Global optimization of industrial-scale chemical process flowsheets remains challenging due to nonlinearity, nonconvexity, and large problem scale. While equation-oriented modeling frameworks enable high-fidelity representation of industrial processes, obtaining globally optimal solutions is often computationally intractable for off-the-shelf solvers. In this work, we present a decomposition-based global optimization strategy that solves a high-fidelity flowsheet model from the IDAES framework with the Structured Nonlinear Global Decomposition (SNoGloDe) framework. The proposed approach exploits spatial decomposability by partitioning the flowsheet into coupled subproblems linked through a small set of complicating variables and solving them within a prioritized spatial branch-and-bound framework. The methodology is demonstrated on a hydrodealkylation (HDA) process for benzene production, a nonconvex and industrially relevant case study. The flowsheet is decomposed into reactor and sep... [more]

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 work aims at verifying the CO2 absorption capture model using monoethanolamine (MEA) solvent developed by the U.S. DOE's Carbon Capture Simulation Initiative (CCSI) and performing uncertainty propagation of mass transfer, liquid hold-up and reaction kinetics properties in the complete model, which includes absorber and stripper columns. The verification of the Aspen Plus CCSI model, based on pilot plant data from the National Carbon Capture Center (NCCC) for a CO2 flue gas concentration between 7 and 11% (mol) allowed uncertainty quantification (UQ) analysis for four different selected operational points using Monte Carlo Simulation (MCS), where low liquid mass transfer parameters exhibited an impact on calculation convergence. Gaussian Processes (GP) surrogate model was implemented, followed by a sensitivity analysis in order to correlate the most sensitive parameters with studied outputs.

Hierarchical optimization problems such as (generalized) semi-infinite optimization problems and bilevel problems appear in various disciplines of process systems engineering, such as flexibility analysis or parameter estimation. Adaptive discretization-based algorithms are a family of methods to solve these problems. In these methods, the original problem is decomposed into subproblems, which are solved with a standard optimization solver and then refined iteratively. Several related algorithms have been published. Until recently, computational studies have typically been performed using publication-specific implementations and benchmark problems. We recently published a software package - libDIPS - comprising existing adaptive discretization-based algorithms and a library of test problems for comparison. Several of the algorithms implemented in libDIPS exhibit strong parallelization potential in their algorithmic steps: In the algorithms of Mitsos [Optimization 60:1291-1308 (2011)] a... [more]

Modular plants emerged as the key solution for reducing time-to-market and increasing flexibility in the process industry by combining different modules known as Process Equipment Assemblies (PEAs). While PEA automation is standardised through the Module Type Package (MTP), comparable tools for their documentation remain absent. This work presents the Semantic PEA Datasheet (SPEAD) ontology, which represents PEA documentation as a machine-readable knowledge graph that adheres to the FAIR principles. SPEAD integrates established standards such as DEXPI and the VDI 2776 guidelines and ensures data quality through comprehensive annotations and constraint-based validation. The ontology was evaluated against twelve competency questions derived from a representative use case as well as competency questions from the literature using a continuous stirred-tank reactor PEA as well as a dosing PEA as example systems. SPEAD successfully covers operational and design parameters as well as interface... [more]

Cement is regarded as the most widely used construction material worldwide; however, its production is also recognized as a major contributor to global CO2 emissions. Strict control of cement quality is therefore required to prevent excessive consumption of raw materials and energy, which would otherwise increase the process environmental footprint. Cement quality is largely governed by clinker quality, which is primarily characterized by two quality control parameters: free-lime content and alite fraction. At present, these are characterized by costly and time-consuming laboratory analyses that are not optimal for real time process control and optimization. Hence, in this work, a soft sensor for the real-time estimation of the clinker alite fraction is proposed. The developed soft sensor is designed to adapt to process drifts and operating condition changes, capture nonlinear and dynamic behavior, and retain interpretability through a Partial Least Squares (PLS) modelling framework. T... [more]

