The electrification of industrial heat is a promising pathway for decarbonization, yet challenges persist in balancing capital costs, operating costs, and emissions reduction. While previous studies have assessed electrification through heat integration and graphical methods, these approaches do not inherently determine the optimal hybrid technology configuration. This study introduces an optimization-based framework that systematically evaluates the cost-optimal allocation of electrified and conventional heating technologies. Formulated as a Mixed-Integer Linear Programming (MILP) model and implemented in Gurobi, the framework minimizes Total Annualized Cost (TAC) while satisfying heat demand, technology constraints, and emissions targets. Applied to an industrial case study, the model compares three scenarios: a fully conventional system relying on steam boilers and fired heaters, a fully electrified system utilizing high-temperature heat pumps, electrode boilers, and electric heater... [more]

This paper presents a novel fault detection method based on key variable forecasting models. The approach integrates future forecasts of key variables into a time window, allowing for early fault detection without modifying the offline training phase of the existing fault detection model. By incorporating predicted data into the detection process, the proposed method significantly improves fault detection rates and reduces detection delays. Experiments using the Continuous Stirred Tank Heater (CSTH) system demonstrate the superiority of our method over traditional approaches, showing the advantages of forecasting in enhancing detection performance. However, our results also highlight the dependency of the method's effectiveness on the quality of the forecasting model, suggesting the need for more advanced time-series forecasting techniques. Additionally, the current point forecasting method may not be sufficient in real-world applications, where probabilistic modeling of key variables... [more]

Large Language Models have demonstrated potential in accelerating scientific discovery, but they face challenges when making inferences in rapidly evolving and niche domains like Process Systems Engineering (PSE). To address this, we propose a Graph-based Retrieval-Augmented Generation (RAG) pipeline specifically designed for PSE papers. Our pipeline includes custom document parsing, knowledge graph construction, and refinement to enhance retrieval accuracy. We evaluate the effectiveness of our approach using an automatically generated benchmark consisting entirely of PSE-related questions. The results show that our pipeline outperforms both non-RAG and vanilla RAG implementations in terms of relevant document retrieval and overall answer quality. Additionally, our implementation is fully customizable, allowing users to select the papers most relevant to their specific tasks. This framework is openly available, providing a flexible solution for those working in PSE or similar domains.

Robust optimisation is a powerful approach for addressing uncertainty ensuring constraint satisfaction for all uncertain parameter realisations. While convex robust optimisation problems are effectively tackled using robust reformulations and cutting plane methods, extending these techniques to non-convex problems remains largely unexplored. In this work we propose a method that is based on a parallel robustness and optimality search. We introduce a novel spatial Branch-and-Bound algorithm integrated with robust cutting-planes for solving non-convex robust optimisation problems. The algorithm systematically incorporates global and robust optimisation techniques, leveraging McCormick relaxations. The proposed algorithm is evaluated on benchmark pooling problems with uncertain feed quality, demonstrating algorithm stability and solution robustness. The computational time for the examined case studies is within the same order of magnitude as state-of-the-art. The findings of this work hig... [more]

This work addresses the process design of Power-to-Ammonia plants (i.e. ammonia from renewable-powered electrolysis) by a novel methodology based on the multi-objective optimization of the “Three pillars of sustainability”: economic, environmental, and social. Specifically, we developed a tool estimating the installed capacities of every main process section typically featured by Power-to-Ammonia facilities (e.g., the renewable power plant, the electrolyzer, energy and hydrogen storage systems, etc.) to maximize the plant’s “Global Sustainability Score”.

Pressure swing adsorption (PSA) is an energy-efficient technology for gas separation, while the multi-objective optimization of PSA is a challenging task. To tackle this, we propose a hybrid optimization framework, which integrates three steps. In the first step, we establish surrogate models for the constraints using Gaussian processes (GPs) and employ multi-objective Bayesian optimization to search for feasible points that satisfy the constraints. In the second step, we establish surrogate models for the objective function and constraints using GPs and utilize constrained multi-objective Bayesian optimization to search for an approximate Pareto front. In the third step, we perform a local search based on the approximate Pareto front. By employing the trust region filter method, we construct quadratic models for each constraint and objective function and refine the Pareto front to achieve local optimality. This framework demonstrates the efficiency of Bayesian optimization and the loc... [more]

