithmic scalability. Reinforcement learning (RL)-driven flowsheet synthesis techniques provide a promising approach for conceptual process design, in addition to traditional optimization-based methods. These RL-based strategies identify the optimal flowsheet configurations from a maximum set of available processing units, without requiring to pre-postulate an interconnected superstructure. However, the resulting combinatorial design space for RL can scale extensively with the increased number of available processing units, which can render the algorithms to be computationally intensive or even intractable. To address this challenge, our prior work has introduced a quantum-enhanced approach to RL-driven process synthesis. However, this algorithm was limited in its capacity to solve larger flowsheeting pr... [more]

Modular plant design requires assessing whether independently characterized process requirements and module capabilities are compatible-a challenge that established methods address incompletely. Feasibility and flexibility analysis, as well as Quality by Design, typically assume integrated single-domain models where all variables belong to one coherent description, yet modular design involves domains that originate from different sources, evolve independently, and connect through interface variables. This work proposes Quantified Constraint Satisfaction Problems (QCSPs) as a formulation for interface-level suitability assessment: universal quantification encodes properties that must hold across their entire admissible range (e.g., physical properties, uncertain or environment-dependent characteristics requiring robustness), while existential quantification encodes variables where at least one feasible value must exist (e.g., critical process parameters, control inputs, configuration op... [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]

This work presents guidelines for controlling and intensifying mixing in oscillatory opposed-jet mixers, focusing on Confined Impinging Jets (CIJs) as a model system where flow behavior is primarily governed by oscillatory parameters, decoupled from geometric complexity. Computational Fluid Dynamics (CFD) simulations were used to investigate the effects of oscillation amplitude and frequency on mixing. The results show that at high amplitudes, mixing is robust across a broad frequency range, as energy injection is sufficient to promote vortex formation and their propagation to the reactor's outlet. At low amplitudes, mixing is highly sensitive to the oscillation frequency and occurs only near the resonance frequency, the specific frequency at which the flow's response to the applied oscillation is maximized. At low amplitude, lower frequencies fail to inject sufficient energy, while higher frequencies promote flow segregation. Remarkably, effective vortex propagation and mixing were ac... [more]

This work presents a data-driven surrogate modeling framework for predicting distillation behavior assuming an infinite number of stages and distillation limits informed by residue-curve topology and pinch-point feasibility analysis. The framework provides a direct mapping from feed composition and distillate-to-feed ratio (D/F) to distillate and bottom product compositions, making it suitable for flowsheet synthesis and optimization applications. The approach combines three components: a classifier that identifies feasible singular-point splits, a boundary regression model that predicts D/F limits separating pure- and mixed-product operating regimes, and a neural network that interpolates product compositions in the intermediate regime. The method is demonstrated for the ternary system ethanol, benzene, and water at 1 atm using data generated from rigorous vapor-liquid-liquid equilibrium analysis. Results show that the framework provides reliable predictions for pure splits while reta... [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]

The objective of the present work is to optimize the simulation of an electrodialysis reactor for the desalination and regeneration of multi-ionic wastewater with high salt contents and conductivities, within the framework in the Sustainable Development Goal 6 (clean water and sanitation) and remarking the Electrodialysis (ED) as a highly energy-efficient and sustainable technology. The mathematical modelling has been carried out by using a semiempirical model that involves an algebraic system of differential equations, including mass and charge balances (taking into account the ions present in the wastewater: Na?, Ca²?, Mg²?, Cl?, SO4²?, and HCO3?), and the total electrodialysis stack voltage considering ohmic drops (in the dilute and concentrate compartments), the potential of membrane in each cell pair, and the electrode potentials. In the simulation process, different theoretical and experimental parameters are necessary such as number of cells, membrane working areas, efficiency,... [more]