Due to the importance of unsaturated fatty acids for human health and the increasing global demand in the food and food crop area, oleaginous yeasts are promising alternative microorganisms for commercial lipid production due to the high volumetric productivity, with Metchnikowia pulcherrima being an underexplored oleaginous yeast with potential as a lipid producer. Critical to achieving high productivity lipid production are nutrient factors. A sensitivity test identified carbon and nitrogen sources as important factors in nitrogen limited broth (NLB) for lipid production in M. pulcherrima i.e. glucose, yeast extract and Ammonium sulphate. Response Surface Methodology (RSM) involving sets of 15 experimental runs of three-factor three-level Box-Behnken Design (BBD) was implemented for exploring the design of the carbon and nitrogen source in the growth media composition. Quadratic surfaces were least-square fitted and used to identify regions of optimal lipid yield. Multiple sets of ru... [more]

Incorporating process knowledge from various sources often presents challenges in process development, optimization, and control. To utilize available knowledge, linking existing process mo-dels is a viable approach. This work introduces a methodology using latent variable models, specifically sequential and orthogonalized partial least squares (SO-PLS), to capture and quantify the contribution of first-principles knowledge in process models. Applied to a continuously stirred tank reactor (CSTR) case study, the methodology demonstrates how available knowledge can be quantified and how structural and parametric errors in first-principles are addressed using measured data. The methodology is discussed in relation to root cause analysis in bioprocesses.

Pharmacokinetic and pharmacodynamic (PK/PD) models are used to predict drug transport in the body and to assess treatment efficacy and optimal dosage. The kinetic parameters embedded in the models, which define transport across body compartments or drug efficacy, can be linked to patient-specific characteristics; understanding the parameter space-model output relationship is critical towards linking patient population heterogeneity to the therapeutic outcome variability. Global Sensitivity Analysis (GSA) is a well-established tool used to examine parameter-to-parameter interactions, shedding light on underlying interactions towards enhanced system understanding. Despite its potential and usefulness, GSA performance is dependent to the model complexity; large-scale and nonlinear PK/PD models, which often have large sets of parameters, can render GSA challenging to perform, requiring excessive computational effort. Proposed approaches to reduce GSA complexity, such as segmentation in par... [more]

In recent years, burgeoning interest in products derived from microbial production across various sectors has significantly propelled the evolution of the field of metabolic engineering. As a Gram-negative bacterium, Vibrio natriegens is characterized by its fast growth, robust metabolic capabilities, and a broad substrate spectrum, making it a promising candidate as a standard biological host for the industrial bioproduction of metabolites. Genome-scale metabolic models (GSMMs) are mathematical representations constructed based on genome annotations and gene-protein-reaction (GPR) associations within a cell. These models enable the computational simulation of intracellular reaction flux distributions. In this study, we developed a hybrid method based on metaheuristic algorithms and the GSMM to optimize metabolism for the production of ethanol and 1,3-propanediol (1,3-PDO) as target products in Vibrio natriegens. The modified GSMM used in this study contains 1195 reactions, 1094 metabo... [more]

The European Union is transitioning towards a circular and low-carbon economy, emphasizing renewable biological resources. This study explores the production of high-value compounds like chitosan from fungal biomass and presents a potential design for a sustainable biorefinery process, contributing to the diversification and optimisation of biomass feedstock utilisation. The process simulation includes dedicated sub-models for each unit operation, based on laboratory data and integrated into a comprehensive process flow sheet using COCO-COFE. The productivity of the simulated plant results in 2 500 tons of triglyceride oils and 1 800 tons of chitosan that can be produced from 15 000 tons of Aspergillus niger. On-site acetic acid production meets 45% of the total plant's demand, significantly reducing the amount of additional acetic acid to be purchased as raw material. Additionally, large-scale enzyme consumption and the substantial heat demand for biomass processing are key economic a... [more]

Machine learning algorithms have led to the development of numerous vector embeddings for biological entities such as metabolites, proteins, genes, and enzymes. However, these embeddings often lack contextual information due to their specialized focus on individual omics. Disease progression and biosynthesis pathways are increasingly understood through complex, multi-layered networks that integrate diverse omics data and intricate signaling and reaction sequences. Capturing these relationships in a meaningful way requires embeddings that account for both functional and multi-modal dependencies. We propose an embedding approach that unifies these different biological modalities by treating them as directions in a shared space rather than as isolated data types. Similar to how word embeddings in natural language processing reveal meaningful relationships (e.g., Tokyo – Japan + UK = London, indicating a directional representation of capitals), we can model genes and proteins in a way that... [more]

To match the growing demand for biomethane production, anaerobic digestors need an optimal and time-varying adaptation of the input diet. Dynamic co-digestion constitutes a hard challenge for the limited instrumentation and control equipment typically installed aboard full-scale plants. The development of prediction models is foreseen to support process (optimal) design and control. In this work, a rigorous framework was applied to take full-scale applicability into account while dealing with the design and training of both high-fidelity and control-oriented first-principle/grey-box models, to be used for real-time optimization and process control respectively.

