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]

Dynamic simulation of distillation columns and chemical reactors remains essential for plant design, controllability analysis, and economic optimization. High-purity separations of close-boiling mixtures present significant computational challenges due to nonlinear thermodynamic behavior and stiff differential-algebraic equation (DAE) systems. This work presents a unified Python/JAX framework integrating four computational modules: (1) Peng-Robinson thermodynamics with complex-step differentiation, (2) nonlinear solvers (Newton, Broyden, Newton-Krylov) with automatic Curtis-Reid scaling, (3) DAE solver with Radau IIA collocation and intelligent auto-selection, and (4) constrained optimization using the Augmented Lagrangian Method with JAX automatic differentiation. The framework leverages JAX's just-in-time compilation (JIT), vectorization (vmap), and automatic differentiation (AD) to achieve near-compiled-language performance. Validation includes: nonlinear solver benchmarks with Newt... [more]

Fault diagnosis is essential for ensuring safety and efficiency in chemical process industries. Conventional diagnostic systems often generate raw numerical outputs that require extensive human interpretation, increasing the operator's workload and slowing decision-making during abnormal events. To overcome these limitations, this work introduces a model context protocol (MCP)-integrated fault diagnosis framework, where a Large Language Model (LLM) functions as the MCP client, coordinating multiple diagnostic tools through a unified protocol. Within the proposed framework, the LLM interacts with specialized diagnostic tools, including a convolutional neural network-based fault diagnosis model and an ensemble-based variant for uncertainty-aware analysis. The LLM synthesizes the outputs of these tools and generates operator-oriented natural-language reports that summarize diagnostic results and explicitly communicate uncertainty, thereby supporting more transparent and efficient decision... [more]

Large-scale mixed-integer programs (MIPs) typically cannot be solved by standard solvers with reasonable computational cost. MIP-heuristics decompose large-scale monolithic mixed-integer programs into polylithic programs such that they can be solved with reasonable computational cost at the price of loosing their optimality certificate. The decomposition is steered by hyperparameters that impact the solution quality and the computational cost diametrically. The proper selection of the hyperparameter values is a black-box optimization problem which is mostly solved by grid search or random search. In previous publications the authors proposed a novel hyperparameter optimization method based on Bayesian optimization and studied a use case from the PSE domain. Computational studies showed that the BO-based algorithm is superior for objective functions with few optimal solutions.This contribution generalizes the description of the MIP-Heuristic Optimization Problem (MIP-HOP) and the comput... [more]

Porous materials such as Metal-Organic Frameworks and Covalent Organic Frameworks are emerging adsorbent materials with tunable structures and chemistry, making them useful for applications such as carbon capture, drug delivery, gas separations, and storage. This work aims to design and develop a systematic approach to build a data-driven recommendation system that leverages the historical experimental data or simulation data to assist process engineers in identifying the most suitable adsorbents from a large candidate space. In general, only some of the adsorption properties are available for porous materials owing to limited experimental data. In this scenario, this problem can be formulated as a matrix completion problem, which aims to impute the missing data by exploiting the underlying pattern in the available data. To this end, we propose a parameterization of the kernelized probabilistic matrix factorization framework, which aims to determine the nonlinear latent factors that ar... [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]

Piping and instrumentation diagrams (P&IDs) encode the functional structure of process plants and are a critical yet underutilised source of engineering knowledge for digital twins and intelligent decision support. However, digitising legacy P&IDs remains challenging due to heterogeneous drawing standards and the reliance of existing methods on brittle symbol recognition and rule-based connectivity reconstruction. This work reframes P&ID digitization as the extraction of equipment tags and inference of process topology, rather than graphical reproduction. We propose a two-stage workflow based on multimodal large language models, in which visual extraction and topology reconstruction are treated as distinct reasoning stages guided by chemical engineering process knowledge. The approach is evaluated on two ANSI-standard P&ID case studies of increasing complexity. Results show that decomposing visual extraction and topology reasoning yields more accurate and structurally consistent proces... [more]

Anomaly detection is essential for ensuring the safe and efficient operation of chemical plants. Although many deep-learning-based methods have been proposed in recent years, their evaluation remains largely limited to synthetic benchmarks such as the Tennessee Eastman Process (TEP) [1]. While these simulators enable controlled and reproducible comparisons, they fail to capture the noise characteristics, operational complexity, and irregular fault dynamics of real industrial plants, leaving the practical generalizability of many methods unclear. In this work, we extend our earlier ESCAPE study [2] beyond water-based systems to industrially relevant chemical processes. We analyze multivariate time-series data from two continuously operated pilot-plant scenarios at the Technical University of Munich, namely n-butanol/water heteroazeotropic distillation and poly(oxymethylene) ether purification, whose datasets were recently published at NeurIPS 2025 [3]. Using the open-source TimeSeAD lib... [more]

