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]

Centralized monitoring methods experience reduced fault detection sensitivity in large-scale industrial systems due to the masking effect arising from the aggregation of many interconnected variables. Decentralized monitoring, where variables are grouped into subsystems, has been shown to effectively address these limitations. However, the performance of this class of methods critically depends on how the network is partitioned, and the role of its structural factors on fault detection remains poorly understood. This work studies how network topology and causal structure affect decentralized monitoring in high-dimensional systems. Using SimCaNet, a DAG-based data simulator, where large-scale systems with 100-1000 variables were generated, we rigorously compared the performance of centralized and decentralized causal log-likelihood monitoring methods under process perturbations and sensor bias faults. Network partitioning is performed using the Leiden community detection algorithm and c... [more]

Digital competency is now core to chemical engineering practice, yet the extent and coherence of digitalisation skills provision across undergraduate curricula remain uneven. This study maps qualitatively and qualitatively digital learning outcomes across undergraduate chemical engineering programmes at the University of Sheffield, against a digital skills framework (data analysis, process simulation, process automation & control, reproducible workflows, programming, data governance). In recent years, digital skills education within chemical engineering education has advanced considerably, driven by the broader industrial shift toward Industry 4.0 and reinforced by the global challenges. Academic institutions have begun to integrate digitalisation-related content more deliberately within syllabus, in alignment with degree programme accreditation requirements and industry needs. Beyond introductory spreadsheet manipulation and basic programming, many courses are now embedding more advan... [more]

The rapid shift toward Industry 4.0 and data driven manufacturing has prompted universities to reshape chemical engineering programmes, yet the scope and coherence of "Digital Chemical Engineering" (DCE) within UK curricula remain unclear. This study qualitatively maps DCE provision across five IChemE accredited MEng degrees to identify which digital skills are taught, how they progress across programme stages, how skills are distributed between core and elective content or taught versus applied learning, and how well provision aligns with accrediting frameworks. The analysis is structured around eight domains: (D1) programming and computation; (D2) data literacy and statistics; (D3) process modelling and simulation; (D4) optimisation and process systems engineering; (D5) control, automation and instrumentation; (D6) AI, machine learning and digital twins; (D7) software engineering practices; and (D8) data governance, ethics and cybersecurity. Results show institution dependent digital... [more]

With the ongoing advances in generative artificial intelligence (GenAI), the initial skepticism surrounding its tools is gradually diminishing. In fact, tools such as ChatGPT, Copilot and similar, are often used in everyday tasks, both in our personal lives and in educational contexts. Educators may use them for content creation, grading exams, or automating repetitive tasks. Students resort to them to better understand a topic, get feedback on an assignment and brainstorm ideas. Research has shown that, if used correctly, these tools can spur and support both teaching and learning. However, these continuous advancements and the increasing number of available tools also require more research to benchmark all these models and, if possible, provide quantifiable indications of which tool is better to use for which specific subtopic. As such, we created FermBench, a dataset of fermentation knowledge, which can be used to benchmark various large language models (LLMs). The models selected f... [more]

Modern semiconductor manufacturing requires extreme precision as yield margins narrow in the "More-than-Moore" era. While physics-based models (PBMs) provide high-fidelity insights into plasma etching, their computational intensity-often requiring hours per simulation-renders them impractical for direct iterative optimization. This work demonstrates a hybrid framework that utilizes data-driven surrogate models to enable rapid, cost-effective process optimization. A 2D axisymmetric fluid model of an inductively coupled O2 plasma (ICP) reactor was developed to generate a training dataset for two neural architectures: a Multi-Layer Perceptron (MLP) and a Kolmogorov-Arnold Network (KAN). These surrogates predict radial etching rates across a wide operating window of power, pressure, gas flow, and bias voltage. By replacing the expensive PBM with these high-speed surrogates, derivative-free optimization algorithms (Nelder-Mead and Powell) successfully identified a profit-maximizing operatin... [more]