Microalgae cultivation on liquid digestate from the anaerobic co-digestion of agricultural feedstocks is an interesting option for digestate nutrient removal and resource recovery coupled to value-added biomass production. In this paper, a first-principle plant-wide modelling of the process is described. Two well-established models for anaerobic digestion (IWA – ADM1) and algae-based bioremediation processes (ALBA) were considered and modified with necessary equations and extensions to develop a coherent interface between the state variables of the two models. The resulting system is composed by highly non-linear and non-smooth DAEs. Open-loop scenario analysis for different upstream co-digester design and operating conditions was carried out to assess the impacts on the downstream microalgae outputs. It highlighted the importance of a proper biorefinery design and yet a noteworthy robustness of the system performance. The exploitation of the model can facilitate: a more realistic asse... [more]

Hybrid modelling utilizes advantageous aspects of both mechanistic (white box) and data-driven (black box) modelling. Combining the physical interpretability of kinetic modelling with the power of a data-driven Artificial Neural Network (ANN) yields a hybrid (grey box) model with superior accuracy when compared to a traditional mechanistic model, while requiring less data than a purely data-driven model. This study demonstrates the construction a hybrid model with transfer learning for the predictive modelling and optimization of a high-cell-density microalgal fermentation process for lutein production. Dynamic optimization was conducted to identify a feeding strategy that maximized final lutein production. The results were then experimentally validated. Overall, this work presents a novel digital twin application that can be easily adapted to general bioprocesses for model predictive control and process optimization.

Maintaining consistent product quality and yield in bioprocess operations is challenging due to uncertainties inherent in biological systems. Thus, robust strategies are essential to ensure key performance indicators (KPIs), such as product concentration and yield, are consistently met despite the uncertainties. Real-time feedback co Interntrol, though widely used, is often impractical due to its reliance on expensive sensors, rapid data processing, and high-speed control actions. This paper proposes a novel approach to address these challenges by identifying the operational space for control variables, ensuring KPI reliability without requiring real-time control. This operational space serves as a guideline such that, if we operate within this space, the KPIs can be reliably achieved, regardless of the considered uncertainties. Specifically, we reformulate the problem as an optimization task to maximize the operational space, subject to constraints imposed by process dynamics and perf... [more]

The 2,3-, 1,4- and 1,3-butanediols (BDOs) are valuable platform chemicals traditionally produced through petrochemical routes. Alternatively, there is growing interest in synthesizing these chemicals through fermentation processes. However, several drawbacks of the fermentation process (e.g. low product concentration, formation of by-products and high-boiling temperatures of BDOs) hinder the downstream process and increase overall production costs. This original research proposes an advanced large-scale (processing capacity of 160 ktonne/y) process design for the purification of different BDOs after fermentation. The initial preconcentration step removes most water and light impurities in heat pump-assisted distillation column. The heart of the developed process is an integrated dividing-wall column that effectively separates high-purity BDO (>99.4 wt% in all cases) from the remaining impurities. Each BDO isomer was purified cost-effectively (0.208 – 0.243 $/kgBDO) and energy-efficient... [more]

The growing concern over the environmental impacts of the wine industry has driven the search for sustainable technologies to manage its waste, particularly the residual sludge generated during effluent treatment. This sludge, rich in organic matter, represents a significant source of pollution if not properly treated. However, their energy content allows them to turn this environmental liability into an asset through innovative valorization. Hydrothermal liquefaction (HTL) emerges as a promising technology in this context. This process allows the direct conversion of residual sludge into high-energy-value liquid biofuels. Unlike other treatment methods, HTL can process wet biomass without needing prior drying, making it particularly suitable for managing sludge from wine effluents. Thus, this research aims to evaluate the conversion of residual sludge derived from wine effluent treatment into biofuels through a hydrothermal liquefaction simulation, integrating this process into a sust... [more]

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]

Optimal and autonomous daily use of new technologies isn’t a reality for everyone. In a societal context driven by sociotechnical transitions [1] many people lack access to digital equipment and skills, preventing their participation in digital social life, including energy services. Our exploratory and phenomenological research, guided by European Union directives [2], explores the social appropriation [3] of new technologies during the deployment of smart meters in Wallonia. The study investigates social behaviour of audiences with support during smart meter installation and identifies barriers to technology appropriation. In an exclusively qualitative approach, the field surveys aim to determine to what extent individual participatory forms [4][5] and collective forms [8][9] of support, through active pedagogies like experiential learning [6][7], can include digitally vulnerable users. The central role of field professionals as interfaces [10] is also highlighted within the service... [more]