Individual-based modeling (IbM) has emerged as a powerful approach for studying microbial populations, offering a bottom-up framework to simulate cellular behaviors and their interactions. Unlike continuum-based models, IbM explicitly captures the heterogeneity and emergent dynamics of microbial communities, making it invaluable for studying spatially structured phenomena such as nutrient competition, biofilm formation, and colony interactions. However, IbM faces significant computational challenges, particularly in resolving spatial overlaps during simulations of large microbial populations. Traditional approaches, such as pairwise comparisons or kd-trees, are computationally expensive and scale poorly with population size. The Discretized Overlap Resolution Algorithm (DORA) introduces a novel grid-based solution to overcome these limitations. By encoding spatial information into an occupancy matrix, DORA achieves a time complexity of O(N), enabling efficient resolution of overlaps wh... [more]

Bayesian classification (BC) is a powerful supervised machine learning method for modelling the relationship between a set of continuous variables and a set of discrete variables that represent classes. BC has been successful in engineering and medical applications, including feasibility analysis and clinical diagnosis. Gaussian process (GP) models are widely used in BC methods to model the probability of assigning a class to an input point, typically through an indirect approach: a GP predicts a continuous function value based on Bayesian inference, which is then transformed into class probabilities using a nonlinear function like a sigmoid. The final class labels are assigned based on these probabilities. In this commonly used workflow, the uncertainty associated with the class prediction is usually evaluated as the uncertainty in the GP function values. A disadvantage of this approach is that it does not consider the uncertainty directly associated with the decision-making. In this... [more]

Currently, state-of-the-art approaches to solving complex optimization problems have focused solely on methods requiring high computational time and unable to find the global optimal solution. In this work, a methodology based on quantum computing is presented to overcome these drawbacks. The novelty of this framework stems from the quantum computer’s architecture and taking into consideration the quantum phenomena that take place to solve optimization problems with specific structure. The proposed methodology includes steps for the transformation of the initial optimization problem into an unconstrainted optimization problem with binary variables and its embedding onto a quantum device. Moreover, different resolution levels for the transformation step and different architectures for the embedding process are utilized. To illustrate the procedure, a case study based on Haverly’s pooling and blending problem is examined while demonstrating the potential of the proposed approach. The res... [more]

Prospective life-cycle design of emerging technologies is important in discussions of decarbonization and resource circulation strategies. This study demonstrates the role of computer-aided process engineering in reflecting technology information with appropriate granularity and accuracy using air conditioning as a case study. Process simulations involving heat exchangers (indoor/outdoor units), compressors, and expansion valves were developed to model air conditioners to quantify changes in performance and heat exchanger size as existing and alternative refrigerants are introduced. The process simulation results were incorporated into a material flow analysis and life cycle assessment to quantify the change in life cycle greenhouse gas (GHG) emissions through 2050 for each refrigerant installed. The results show that operational emissions dominate the life cycle GHG emissions of air conditioners, that decarbonization of electricity can significantly reduce life cycle GHG emissions, wi... [more]

Batch chemical processes face significant challenges due to frequent operational shifts and varying conditions, requiring models to be retrained for each new scenario. This high retraining demand limits the scalability of traditional process monitoring systems, making them unsuitable for dynamic batch operations. To address this, we propose a transfer learning-based framework that enhances adaptability by reusing learned features across different batch conditions, reducing the need for extensive retraining. Proposed method integrates Temporal Convolutional Networks (TCNs) for capturing temporal dependencies in batch data and predicting Quality-Indicative Variables (QIVs) to identify deviations. The core innovation lies in transfer learning, enabling the model to adapt to new process variations with minimal updates. This approach ensures robust, accurate monitoring even under evolving conditions. This framework is validated using the IndPenSim penicillin fermentation dataset, which simu... [more]

This study presents a simple tool to provide decision-makers data that will facilitate informed decisions in selecting utilization for small- to medium-scale utilization of stranded natural gas resources that would otherwise be flared. The methodology involves the simulation of different natural gas utilization technologies on Aspen Plus simulation software and utilizing the results to develop a tool on python that enables the user to assess recoverable valuable products from different natural gas profiles. Ten utilization technologies were implemented and six different natural gas profiles (rich and lean) were used as case studies to ascertain the capabilities of the tool. The results provide the user with the Net Present Values (NPV) of different technologies and the most profitable or infeasible utilization technology. The results also show the potentials of utilizing the gas over flaring. For very small volumes of gas the results favored the compressed natural gas (CNG) with positi... [more]

