Economic and environmental analysis of isopropanol-acetone-hydrogen based chemical heat pump (IAH-CHP) were studied at the endothermic reaction temperatures (heat input) of 100°C-200°C at gross temperature lifts of 50°C, 75°C and 100°C and heat output capacity of 500 kW.
Attached files are the following:
Equipment cost correlations along with key parameter values used in economic analysis.
List of ecoinvent processes used in Life cycle assessment studies.
Sample economic calculation file for 130°C/205°C case.
Optimization results - decision variable values along with cost details for all the cases studied.

This supplementary material provides supporting methodological and numerical information for the study of uncertainty-aware fleet planning for heavy-duty freight. It expands on the construction of the case study, including the truck technologies, route characteristics, freight classes, operating scenarios, and economic and environmental assumptions used in the stochastic optimization model.
The document also provides additional formulation details, data tables, computational settings, and supporting analyses needed to interpret and reproduce the results. Together, these materials complement the main manuscript by documenting assumptions and calculations that could not be included in the article because of space limitations.

Supporting Information document to submission for Foundations of Computer Aided Process Operations - Chemical Process Control Conference 2027

Recent research has introduced large language model (LLM)-based chatbots to help practitioners interpret solutions of optimization models. Existing chatbots primarily support the interpretation of a solution obtained from a single solve. In rolling horizon optimization, however, practitioners need to understand how and why solutions change across successive solves as the planning window advances and changes occur in model data and/or structure. We present an LLM-based agentic framework for this comparison task, works for time-indexed linear programming (LP) and mixed-integer linear programming (MILP) models represented in Pyomo. Four deterministic analytical modules detect model changes, quantify solution differences, assess whether prior decisions remain feasible under the updated model, and and links differences between successive plans to changes in model parameters and structure. LLM agents interpret user questions, select the corresponding module, and synthesize its numerical resu... [more]

This document presents the objective functions for the deterministic case study and the reformulated stochastic model, along with the tables and data used in the modeling process.

Contains 337 original peer-reviewed research articles presented at the 36th European Symposium on Computer Aided Process Engineering (ESCAPE 36) in Sheffield, UK. Subject categories include CAPE in Circular Economy, CAPE in Clean Energy Systems, CAPEing with Uncertain Futures, Pharmaceutical & Biotechnological Systems, Modelling & Simulation, Concepts, Methods & Tools, Process Design, Scheduling & Optimisation, Process Control & Operation, Education, and Knowledge Transfer & Entrepreneurship.

Biomethane can serve as an alternative to fossil fuels. Pipeline and compressor infrastructure must be built to enable the feed-in of biomethane from decentralized production sites into the existing gas network. Costs can be reduced by sharing this transport infrastructure among individual biogas plant operators. To find the cost-optimal layout, a mixed integer linear programming (MILP) problem is formulated. The model is expanded to show the effect of considering not only pipe cost but also compressor cost in the objective. It is applied to an Austrian region to find the optimal network for 14 biogas plants. After optimization, two allocation methods for sharing investment costs among biogas plants are explored: an equal cost approach and a volumetric share approach. Optimal network topology changes due to the modified objective, resulting in significant cost savings of 9 % for the total region and 19 % for a subregion. Investment costs for individual biogas plant operators vary widel... [more]

With the increasing share of volatile renewable energies, there is a growing need for flexible storage systems to balance fluctuations between generation and demand. Multi-energy systems, featuring battery and hydrogen storage systems, provide an efficient and scalable solution for this purpose. While rule-based control is primarily used in industry, model predictive control (MPC) is considered the most promising strategy for cost-optimized and safe operation. The performance of such controllers depends heavily on the capacities, power rates, and degradation behavior of the storage systems; for hydrogen systems, it can also depend on minimum switch-on/off times and ramping rates. This work aims to use sensitivity analysis to quantify the influence of variations in these component parameters on control quality. To this end, a lab-scale battery-hydrogen storage system (TU Wien) is modeled and operated using MPC to minimize grid exchange and thus increase self-sufficiency. Experimental da... [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]

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]

This talk presents possible strategies for the calibration of crystallization models aimed at predicting particle size distributions (PSDs) of active pharmaceutical ingredients (APIs) when using industrial datasets, which are limited in terms of number or information for the modeling exercise. Industrial data concerning a seeded batch cooling recrystallization of an API in an organic solvent are used as a case study, representing an example of the issues to be faced with real-world experimental datasets. The results are discussed showing how the model performances can be deemed satisfactory, at least from the industrial perspective, and how this can be useful to enhance process understanding and to guide process development and scale-up.

This submission corresponds to the supplementary material for article submitted to FOCAPO titled:
"Privacy-Preserving Coordinated Operation of Multi-Player Industrial Network Using Secure Aggregation".