Pharmaceutical and fine chemical industries face strong pressure to shorten time-to-market while maintaining compliance with complex regulatory frameworks. These conflicting demands require rapid process design, validation, and scale-up. Modular production plants standardized in VDI 2776 and VDI/VDE/NAMUR 2658 have emerged as a promising strategy to shorten engineering and validation efforts. The Product-Process-Resource (PPR) philosophy represents a key approach to efficient data management in modular plant engineering. It enables the separation of different flexibility dimensions into distinct, relevant aspects that can ideally be exchanged or modified independently. To realize this principle in practical applications, formalized information models and ontologies serve as a key enabler for structuring and managing semantic data. This work investigates several information models and ontologies for the process engineering domain regarding their suitability to support separation accordi... [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]

Copper refining via electrolysis is a core metallurgical process that takes place in tankhouses, subject to strict temporal, spatial, and operational constraints. The efficiency and stability of this process depend critically on the coordinated scheduling of crane operations responsible for handling anodes, cathodes, and auxiliary tasks. In industrial practice, crane scheduling must simultaneously satisfy long-term production targets and short-term operational feasibility, while respecting process-dependent timing constraints imposed by electrochemical parameters. Inefficient or inconsistent schedules can lead to process delays, suboptimal resource utilization, and degraded electrolysis performance, ultimately affecting product quality and operational stability. This paper presents a modeling approach for optimizing tankhouse operations. The uniqueness of this case lies in the broad range of constraints, including human capacity, energy restrictions, metallurgical rules, and logistical... [more]

Despite the growing economic importance of the fragrance industry, perfume formulation remains largely guided by empirical knowledge and iterative trial-and-error approaches. The structured design of fragrances, typically organised into top, middle, and base notes through the blending of perfume raw materials, is therefore time-consuming, costly, and difficult to generalise. Existing computational approaches have begun to address this challenge, but are commonly limited to small ingredient sets or require extensive sensory data that are not always available. This work proposes a computer-aided optimisation framework for Eau de Parfum formulation that simultaneously maximises perceived olfactory intensity and minimises formulation cost. The resulting optimisation problem is formulated to preserve the structural balance of top, middle, and base notes inherent to the perfume pyramid. Application of the framework to a fruity-floral Eau de Parfum formulation demonstrates a substantial incre... [more]

Near-infrared (NIR) sorting systems in waste sorting plants operate under multiple settings, creating distinct input-output relationships that challenge predictive modeling. Conventional neural networks, such as multilayer perceptron (MLP), often suffer from catastrophic forgetting under continual training, limiting reliability across settings. This study evaluates Kolmogorov-Arnold Networks (KAN) for continual regression modeling of multi-setting NIR systems. KAN assign nonlinear transformations to network edges using localized spline grids, enabling structural isolation between input regions. We introduce controlled input-space manipulations (shifting successive settings to adjacent or non-overlapping grid regions) and compare KAN performance with MLPs of comparable parameter count. We also examine single-input versus multi-input configurations to assess dimensionality effects. Results show that KANs with sufficient input-space separation maintain previously learned knowledge with pe... [more]

The pharmaceutical industry is placing growing emphasis on sophisticated process modeling to enhance the efficiency of drug design and production pipelines. Optimal control over these models can significantly improve manufacturing performance by lowering costs, boosting productivity, and ensuring rigorous quality compliance. However, the intricate nature and heavy computational load of these models often require the adoption of more practical or simplified alternative strategies for optimisation such as simulation-based approaches. In this work, we introduce a simulation-based framework including a "top-level" gradient-based mathematical programming optimisation model coupled with a "low-level" simulation scheme, to optimise multi-scale drug substance manufacturing flowsheets. The proposed framework optimises critical quality attributes, such as yield and purity, including green metrics such as process mass intensity, aligning with digital platforms (e.g. gPROMS) used in the pharmaceut... [more]

The global transition toward sustainable energy has intensified research into biofuels, with bioprocess optimization playing a central role in achieving decarbonization goals. Biobutanol, in particular, is a high-value molecule for sustainable fuel applications due to its superior energy density and compatibility with existing infrastructure. However, model-based optimization of its production is hindered by traditional semi-structured kinetic models that often suffer from limited predictive robustness. To address this challenge, within this study we developed a hybrid modeling framework for Clostridium saccharoperbutylacetonicum that integrates mechanistic mass-balance equations with Gaussian Processes (GPs) aiming to describe the biobutanol formation rate. Here, we investigate the effect of data normalization techniques on hybrid model's prediction capabilities comparing min-max normalization, z-score normalization, and no transformation. For each data treatment strategy, 8, 000 hybr... [more]