The growing demand for sustainable alternatives to petroleum-based products drives the development of biomanufacturing using agriculture-based sugars. However, agricultural sugar production faces significant challenges due to limited production capacity and potential negative environmental impacts. This research examines chemical sugar synthesis as an alternative, assessing its environmental impact with conventional agricultural production methods through life cycle assessment. As formaldehyde serves as a primary substrate in chemical synthesis, four production cases were evaluated—comprising two pathways (conventional methods and CO2 capture and utilization (CCU) technologies), each implemented with either fossil fuels or renewable energy sources. The analysis revealed that semi-batch reactors in chemical synthesis substantially reduce environmental impacts compared to batch reactors. Chemical sugar synthesis demonstrated marked advantages in reducing eutrophication, land use change,... [more]

Optimization under uncertainty is inherent to many PSE applications ranging from process design to RTO. Reaching process true optima often involves learning from experimentation, but actual experiments involve a cost (economic, resources, time) and a budget limit usually exists. Finding the best trade-off on cumulative process performance and experimental cost over a finite budget is a Partially Observable Markov Decision Process (POMDP), known to be computationally intractable. This paper follows the nonmyopic Bayesian optimization (BO) approximation to POMDPs developed by the machine-learning community, that naturally enables the use of hybrid plant surrogate models formed by fundamental laws and Gaussian processes (GP). Although nonmyopic BO using GPs may look more tractable, evaluating multi-step decision trees to find the best first-stage candidate action to apply is still expensive with evolutionary or NLP optimizers. Hence, we propose modelling the value function of the first-st... [more]

Processes increasingly need to consider electricity markets, which shifts the traditional demand side management scope towards a more dynamic nature. Instead of only focusing on day-ahead energy trading, demand-side management scope should be broadened towards being able to support the power grid stability during frequency events. This paper studies an artificial example process, similar to the melt-shop process from the steel industry, highlighting the challenges and opportunities of an energy intensive process. We show the potential benefits of having a battery energy storage system on-site, as well as opportunities in lowering the electricity cost by participating in the bidding process of various ancillary products.

Rubber mixing (RM) is a vital batch process producing high-quality composites, which serve as input material for manufacturing different types of final products, such as tires. Due to its complexity, this process faces two main challenges regarding the final quality: i) lack of online measurement and ii) limited comprehension of the influence of the different factors involved in the process. While data-driven and machine learning (ML) based soft-sensing methods have been widely applied to address the first challenge, the second challenge, to the best of the author's knowledge, has not yet been addressed in the rubber industry. This work presents a data-driven method for extracting knowledge and providing explainability in the quality prediction in RM processes. The method centers on an XGBoost model while leveraging high-dimensional data collected over extended time periods from one of Michelin’s complex mixing processes. First, a recursive feature elimination-based procedure is used f... [more]

Solvent selection is a critical decision-making process that balances economic, environmental, and societal factors. The vast chemical space makes evaluating all potential solvents impractical, necessitating pre-selection strategies to identify promising candidates. Predictive thermodynamic models, such as the UNIFAC model, are commonly used for this purpose. Recent advancements in deep learning have led to models like the Gibbs-Helmholtz Graph Neural Network (GH-GNN), which overall offers higher accuracy in predicting infinite dilution activity coefficients over a broader chemical space than UNIFAC. This study presents a systematic comparison of solvent pre-selection using GH-GNN and UNIFAC-IL in the context of liquid-liquid extraction. The original GH-GNN model is extended to simultaneously predict organic and ionic systems. This extended GH-GNN model predicts more than 92 % of the logarithmic IDACs with an absolute error of less than 0.3. By comparison, UNIFAC-based models only achi... [more]

Recent advancements in material design have made adsorption a more energy-efficient alternative to traditional thermally driven separation processes. Accurate modelling of adsorption thermodynamics is crucial for designing and operating equilibrium-limited adsorption systems. High-quality open-source packages like PyIAST, PyGAPsare available for processing adsorption data in Python. They provide a robust set of features for processing and analysing isotherms. However, they have no support for automatic differentiation and are not targeted for performance. Langmuir.jl addresses these limitations by leveraging Julia's composable and differentiable programming ecosystem. Langmuir.jl includes tools for processing adsorption thermodynamics data—loading data, fitting isotherms with most often used models, predictive multicomponent adsorption through Ideal Adsorption Solution Theory (IAST) — and, importantly, enabling accurate derivative calculations through Julia's automatic differentiation... [more]

This study introduces a novel methodology that combines Gaussian process regression (GPR) with alpha shape design space reconstruction to visualize multi-dimensional design spaces. The proposed GPR surrogate approach incorporates a kernel optimization step, employing a greedy tree search strategy to identify the optimal combinatorial kernel from a selection of base kernels. This approach efficiently evaluates design spaces around specific points of interest, enabling alpha shape reconstruction. The methodology's adaptability is demonstrated through its application to both lower-dimensional (2D and 3D) cases and more complex, higher-dimensional systems (up to 7D), showcasing its scalability and versatility. Its effectiveness is further validated by its ability to generate accurate surrogate models from limited data. Overall, this study presents a robust framework that leverages GPR surrogate modeling and alpha shape reconstruction to facilitate design space evaluation in complex, multid... [more]

