Reinforcement learning has shown some success in automating process design by integrating data-driven models that interact with process simulators to learn to build process flowsheets iteratively. However, one major challenge in the learning process is that the reinforcement learning agent demands numerous process simulations in rigorous process simulators, thereby requiring long simulation times and expensive computational power. We propose employing transfer learning to enhance the reinforcement learning process in process design. This study examines two transfer learning strategies: (i) transferring knowledge from shortcut process simulators to rigorous simulators, and (ii) transferring knowledge from process variational autoencoders (VAEs). Our findings reveal that appropriate transfer learning can significantly improve both learning efficiency and convergence scores. However, transfer learning can also negatively impact the learning process when there are substantial discrepancies in decision range and reward function. This suggests that pre-trained process data should match the complexity of the fine-tuning task.

The chemical industry needs to undergo a significant transformation towards more sustainable and circular production systems. To guide this transformation, estimating the environmental impacts of chemical production at early product screening or development stages is highly desirable. This study leverages the molecular structure of the process products with graph neural networks (GNNs) for early-stage environmental impact approximation of chemical processes. Specifically, we use end-to-end GNN models to predict fifteen environmental impact categories, utilizing a CarbonMinds dataset of 51,905 processes producing 791 molecules produced in 91 countries, augmented with country-specific energy mix data. Our analysis begins with a comparison of Quantitative Structure-Property Relationship (QSPR) and GNN models for the climate change impact category. Specifically, we develop three different GNN models: (i) GNN with only molecular structure, (ii) GNN with molecular structure and additional geographical features, and (iii) GNN with molecular structure and additional energy mix features. The results indicate that the three GNN models show an improvement over the QSPR models. Furthermore, benchmarking our GNN models against the existing literature in the climate change impact category reveals that our models perform comparably. We then extend our approach by developing both single- and multi-task GNN models to predict all fifteen impact categories. The findings indicate that multi-task learning can improve model performance in complex environmental impact predictions compared to single-task GNNs. Therefore, we recommend using a multi-task GNN for predicting multiple impact categories, with single-task models applied to fine-tune performance on underperforming categories. Although our proposed approach shows improvements over previous models, the prediction of environmental impacts solely based on molecular information remains a rough approximation.

The term hybrid modeling refers to the combination of parametric models (typically derived from knowledge about the system) and nonparametric models (typically deduced from data). Despite more than 20 years of research, over 150 scientific publications (Agharafeie et al., 2023), and some recent industrial applications on this topic, the capabilities of hybrid models often seem underrated, misunderstood, and disregarded by other disciplines as “simply combining some models” or maybe it has gone unnoticed at all. In fact, hybrid modeling could become an enabling technology in various areas of research and industry, such as systems and synthetic biology, personalized medicine, material design, or the process industries. Thus, a systematic investigation of the hybrid model properties is warranted to scoop the full potential of machine learning, reduce experimental effort, and increase the domain in which models can predict reliably.

Machine Learning (ML) techniques are encountered nowadays across disciplines, from social sciences, through natural sciences to engineering. However, teaching ML is a daunting task. Aside from the methodological complexity of ML algorithms, both with respect to theory and implementation, the interdisciplinary and empirical nature of the field need to be taken into consideration. This paper introduces the MachineLearnAthon format, an innovative didactic concept designed to be inclusive for students of different disciplines with heterogeneous levels of mathematics, programming, and domain expertise. The format is grounded in a systematic literature review and the didactic principles action orientation, constructivism, and problem orientation. At the heart of the concept lie ML challenges, which make use of industrial data sets to solve real-world problems. Micro-lectures enable students to learn about ML concepts and algorithms, and associated risks. They cover the entire ML pipeline, promoting data literacy and practical skills, from data preparation, through deployment, to evaluation.

ChatGPT is a powerful language model from OpenAI that is arguably able to comprehend and generate text. ChatGPT is expected to greatly impact society, research, and education. An essential step to understand ChatGPT’s expected impact is to study its domain-specific answering capabilities. Here, we perform a systematic empirical assessment of its abilities to answer questions across the natural science and engineering domains. We collected 594 questions on natural science and engineering topics from 198 faculty members across five faculties at Delft University of Technology. After collecting the answers from ChatGPT, the participants assessed the quality of the answers using a systematic scheme. Our results show that the answers from ChatGPT are, on average, perceived as “mostly correct”. Two major trends are that the rating of the ChatGPT answers significantly decreases (i) as the educational level of the question increases and (ii) as we evaluate skills beyond scientific knowledge, e.g., critical attitude.

The transformation toward renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches. Recently, deep reinforcement learning (RL), a subclass of machine learning, has shown the potential to solve complex decision-making problems and aid sustainable process design. However, its suitability in static process design still needs to be examined. We discuss the advantages and disadvantages of RL for process design. Then, we survey state-of-the-art research through three major elements: (1) information representation, (2) agent architecture, and (3) environment and reward. Moreover, we discuss perspectives on underlying challenges and promising future works to unfold the full potential of RL for process design in chemical engineering.