In pharmaceutical process engineering, accurate prediction of tablet properties is crucial for ensuring product quality, optimizing manufacturing efficiency, and advancing sustainable production practices. This study presents a physics-informed neural network (PINN) framework for predicting the physical properties of pharmaceutical tablets from near-infrared (NIR) spectra. The PINN framework integrates revised Kubelka-Munk theory and physical constraints to ensure physically consistent predictions while requiring less training data than conventional artificial neural networks. Tablets were manufactured using acetaminophen and microcrystalline cellulose formulations with varying compositions and compression settings. The PINN framework successfully predicts critical quality attributes, including tensile strength, porosity, and density. It offers a data-efficient, interpretable solution for pharmaceutical tablet quality control.

Mathematical models are increasingly used in pharmaceutical process development within quality-by-design (QbD) frameworks to reduce experimental effort and enable rational process design. However, model validation is still often based on deterministic performance indicators, which do not explicitly account for experimental variability, measurement noise, and model uncertainty. This work proposes an uncertainty-aware framework for model validation in pharmaceutical processes that quantifies predictive reliability in probabilistic terms, consistent with regulatory concepts. The framework explicitly integrates uncertainty in operating conditions, measurements, and model parameters, and evaluates model performance based on the probability that prediction satisfy predefined acceptance criteria rather than on single-point accuracy indicators. An in-silico case study of crystallization was performed to demonstrate the approach, where synthetic experimental data with controlled uncertainty wer... [more]

Maintenance is critical for industrial plants to ensure operational reliability and worker safety. In process industries, fouling, the accumulation of solid residues in equipment, poses a significant challenge, causing inefficiencies and productivity losses. Effective modeling of fouling evolution over time is essential for maintenance planning to prevent equipment from operating under suboptimal conditions. Traditional approaches to fouling prediction include equation-based models, which offer high precision but may struggle with continuously changing process bound-aries, and machine learning techniques, which are more adaptable but less effective at capturing rapidly evolving trends driven by complex underlying physics. This study introduces an innova-tive hybrid machine learning approach for predictive maintenance, combining the strengths of both methods. Pressure differential is modeled using an equation-based approach that links pressure data with fouling thickness, while the foul... [more]

Tableting is a dry granulation process for compacting powder blends into tablets. In this process, a blend of active pharmaceutical ingredients (APIs) and excipients are fed into the hopper of a rotary tablet press via feeders. Inside the tablet press, rotating feed frame paddle wheels fill powder into dies, with tablet mass adjusted by the lower punch position during the die filling process. Pre-compression rolls press air out of the die, while main compression rolls apply the force necessary for compacting the powder into tablets. In this paper, process variables such as feeder screw speeds, feed frame impeller speed, lower punch position during die filling, and punch distance during main compression have been systematically varied. Corresponding responses, including pre-compression force, ejection force, and tablet porosity have been evaluated to optimize the tableting process. After implementing an open platform communications unified architecture (OPC UA) interface, process variab... [more]

Understanding the complex dynamics of continuous processes in pharmaceutical manufacturing is essential to ensure product quality across the production line. This paper presents a data-driven modeling approach using Sparse Identification of Nonlinear Dynamics with Control (SINDYc) to capture the dynamics of a continuous direct compression (CDC) tableting line. By incorporating delayed control inputs into the candidate function library, the model effectively captures deviations from steady state in response to dynamic changes. The proposed model was developed by finding a balance between accuracy and sparsity, with focus on the ability to generalize to a wide range of operating conditions.

The paradigm shift into an era of Industry 4.0, also referred to as the fourth Industrial Revolution, has emphasized the need for intelligent networking between process equipment and industrial processes themselves. This has brought on an age of research and framework development for smart manufacturing in the name of Industry 4.0 [1]. While the physical and digital advancements towards smart manufacturing integration are substantial the inclusion of engineers themselves amongst this shift is often less considered [2]. There are educational efforts in Europe to create and implement smart manufacturing curriculum for non-traditional or adult learners already integrated in the workforce, but attention is also needed on a next generation smart manufacturing curriculum for pre-career students [3]. We, the teaching team of CHE 554: Smart Manufacturing at Purdue University, developed and implemented a curriculum geared towards the training of undergraduate, graduate, and non-traditional stud... [more]