Physics-Informed Neural Networks (PINNs) offer a promising approach for integrating first-principles modeling with data-driven methods, especially in dynamic thermal systems. This study introduces a hybrid PINN framework for a one-dimensional heating rod governed by heat trans-fer equations. Unlike traditional PINNs that rely on time-dependent automatic differentiation, this approach employs numerical derivatives to bypass gradient saturation and enhance robustness. The proposed model demonstrates accurate extrapolation and generalization with limited train-ing data and is effectively used as a surrogate in a Model Predictive Control (MPC) framework for rod-tip temperature regulation. Additionally, a plan is outlined to apply physics-informed dimen-sionality reduction and model order reduction to improve computational efficiency and enable real-time application. The findings affirm PINNs' potential as control-oriented reduced models for thermal processes.

Supplementary material describing the refinery optimization problem used in the manuscript ``Physics Constrained Machine Learning for modeling of Chemical Refineries"

Solubility is a critical parameter governing the efficacy and stability of agrochemical active ingredients (AIs) formulated in solvent mixtures. Traditionally, the screening of efficient solvent mixtures heavily relies on empirical, trial-and-error experiments. The non-linear behaviours of complex solutes in multi-component solvent systems remain difficult to predict. Furthermore, while predictive machine learning models have shown success in predicting the interaction between multi-component solvents, the application of multi-component system solubility prediction is lacking in the agrochemical field. To address this gap, our study employs a Graph Neural Network (GNN) to model the behaviour of dissolving systems from both intramolecular and intermolecular perspectives. Rather than altering the underlying structural architecture, this work focuses on the domain-specific application and transferability of the model to a novel, high-standard experimental dataset of agrochemical AIs and t... [more]

Scheduling is a fundamental decision-making problem in chemical engineering as well as numerous other sectors, arising in manufacturing, energy systems, and supply chains. Many scheduling problems are NP-hard [1], meaning that even small problems are computationally hard to solve deterministically. As a result, existing exact and heuristic methods face trade-offs between scalability, solution quality, and generalizability. This work addresses these limitations through a hybrid machine learning-optimization framework for the single-machine total tardiness scheduling problem (SMTTP). We introduce the DeepSets-Guided Scheduling Framework (DGSF), a hybrid methodology that integrates data-driven priority learning with structured optimization for single-machine scheduling. First, we propose a geometric instance classification rule that characterizes scheduling instances through aggregate structural parameters, enabling models trained on small instances to generalize to larger instances withi... [more]

Chemical kinetics provides the phenomenological framework for the elucidation of reaction mechanisms, in which ab-initio microkinetic models translate density-functional energetics into catalytic rates. Yet the underlying barriers carry uncertainties of 0.1 to 0.3 eV, and the extent to which a given set of measurements can retrieve them remains largely unquantified. Here, we frame the operando inverse problem as a maximum-likelihood estimation over a differentiable microkinetic model. The pseudo-steady-state surface enters as an algebraic constraint, and parameter sensitivities follow by automatic differentiation through its adjoint, the implicit function theorem applied at the converged root rather than through the solver iterations. These sensitivities propagate the measurement covariance into the parameter covariance, and the resulting Fisher information, the information each experiment carries about each barrier, ranks candidate experiments to establish which measurement determines... [more]

Machine learning (ML) models are widely used as surrogates for complex optimization problems in process systems engineering; however, selecting an appropriate surrogate remains challenging. Current selection approaches often rely on cross-validation, prior experience, or statistical and gradient-based response-surface descriptors. We hypothesize that topological descriptors such as the Euler characteristic curve (ECC), which captures structural changes across thresholds, provide complementary information for structure-aware surrogate model selection. We evaluated this hypothesis using 41 two-dimensional optimization test functions annotated with four landscape characteristics: modality, ruggedness, abruptness, and geometric complexity. For each function, ECCs were first computed on a dense 25*25 (625 samples) grid using sublevel filtration. Summary metrics from ECC, including the number of ECC jumps, cumulative ECC variation, and maximum ECC jump, were extracted from each curve. Thresh... [more]

Designing peptides plays an important role in applications ranging from therapeuticsand biomaterials to diagnostics. However, due to the large combinatorial sequence spaceand the high cost and time required for experimental screening, experimental trial anderror approaches are prohibitively expensive. Furthermore, peptide design is inherentlya multi-objective problem that requires simultaneous optimization of different propertiessuch as biological activity, stability, solubility, and safety. These challenges motivate theuse of computational design strategies. Traditional physics-based and sequence-alignmentmethods often struggle to handle variable length sequences and often rely on structuralinformation that is unavailable for many peptides.1, 2 More recently, deep learning modelssuch as AlphaFold, ESM, and diffusion-based approaches have transformed protein mod-eling3, 4, 5 . However, their large data requirements, high computational cost, and black-boxnature reduce their practicality... [more]