This “white paper” is a concise perspective of the potential of machine learning in the process systems engineering (PSE) domain, based on a session during FIPSE 5, held in Crete, Greece, June 27–29, 2022. The session included two invited talks and three short contributed presentations followed by extensive discussions. This paper does not intend to provide a comprehensive review on the subject or a detailed exposition of the discussions; instead its aim is to distill the main points of the discussions and talks, and in doing so, highlight open problems and directions for future research. The general conclusion from the session was that machine learning can have a transformational impact on the PSE domain enabling new discoveries and innovations, but research is needed to develop domain-specific techniques for problems in molecular/material design, data analytics, optimization, and control.

The estimation of polymer properties is of crucial importance in many domains such as energy, healthcare, and packaging. Recently, graph neural networks (GNNs) have shown promising results for the prediction of polymer properties based on supervised learning. However, the training of GNNs in a supervised learning task demands a huge amount of polymer property data that is time-consuming and computationally/experimentally expensive to obtain. Self-supervised learning offers great potential to reduce this data demand through pre-training the GNNs on polymer structure data only. These pre-trained GNNs can then be fine-tuned on the supervised property prediction task using a much smaller labeled dataset. We propose to leverage self-supervised learning techniques in GNNs for the prediction of polymer properties. We employ a recent polymer graph representation that includes essential features of polymers, such as monomer combinations, stochastic chain architecture, and monomer stoichiometry, and process the polymer graphs through a tailored GNN architecture. We investigate three self-supervised learning setups: (i) node- and edge-level pre-training, (ii) graph-level pre-training, and (iii) ensembled node-, edge- & graph-level pre-training. We additionally explore three different transfer strategies of fully connected layers with the GNN architecture. Our results indicate that the ensemble node-, edge- & graph-level self-supervised learning with all layers transferred depicts the best performance across dataset size. In scarce data scenarios, it decreases the root mean square errors by 28.39% and 19.09% for the prediction of electron affinity and ionization potential compared to supervised learning without the pre-training task.

Microgels are cross-linked, colloidal polymer networks with great potential for stimuli-response release in drug-delivery applications, as their small size allows them to pass human cell boundaries. For applications with specified requirements regarding size, producing tailored microgels in a continuous flow reactor is advantageous because the microgel properties can be controlled tightly. However, no fully-specified mechanistic models are available for continuous microgel synthesis, as the physical properties of the included components are only studied partly. To address this gap and accelerate tailor-made microgel development, we propose a data-driven optimization in a hardware-in-the-loop approach to efficiently synthesize microgels with defined sizes. We optimize the synthesis regarding conflicting objectives (maximum production efficiency, minimum energy consumption, and the desired microgel radius) by applying Bayesian optimization via the solver “Thompson sampling efficient multi-objective optimization” (TS-EMO). We validate the optimization using the deterministic global solver “McCormick-based Algorithm for mixed-integer Nonlinear Global Optimization” (MAiNGO) and verify three computed Pareto optimal solutions via experiments. The proposed framework can be applied to other desired microgel properties and reactor setups and has the potential of efficient development by minimizing number of experiments and modeling effort needed.

Co-design of alternative fuels and future spark-ignition (SI) engines allows very high engine efficiencies to be achieved. To tailor the fuel’s molecular structure to the needs of SI engines with very high compression ratios, computer-aided molecular design (CAMD) of renewable fuels has received considerable attention over the past decade. To date, CAMD for fuels is typically performed by computationally screening the physicochemical properties of single molecules against property targets. However, achievable SI engine efficiency is the result of the combined effect of various fuel properties, and molecules should not be discarded because of individual unfavorable properties that can be compensated for. Therefore, we present an optimization-based fuel design method directly targeting SI engine efficiency as the objective function. Specifically, we employ an empirical model to assess the achievable relative engine efficiency increase compared to conventional RON95 gasoline for each candidate fuel as a function of fuel properties. For this purpose, we integrate the automated prediction of various fuel properties into the fuel design method: Thermodynamic properties are calculated by COSMO-RS; combustion properties, indicators for environment, health and safety, and synthesizability are predicted using machine learning models. The method is applied to design pure-component fuels and binary ethanol-containing fuel blends. The optimal pure-component fuel tert-butyl formate is predicted to yield a relative efficiency increase of approximately 8% and the optimal fuel blend with ethanol and 3,4-dimethyl-3-propan-2-yl-1-pentene of 19%.

An educational workshop for developing Process Systems Engineering (PSE) courses will be held during ESCAPE-33, following the model workshop that was run during the CAPE Forum 2022 held at the University of Twente, in the Netherlands. This 3-hour workshop distributes the participants into four teams working together to develop the outline of a course on a novel application area in PSE motivated by a selected plenary or keynote talk at the conference, with each team led by authors of this contribution. This paper provides an overview of the approach used in the workshop for the effective development of a PSE course.

We propose a novel method enabling autocompletion of chemical flowsheets. This idea is inspired by the autocompletion of text. We represent flowsheets as strings using the text-based SFILES 2.0 notation and learn the grammatical structure of the SFILES 2.0 language and common patterns in flowsheets using a transformer-based language model. We pre-train our model on synthetically generated flowsheet topologies to learn the flowsheet language grammar. Then, we fine-tune our model in a transfer learning step on real flowsheet topologies. Finally, we use the trained model for causal language modeling to autocomplete flowsheets. Eventually, the proposed method can provide chemical engineers with recommendations during interactive flowsheet synthesis. The results demonstrate a high potential of this approach for future AI-assisted process synthesis but also reveal the limitations at the present state and the next steps that need to be taken to deploy this technique in realistic flowsheet synthesis scenarios.