The field of Process Systems Engineering (PSE) is undergoing a renaissance through the integration of artificial intelligence (AI) and machine learning (ML). This transformation is driven by the vast availability of industrial data and advanced computing power, enabling the practical application of sophisticated ML models. These models enhance PSE capabilities in design, control, optimization, and safety. The progress of ML and ever-present data collection address previously intractable problems, particularly in system integration and life-cycle modeling. ML-powered predictive algorithms are augmenting traditional control systems, showing potential in supply chain optimization and increasing operational resilience. Additionally, ML-driven fault prediction and diagnostics are enhancing process safety systems, allowing for predictive maintenance and minimizing risks of accidents. A case study of the collaboration between the University of West Attica and Helleniq Energy through the MSc p... [more]

This paper examines the current state of green and digital integration in traditional chemical engineering education, focusing on how artificial intelligence (AI) can enhance learning. A review of curricula shows that sustainability principles, such as green chemistry, circular economy, and resource efficiency, are often confined to electives rather than core courses. Likewise, digital skills are introduced at a basic level, with limited exposure to AI, especially machine learning, and advanced process optimization. The paper emphasizes the need for a structured approach to integrating sustainability and digitalization into core subjects, supported by interdisciplinary learning. It also explores AI’s role in transforming education, particularly in predictive modeling, process optimization, and adaptive learning. The study provides recommendations for redesigning the traditional chemical engineering curriculum to strengthen green and digital transformation.

The Integrated Project in the Master of Chemical Engineering and Materials Science at the University of Liège (ULiège) aims to consolidate technical knowledge and promote the acquisition of soft skills by integrating various chemical engineering disciplines. The project focus on the design of an industrial process and is divided into five parts: individual work on mass balances and literature reviews, detailed modeling of thermodynamics and key unit operations, sensitivity studies, process integration, and report to a general audience. Key learning outcomes include developing critical thinking, addressing complex multidisciplinary topics, and understanding the role of science and technology in society. Students enhance their soft skills in project management, teamwork, and effective communication in English. Regular interactions with industry and academic experts, along with support from the ULiège Soft Skills Team, ensure comprehensive development. Evaluation includes both technical a... [more]

Carnegie Mellon University (CMU) has a strong tradition and expertise in Chemical Process Systems Engineering. This short article comments on the CMU PSE-related courses and describes in more detail our approach to teaching Chemical Process Design. We discuss (i) our emphasis on proposing processes related to energy and sustainability and (ii) some of the challenges that are currently faced when teaching this course.

This work explores several examples of how the thermodynamic concept of exergy can be used in the chemical engineering classroom. Examples include using exergy to determine thermodynamic and monetary value of utilities, to identify better heat exchanger network designs, to aid in work-heat integration applications such as heat pumps and organic Rankine cycles, to scope out realistic energy integration cases, and to assess how well chemical potential is being used and managed. The examples are presented in one connected context that makes it easy to see how exergy analyses can be useful across many aspects of chemical and energy industry supply chains.

This work gives a detailed description of the models, methods, and equipment used in a bachelor’s degree lab course. The connections between simulation results and real-world data are highlighted and tools for making the models useful for process design tasks are portrayed. The models cover the production chain for fuel-grade bioethanol, starting from the fermentation of sugar with yeast. In only one semester (14 weeks with 180 minutes per week) the students achieve to produce high-purity ethanol. Some exemplary results of the process designs and their comparison to the realized intermediate and final products are given together with production cost data.

The integration of mixed reality (MR) and virtual reality (VR) into Chemical, Biochemical, and Biomolecular Engineering (CBB) education presents an opportunity to address one of today’s most pressing pedagogical challenges: sustaining student attention and engagement. Traditional “magistral” approaches often tend to limit the adoption of interactive methodologies. By contrast, MR/VR technologies can heighten immersion and practical intuition, capturing learner focus more effectively than conventional lectures. Yet, if deployed as superficial, isolated demonstrations, these tools may fail to support deep conceptual understanding and risk supplanting core course content. This work proposes a flipped-classroom model that deliberately embeds MR/VR exercises throughout the typical CBB curriculum. The methodology emphasizes a human-in-the-loop concept, whereby the educator strategically orchestrates virtual simulations and real-world problem-solving, reinforcing theoretical concepts through... [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]

UCL Chemical Engineering ensures graduates are digitally literate by integrating computational tools like gPROMS, Aspen Plus, and GAMS into the undergraduate curriculum. Students in the first year of undergraduate program use GAMS to solve simple simulation and optimization problems and gPROMS for solving ordinary differential equations (ODEs) in reactor design problems. In the second year, students start using Aspen Plus to simulate more complex chemical process units, interpret and discuss results obtained and justify any differences observed between experimental data and computational results. They use GAMS to simulate and optimize a process flowsheet with considerations of the implications of proper initialization procedures and strategies for obtaining optimal parameters and gPROMS for advanced reactor and separator problems. The computational knowledge acquired in the first two years prepares students for the third-year capstone design project where they use the various tools in... [more]