Effective prediction of nutrient demands is crucial for optimising microalgae growth, maximising productivity and minimising the waste of resources. With the increasing amount of data related to microalgae cultivation systems, data mining and machine learning models to extract additional knowledge have gained popularity. In the development of such models, a data preprocessing stage is necessary due to the poor data quality. At this stage, cleaning and outlier removal techniques are employed to eliminate missing data and outliers, respectively. Afterwards, data splitting and cross-validation strategies are employed to ensure that the models are trained and evaluated with representative subsets of the data. Principal component analysis is also applied to simplify complex environmental datasets by reducing the number of features while retaining as much information as possible. To further improve prediction capabilities, ensemble methods are incorporated, leveraging multiple models to achi... [more]

Understanding the complexity of human digestion is critical for designing models that serve as valuable research tools for process simulation and prediction. Due to the high cost of medical intervention & recent advancements in in vitro digestion protocols, increased demand for inexpensive in silico solutions emerges. This study aims to develop a mathematical model that simulates the in vitro dynamic Food Digestion SIMulator (FooDSIM) functionalities via a digital twin approach. Ordinary Differential Equations (ODEs) simulate the system as a series of Continuously Stirred Tank Reactors (CSTRs) and describe different regions of human organs (stomach, duodenum, ileum, colon) of the human Gastrointestinal Tract (GIT). Various time horizons were used to investigate the effect of periodic feeding on the dynamic stabilisation of the inherently simulated processes (hydraulics, pH, biochemical interactions between enzymes & substrates, and nutrient absorption). A Polynomial Chaos Expansion (P... [more]

Predicting water quality is essential for effective environmental management and pollution control. Dissolved oxygen (DO), one of key water quality parameters, plays a vital role in biological wastewater treatment [1]. This study aims to forecast DO levels in activated sludge tanks of an oil refinery’s wastewater treatment plant (WWTP). Proper oxygen concentration is critical for microbial activity, as inadequate levels can disrupt the biological breakdown of pollutants. The objective is to develop predictive models to identify operational risks early, enhancing treatment efficiency and optimizing resources like chemicals, bacterial cultures, and aeration systems. Additionally, the study aims to provide early warnings to operators, minimizing reliance on laboratory tests and ensuring optimal conditions for bacteria, leading to better operational performance, cost reduction, and improved water quality ultimately promoting sustainable wastewater treatment. Various deep learning models, i... [more]

Water lily (Eichhornia crassipes) has been identified as an invasive exotic plant with high proliferation in Mexico, affecting aquatic bodies, such as lakes. After extraction, the water hyacinth biomass can be used as raw material for the production of bioproducts and bioenergy, however, the majority of them not covered the region's needs, and their economic profitability decreases significantly. Also, few reports present its use as raw material inside a biorefinery scheme. In this work, we propose a local biorefinery scheme to produce power and bioproducts from water lilies, using Aspen Plus V.10.0, per the needs of the Patzcuaro Lake community in Michoacán, Mexico. The scheme has been designed to process the harvested and sun-dried water lily from 197.6 kg/h of total wet harvested biomass, according to the extraction region schedule. The biomass is separated: root (RT) and stems-leaves (SL). The processing scheme involves the RT combustion to produce electric power, and two process... [more]

Optimizing greenhouse operations in arid regions is essential for sustainable agriculture due to limited water resources and high energy demands for climate control. This paper proposes a multi-objective optimization framework aimed at minimizing both the operational costs and environmental emissions of a climate-controlled greenhouse. The framework determines optimal allocation of growing area among three crops (tomato, cucumber, and bell pepper) throughout the year. These crops were selected for their varying growth conditions, which induce variability in energy and water inputs, providing a comprehensive assessment of the optimization model. The model integrates factors such as temperature, humidity, light intensity, and irrigation requirements specific to each crop. It is solved using a genetic algorithm combined with Pareto front analysis to address the multi-objective nature effectively. This approach facilitates the identification of optimal trade-offs between cost, emissions, a... [more]

The extraction of lycopene from tomato waste has been largely evaluated at an experimental level, leading to the creation of polynomial models or response surfaces that allow the representation of the extraction behavior. However, these studies are based on laboratory level and an extraction process has not yet been scaled up. This study evaluates the design and optimization of the lycopene extraction process from tomato waste. The proposed model is solved through a link between Python and Aspen Plus, performing the optimization a genetic algorithm (GA) in Pymoo. The minimum value of TAC is 211,692.2 USD/yr, corresponding to a production of 2.29 g/h of lycopene, starting from 1000 kg/h of tomato waste. This work represents a first approach to the design of a commercial-scale lycopene production process.

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.