Accurate life cycle assessment (LCA) depends on robust characterization factors (CFs), which quantify impacts such as ecotoxicity through the integration of fate (FF), exposure (XF), and effect (EF) factors. While databases such as USEtox and Ecoinvent provide essential CFs, significant data gaps remain, particularly in ecotoxicity endpoints like hazardous concentration 50% (HC_50), which directly inform effect factor calculations. Existing machine learning models can predict such values, but they often lack interpretability, which limits trust and transparency in environmental modeling. To address this, a machine learning framework is applied that utilizes symbolic regression (SR) and genetic programming (GP) to predict missing HC_50 values from physicochemical descriptors. A dataset with 14 descriptors was used to train SR models capable of generating interpretable mathematical expressions that link chemical properties to HC_50 values. SR models were benchmarked against prominent bla... [more]

The bioprocess industry is actively exploring technologies associated with the fourth industrial revolution, with modeling offering considerable potential for process optimization. Nevertheless, model adoption in industry remains limited. This is partly because model development continues to depend heavily on offline sampling, and because relatively few industrial applications convincingly demonstrate their practical value. This study therefore first examines the benefits of online rheology and online biomass measurements for model development and demonstrates, among other aspects, that online biomass significantly improves model fidelity. The second part examines how electricity prices affect process conditions, a key factor in production, and finds that, contrary to common practice, maximizing all operating parameters is not the most cost-effective strategy. Finally, an insilico framework for model predictive control, applied to a reactor endfill scenario, demonstrates that oxygen... [more]

As chemical processes become increasingly complex and costs of experimentation increase, understanding the practical effectiveness of Active Learning methodologies is essential. In this regard, an ongoing debate is occurring within the research community about the use of Design of Experiments (DOE) and Bayesian Optimisation (BO). However, this debate is limited by the scarcity of systematic comparative studies. Therefore, this work provides a comparative analysis of two widely adopted data-driven optimisation approaches: DOE and BO. The comparison is conducted across two distinct case studies reflecting different levels of complexity, regarding the quantity and variety of input variables involved. The first case study represents a realistic in silico experimental scenario, with multiple decision variables of different types (continuous, categorical and mixture), and two distinct single-objective optimisation goals, while the second one considers a simpler, well-known benchmark model wi... [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]

Addressing the urgent transition to low-carbon energy systems requires microgrids capable of locally coordinating electricity generation, storage, and flexible consumption. Their efficient integration calls for control strategies that are scalable, privacy-preserving, and robust to uncertainty. To address such a challenging control problem, this work proposes a decentralised Multi-Agent Reinforcement Learning (MARL) approach based on the Cross-Entropy Method (CEM) for the coordination of prosumers, equipped with renewable generation and vehicle-to-grid capabilities. To improve sample efficiency and robustness, the policy is warm-started using Behaviour Cloning (BC) from a classical Proportional-Integral-Derivative (PID) controller, resulting in a hybrid BC-CEM framework. The proposed method is evaluated in a realistic microgrid simulation with stochastic demand and real weather and generation profiles. Results show that BC-CEM accelerates convergence and achieves lower energy costs com... [more]

In this study, we propose a novel trust-region funnel (TRF) optimisation framework for process systems that integrate external black-box models, such as rigorous models, within equation-oriented (EO) formulations. The framework is applied to optimise a synthetic natural gas production process combining direct air capture and catalytic CO2 conversion using dual-function material (DFM) technology, with the objective of minimising the total annualised cost. The problem is formulated in Pyomo and solved using IPOPT, treating the DFM reactor as an external black-box model. The TRF method achieves substantial improvements compared to published mixed-integer nonlinear programming and direct nonlinear programming approaches, reducing capture cost from 460 USD to 426 USD per tonne of CO2. Key design improvements include reducing the number of DFM units per train by one-third and achieving a 22% reduction in DFM capital costs. These results highlight the TRF framework's ability to overcome numer... [more]

Understanding the relationships between operating conditions and product formation pathways in biomass pyrolysis remains challenging due to the complex interactions among temperature, catalytic effects, and feedstock composition. In this work, principal component analysis (PCA) was applied to investigate the combined influence of temperature and catalyst-to-biomass ratio on the pyrolysis of sugarcane bagasse and Salicornia. To preserve mechanistic interpretability, two complementary analyses were performed: one considering only catalytic experiments and a second integrating both thermal and catalytic conditions. Separate PCA were conducted for product yields, gas and liquid compositions, and solid-phase FTIR features. The results reveal that thermal conditions promote severe cracking and solid carbonization, whereas catalytic operation favors secondary pathways associated with controlled dehydration and partial stabilization of liquid products. Distinct patterns between the two feedsto... [more]