Nonconvex generalized disjunctive programs (GDPs) frequently arise in chemical engineering applications and are commonly reformulated as mixed-integer nonlinear programs (MINLPs). However, nonconvexities in these reformulations often lead to numerical difficulties, sensitivity to initialization, and degraded solution quality when solved with general-purpose MINLP solvers. This work proposes a two-phase strategy to mitigate these effects by generating improved initial points through the solution of a sequence of relaxed MINLPs, which are subsequently used to initialize the original formulation. The approach is evaluated on a family of purely disjunctive benchmark problems, referred to as the Crescent problems, with sizes ranging from 60 to 1000 binary variables. Numerical experiments using the DICOPT and SBB solvers assess performance in terms of objective value distributions, the percentage of feasible initial points, and average constraint violation. The results indicate that the prop... [more]

This work explores Generative Flow Networks (GFlowNets) as a computational approach for sustainable fragrance design, focusing on generating novel molecules that reproduce the scent profile of vanillin while reducing reliance on resource-intensive synthesis and environmentally vulnerable natural sources. An integrated pipeline couples a GFlowNet generator with a fragrance note predictor, which guides learning toward a target odor by rewarding molecules predicted to be aromatically similar to vanillin. Chemical validity and realism are enforced through chemistry filters that penalize unstable or implausible structures and through an odorless-vs-odorant classifier, so only chemically and olfactorily plausible candidates are selected. The agent is trained in a hybrid offline-online regime, implementing reinforcement-based exploration, with hyperparameters tuned via Bayesian optimization. As an independent validation layer, an olfactory receptor docking model estimates binding affinities t... [more]

Type your abstract text here. This work presents an essential methodological framework oriented to the implementation of sustainability promotion measures in process industries. It makes use of a previously developed paradigm, designated as Water and Energy Integration Systems (WEIS), which are fundamentally conceptual systems based on the implementation of several technologies implemented with the end to minimize water use, energy use and related environmental burdens. The primarily conceptual nature of these systems is significant that these have not been significantly implemented in real-life, and that these have been essentially implemented in the virtual basis of digital twin-based computational models. This work extensively presents a methodology developed for the assessment of the integrity of WEIS models, which have been developed using the capacities of a customised computational tool designated as ThermWatt. Two previously approached case-studies have been considered to perfo... [more]

High-throughput analysis of microfluidic droplets and bubbles is essential for chemical engineering but remains challenging due to the inherent loss of high-frequency details in standard deep learning models. This study proposes a novel Hand-crafted Feature Fusion framework that explicitly integrates physical priors, specifically Local Binary Patterns and Discrete Wavelet Transform, into a two-stage instance segmentation network. We design an adaptive attention-based fusion module embedded within both the Feature Pyramid Network and Region Proposal Network to synergize explicit texture cues with implicit semantic features. Validated on a large-scale dataset comprising over 64000 instances, our method achieves a test mAP of 0.808, significantly outperforming state-of-the-art architectures. Crucially, the framework effectively resolves the detection bottleneck for minute targets and elevates the small-object accuracy to 0.764, representing an improvement of nearly 20% over the baseline.... [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]

Accurate prediction of solubility limits of organic compounds in mixed solvents is critical for the design and optimization of chemical and pharmaceutical processes. Recent advances in machine learning have enabled fast and reliable prediction of physicochemical properties of molecules, including solubility. In this work, we present a Bayesian Optimization framework to identify optimal solvent combinations, compositions, and temperatures that maximize the solubility of active pharmaceutical ingredients. The optimization strategy leverages a multicomponent directed-edge message passing neural network trained on solvent mixtures to predict solubility in ternary systems consisting of a solute and two solvents. To enable efficient Bayesian optimization, we represented the solvents in a continuous space and compare three different strategies: integer enumeration, numerical descriptors, and deep embeddings. The proposed approach was tested on a dataset comprising 14 299 points in solvent mix... [more]

Batch manufacturing is central to fine chemicals, pharmaceuticals, and bioprocessing. Its operation evolves across phases and recipes, which yields high-dimensional trajectories and strong batch-to-batch variability. Meanwhile, key quality-indicative variables are often measured offline and cannot be used as online model inputs. This work presents an integrated deep learning framework that unifies soft sensing and process monitoring in a single module using only process variables as inputs. A multi-scale Temporal Convolutional Network with multiple kernel sizes extracts complementary dynamic features from sliding windows. These features are concatenated and pooled into a compact representation that feeds two task branches. A variational autoencoder branch reconstructs the input window and provides fault monitoring signals via reconstruction deviation while regularizing the latent space through KL divergence. In parallel, a prediction branch estimates the quality-indicative variable dir... [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]

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]