The incorporation of mechanistic, first-principles chemical unit operation models into process modeling frameworks remains computationally challenging. Mechanistic models governed by complex nonlinear systems of ordinary and partial differential equations are intractable for modern deterministic global solvers, particularly within large-scale nonlinear and mixed-integer nonlinear programming (MINLP) formulations. As a result, surrogate modeling approaches have gained increasing attention. However, conventional surrogate models typically rely on strong process-level assumptions, simplified physics, or extensive data generation, which limits their extrapolation capability, physical consistency, and reliability across feasible process operating regions. Physics-informed neural networks (PINNs) offer a promising alternative by embedding governing physics directly into the learning objective loss function, thereby reducing dependence on large supervised training datasets while preserving go... [more]

Instrumented safety systems have driven incident rates down, but also create new challenges: the plants they protect are now far more complex. Two problems follow. First, true process risk hides in combinations of latent failures, process variability, corrosion, and degradation, so catastrophic incidents persist despite layered protection. Second, spurious safety-system trips have risen, eroding availability and driving unplanned downtime. The challenge is to keep the safety benefits while giving operations the information needed to run both safely and reliably. We present a case study in which large language models, grounded in a domain-specific process-safety ontology, extract and cross-link evidence across P&IDs, asset registers, PHAs, management-of-change records, and incident reports. The system surfaces discrepancies between sources and answers operators' questions on demand, at the point of decision, with each answer traceable to its evidence. We report how ontology grounding ch... [more]

Machine learning (ML) is increasingly used for predictive analysis in complex environmental systems. However, its effectiveness is often constrained by spatial heterogeneity and resource limitations that restrict data availability. This study evaluates the feasibility and limitations of ML-based predictive modeling for estimating stormwater heavy metal concentrations and associated public health risks in Camden, New Jersey, a historically industrial city with known contamination challenges. In this work, limited stormwater sampling data are integrated with environmental and anthropogenic predictors, including land use, proximity to industries, vegetation, and elevation for machine learning model development. Multiple regression-based ML models, including linear, ridge, lasso, random forest, and support vector regression, are trained and evaluated. In addition, hierarchical modeling approaches are explored to improve predictive performance. Results highlight challenges in predictive gen... [more]

Biofuel production and upgrading offer a promising pathway for reducing the carbon intensity of liquid fuels. However, biomass-to-biofuel systems are energy intensive and require coordinated supplies of electricity, heat, and hydrogen. In a typical process, biomass is converted to bio-oil through pyrolysis, hydrogen is produced using an electrolyzer, and the resulting bio-oil is upgraded to transportation-grade biofuel. When the pyrolyzer is electrically heated and the upgrading unit uses an electrochemical pathway, the overall process imposes a large and time-varying electrical demand. Supplying this demand with renewable energy is attractive, but the variability of solar and wind generation can lead to renewable curtailment, grid dependence, and operational challenges. These issues motivate the development of integrated microgrid scheduling frameworks that can coordinate renewable generation, storage, grid exchange, and onsite fuel-based power generation. This work develops a renewab... [more]

Converting process flow diagrams into complete simulation models remains a persistent bottleneck in process systems engineering (PSE), requiring significant manual effort and simulator-specific expertise. Although advances in diagram interpretation and automated model generation have been made, these tasks are typically addressed in isolation, limiting the automation of end-to-end workflows. This work introduces Sketch2Simulation, a unified computational framework that automates flowsheet generation directly from raw engineering diagrams using a multi-agent large language model (LLM) architecture. The proposed framework integrates three coordinated layers: (i) Diagram Parsing and Interpretation, (ii) Simulation Model Synthesis, and (iii) Multi-level Validation. In the first layer, multimodal LLM agents extract process semantics, identify unit operations and stream connectivity, and resolve implicit structural features. This information is encoded into a directed graph-based intermediat... [more]

Reinforcement Learning (RL) is a machine learning technique which is capable of generating data and learning from it autonomously by interacting with the environment. RL has been successfully applied for learning and playing various games such as Go, Chess, Atari etc but its application to address process control and optimization problems is not trivial. There is a need for RL implementations in process industry to be safe, fast learning and explainable. A method for achieving such an implementation, for linear systems without disturbance variables, was published by the author in the past. Taking the work further, this paper proposes significant enhancements to the method in terms of ability to address severe process non-linearities, that can't be linearized, and ability to address disturbance variables explicitly in the RL problem formulation. Along with presenting the details on the enhancements, the paper also provides details on actual implementation of the enhanced method for opti... [more]