Mathematical models are paramount to the field of reaction engineering, facilitating reaction mechanism discovery, process optimisation, and informed decision making in academic and industrial settings. Nevertheless, the development of precise mechanistic reaction rate models remains experimentally intensive, requires expert knowledge, and is susceptible to the introduction of structural bias. Similarly, the identification of a suitable reaction network that depicts all chemical transformations remains a non-trivial task, with existing techniques often being ill-suited for large and complex systems, hence limiting their scalability and implementation within chemical and biochemical applications. This work develops a two-stage autonomous framework that exploits non-linear sparse optimisation to identify the minimum size global reaction network representative of the system under study, and subsequently proposes and discriminates between interpretable rate equations developed through symb... [more]

Digitalization, artificial intelligence, and autonomous experimentation are reshaping chemical process development by enabling data-driven system identification and model-based optimization. Despite these advances, mechanistic models remain a cornerstone for predicting chemical reaction behavior and supporting optimization. However, purely mechanistic models often exhibit limited predictive accuracy when key phenomena affecting kinetics, mass and energy transfer are not fully captured. To address limitations on kinetic modelling, a hybrid modelling framework is proposed in this work that integrates a lumped power-law kinetic model with a Gaussian Process (GP) residual model to predict the reaction rate across the experimental design space while quantifying the uncertainty of the predicted rate. The hybrid model is then coupled with multi-objective Bayesian optimization (MOBO) by employing a weighted-sum approach and an upper confidence bound acquisition function to guide experimental d... [more]

Mathematical models in process systems engineering (PSE) are widely used to support decision-making in design and operation, but they are mostly limited to deterministic models. For biochemical systems, the biological variability can give rise to stochastic dynamics. This work addresses the question of when to act in such processes, as the stochastic dynamics affect the timing of important events. We consider the case of batch production of malic acid using Ustilago trichophora. The goal is to predict when the substrate concentration falls below a predefined threshold. We extend an existing deterministic model of the process to a stochastic differential equation (SDE) formulation by introducing a Monod-like noise term. Simulations of the SDE model reveal a distribution of substrate depletion times and a deviation between the mean of the stochastic trajectory and the deterministic solution due to nonlinear effects. To determine optimal intervention times under uncertainty, we formulate... [more]

Laboratory safety is procedure-dependent: required personal protective equipment (PPE) and permissible actions vary across experiments and across experimental steps, yet most vision-based monitoring remains appearance-driven and often produces generic warnings without reliable procedural context. We propose a multimodal framework for step-aware safety monitoring in laboratory videos. The framework first localizes procedural context through clip-level step prediction and protocol alignment to identify the experiment and current step. Given this context, it retrieves step-specific safety constraints, extracts evidence of step-relevant equipment and interactions using an equipment database, and prompts a video-capable vision-language model (VLM) to generate structured (JSON) monitoring reports supported by retrieved constraints and visual evidence. Experiments on protocol-annotated molecular biology lab videos show that our approach improves the mean score from 0.4352 to 0.6430 and reduce... [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]

Low-temperature industrial processes release substantial amounts of waste heat, representing a largely untapped renewable energy resource. This study focuses on the brewery sector, encompassing both beer and whiskey production, along with its integration with manufacturing and city. The brewery industry generates approximately 0.061 kWh of waste heat per liter of beer, while whiskey production releases around 2.2 kWh per liter, with most of this waste heat available at temperatures close to 95 °C. Such low-grade heat is well suited to meet heating demands in manufacturing industries and urban district heating networks, where temperature requirements typically remain below 80 °C. Multiple technological options for meeting process heat requirements and recovering waste heat are evaluated using the OSMOSE energy integration framework. The study assesses the technical performance and economic viability of these options under varying assumptions for electricity prices, natural gas prices, a... [more]