Second-order optimization algorithms that leverage the exact Hessian or its approximation have been proven to achieve a faster convergence rate than first-order methods. However, their applications on training deep neural networks models, partial differential equations-based optimization problems, and large-scale non-convex problems, are hindered due to high computational cost associated with the Hessian evaluation, Hessian inversion to find the search direction, and ensuring its positive-definiteness. Accordingly, we propose a new search direction based on an interval Hessian and incorporate it into a line-search framework. We apply our algorithm to a set of 210 problems and show that it converges to a local minimum for 70% of the problems. We also compare our algorithm with other approaches. We illustrate that our algorithm is competitive to other methods in finding a local minimum using a smaller number of O(n3) operations.

In the domain of process systems engineering, developing software embedded with advanced computational methods is in great demand to enhance the kinetic comprehension and facilitate industrial applications. Polymer production, characterized by complex reaction mechanisms, represents a particularly intricate process industry. In this work, a scientific software is developed for polymerization modelling and simulation with insight on microscopic resolution. From a software architecture perspective, the software is built on a self-developed process modelling platform that allows flexible user customization. A specific design for polymer species with microscopic chain structure information is conducted. From an algorithm perspective, the software offers high-performance solution strategies for polymerization process modelling by utilizing advanced computation approaches. A Ziegler-Natta copolymerization is presented to demonstrate the software’s capability in capturing the microscopic stru... [more]

Anomaly detection is a key technique for maintaining process suitability and safety; however, the quality of process data often deteriorates due to missing or noisy values caused by sensor malfunctions. Such data imperfections may obscure real faults. If anomaly detection models are too sensitive to such abnormal data, they may cause false positives resulting in unnecessary alarms, which may obstruct detection of true process faults. Thus, deterioration of the quality of process data may affect process performance and safety. We propose a new anomaly detection method that utilizes graph Laplacian regularization as a loss function considering data-specific temporal relationships. Graph Laplacian regularization is a mathematical tool used in image processing and denoising to smooth data. We assume that successive process data temporally close to each other have similar values and maintain temporal dependencies among variables. In this study, Laplacian regularization imposes significant p... [more]

One of the most successful continuous high-performance liquid chromatography (HPLC) processes for drug manufacturing is the Simulated Moving Bed (SMB). SMB is a multi-column, continuous, chromatographic process that can handle much higher throughputs than regular batch chromatographic processes. The process is initially transient, but eventually arrives at a cyclic steady state, which makes optimization very challenging, even more so when superstructure optimization is involved. To simplify the optimization problem, many researchers fixed the SMB structure, optimizing only the continuous variables, so they cannot be considered superstructure optimization. In this work, an SMB superstructure that can simultaneously optimize column structure and operation is proposed. The results showed that the superstructure proposed is reliable, and it is more efficient compared to current optimization approaches if the optimal column structure has to be identified.

Heuristic decomposition schemes like moving horizon schemes are a common approach to approximately solve large-scale mixed-integer programs. The authors propose Bayesian optimization as a methodological approach to systematically tune parameters of decomposition schemes for mixed-integer programs. This paper discusses detailed results of three studies of the Bayesian optimization-based approach using hoist scheduling as a case study: Firstly, two objectives of the tuning problem are examined considering sequences of incumbent solutions found by the Bayesian optimization. Secondly, the Bayesian optimization is applied to a set of test instances of the hoist scheduling problem using four types of acquisition functions; they are compared with respect to the convergence of the tuning problem solutions. Thirdly, the scaling behaviour of the Bayesian optimization is studied with respect to the dimension of the space of tuning parameters. The results of the three studies show that the solutio... [more]

A new method for efficient process development is the direct scale-up from laboratory scale to production scale using mechanistic models [1]. The integration of the Quality by Design approach into this scale-up concept may prove beneficial for a variety of product- and process-related aspects. This paper presents a workflow for the digital twin-supported direct scale-up of processes and process plants, which integrates elements of the Quality by Design methodology. To illustrate the concept, the workflow is implemented for the example of an esterification reaction in a stirred tank reactor. Finally, benefits of the implementation of Quality by Design in the direct scale-up using digital twins regarding the product quality and the process development are discussed as well as its limitations.