Process control can be one of the most exciting and engaging chemical engineering undergraduate courses! This paper describes our experience transforming Chemical Process Control into Data Analytics, Optimization, and Control at the University of Notre Dame (second semester required course in the junior year). Our modern course is built around six hands-on experiments in which students practice data-centric modeling and analysis using the Arduino-based Temperature Control Lab (TCLab) hardware. We argue that state-space dynamic modeling and optimization are more critical for educating modern chemical engineers than topics such as frequency domain analysis and controller synthesis emphasized in many classical undergraduate control courses. All the course material is available online at https://ndcbe.github.io/controls.

Prompt engineering – improving the command given to a large language model (LLM) – is becoming increasingly useful in order to maximize the performance of the model and therefore the quality of the output. However, in certain instances, the user is not able to enrich the prompt with additional and personalized details, such as the preferred tone and length of generated response. Therefore, it is useful to create models that learn these preferences and implement them directly in the prompt. Current state-of-the-art inductive logic programming (ILP) systems can play an important role in the development and advancement of digitalization strategies. For example, they can be used to learn personal preferences of users without sacrificing human interpretability of the learned outcomes. These systems have recently witnessed the development of data efficient, robust, and human interpretable algorithms and systems for learning predictive models from data and background knowledge. In this paper,... [more]

Current progress in digitalization has led to a wide interest in learning more from available data. Advanced data analytics can be achieved through commercially available software; however, learning to program allows for more flexibility and, ultimately, more freedom in the potentially tailor-suited investigation. Among other programming languages, Python is one of the most requested, in industry and research alike. To intensify the earlier efforts and create both a pedagogical framework to teach programming to (bio)chemical engineers, and provide students with the opportunity to ask questions, we explore the integration of sPyCE and FermentAI into BioVL, a virtual laboratory for teaching (bio)processes, previously implemented by the authors. sPyCE is an open-source series of Python courses tailored to (bio)chemical engineers, FermentAI is a chatbot trained to answer questions about fermentation processes. The main goal of this work is to enable students to (i) learn (bio)processes and... [more]

Digital twins and digital shadows are frequently used terms by industry and academia to describe data-centric models that accurately depict a physical system intended for process monitoring and control. Processes restricted by a low degree of automation rely greatly on operator competencies in key decision-making; a digital shadow can here assist as a guidance tool [1-4]. This work presents a practical implementation of a digital shadow to support operators running a pilot scale-packed batch distillation column at the Technical University of Denmark (DTU) primarily used in education and teaching activities [5]. This operation is selected due to inherent unsteady process dynamics that are controlled by a set of manual valves, which the student operator must continuously balance to meet purity constraints without disrupting the operation. This realisation employ a modular software architecture, separated into four distinct modules compiled into Docker images and independently deployed. T... [more]

Developing ultra- or high-performance liquid chromatography (HPLC) methods for analysis or purification requires significant amounts of material and manpower, and typically involves time-consuming iterative lab-based workflows. This work demonstrates in two case studies that an autonomous HPLC platform coupled with a mechanistic model that self-corrects itself by performing parameter estimation can efficiently develop an optimized HPLC method with minimal experiments (i.e., reduced experimental costs and burden) and manual intervention (i.e., reduced manpower). At the same time, this HPLC platform, referred to as Smart HPLC Robot, can deliver a calibrated mechanistic model that provides valuable insights into method robustness.

This paper introduces a prototype for constructing an edge AI system utilizing the contemporary Machine Learning Operations (MLOps) concept. By employing microcontrollers such as the Raspberry Pi as hardware, our methodology includes data scrubbing and machine learning model deployment on edge devices. Crucially, the MLOps pipeline is fully developed within the ecoKI platform, a research platform for ML/AI applications. In this study, we thoroughly investigate the performance of our ecoKI platform by comparing it with the established Edge Impulse platform. We deployed the ML model with different weight quantization methods, such as FP32 and INT8, to compare accuracy variations and inference speed between these two platforms and quantization strategies on edge devices. In our experiments, we identified that the average accuracy performance of the ecoKI platform is 3.61% better than the edge impulse. Moreover, real-time AI processing on edge devices enables microcontrollers, even those w... [more]