The purification of therapeutic peptides represents a major bottleneck in biopharmaceutical downstream processing due to the structural similarity between target products and closely related impurities. In this study, a shortcut dynamic simulation model of a two-column Multi-Column Countercurrent Solvent Gradient Purification (MCSGP) process is implemented in Aspen Chromatography for peptide separation. Each column is described using a one-dimensional axial dispersion model coupled with mass transfer kinetics and a modulated Langmuir adsorption equilibrium, while time-dependent boundary conditions are applied to represent solvent gradient elution. The model explicitly incorporates internal recycle streams between columns using the cycle organizer approach, capturing the defining operational features of MCSGP. This enables a unified representation of chromatographic transport phenomena, gradient operation, and discrete recycle logic within a single flowsheet-based framework. The novelty... [more]

Sonochemical reactors are a promising technology in process intensification, offering a sustainable and energy-efficient means of enhancing chemical reactions. By harnessing acoustic cavitation - the formation, oscillation and violent collapse of bubbles in a liquid medium - these systems generate local hotspots that can accelerate reaction kinetics. Despite its potential, efficient design and scale-up of sonochemical reactors remain major challenges, mostly because the cavitation phenomena take place close to the ultrasonic transducer. This work presents a simulation-based framework for the optimization of sonochemical batch reactors by coupling microscopic-level bubble behavior with macroscopic-level reactor performance, focusing on the placement of transducers to maximize reaction activity.

In today's competitive LNG market, reducing energy consumption is critical for enhancing both profitability and sustainability. The hot section of the LNG processing, which includes inlet receivers, acid gas removal, and dehydration units, is the most thermally demanding. Previous optimisation methods targeted each unit separately. On the other hand, this work details the development of a data-driven optimisation framework to minimise energy across these interdependent units. Preliminary application of the framework has yielded encouraging results. Utilising HYSYS process simulation data, the study successfully identifies critical operating variables-such as reboiler duty, amine circulation rate, and air-to-furnace stoichiometry-that drive production efficiency and energy consumption. Results indicate that a baseline condensate mass flow of 2, 048.71 kg/h is achieved at a stripper bottom temperature of 137.74 °C, while the AGRU produces sweet gas with 0.18 ppm H2S. Optimisation using P... [more]

Nanoparticle (NP) synthesis has been extensively studied since the mid-1800s and are utilized across numerous fields due to their unique microscopic properties that collectively yield macroscopic benefits. Of particular interest are silver (Ag) NPs, whose controllable size and morphology impart distinct catalytic, electronic, and optical properties advantageous for environmental and energy-related applications. The theoretical understanding of NP nucleation and growth has advanced considerably starting with classical nucleation theory, evolving into the LaMer model centering on burst nucleation and diffusion-limited growth and resulted in near monodispersed hydrosols. Finke and Watzky later introduced the autocatalytic model considering a slow and continuous nucleation and autocatalytic surface growth not limited by monomer diffusion. However, the precise mechanisms remain the subject of active debate for the different homogeneous and heterogenous nucleation systems. In this study, sim... [more]

This paper presents ProcessSimulator.jl, an open-source process simulation framework built in Julia that combines acausal, equation-oriented modelling with seamless integration of procedural code. The framework leverages ModelingToolkit.jl to enable modular construction of unit operations using symbolic-numeric representations, facilitating the extension of models with advanced thermodynamics, kinetics, and data-driven components. Inspired by the ModuSim concept [3], ProcessSimulator.jl introduces an extensible control-volume abstraction and connector-based composition at the unit-operation level. A steady-state CSTR case study is presented and compared against Aspen Plus, showing good agreement in key variables. The results demonstrate the feasibility of a flexible, open, and composable process simulation paradigm for research and education.

The SEMPRE-BIO project tested and validated three different innovative technologies and pilots within the Horizon Europe framework. One of the pilots is commissioned in Belgium. The proposed technology purifies biogas from manure anaerobic digestion and delivers simultaneously pure biomethane and food-grade CO2, conversely to conventional purification technologies such as absorption and adsorption. Due to the severe cryogenic conditions, energy recovery for purge and waste streams becomes relevant to improve the energy demand of the process. The present work will show an effective solution to reduce the electricity demand of the process. Biomethane slip and other purge stream are valorised in a steam boiler and a two-pressure-level steam turbine to deliver both middle pressure steam as utility in distillation reboilers and produce electricity. The analysis will propose a simple, but rigorous methodology to maximise the steam turbine loop and the net power. The present work is based on... [more]