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Computational modeling and optimization of biopharmaceutical downstream processes

Keulen, D. (author)

Vaccination is a strong and effective way to prevent spreading of infectious diseases and promotes global health. In the future, the importance of vaccines is expected only to increase, driven by factors such as increased international traveling, higher healthcare expenditures, and a growing population. To meet the growing demands, it is...

doctoral thesis 2024

document

Damage Detection of Gantry Crane with a Moving Mass Using Artificial Neural Network

Safaei, Mohammad (author), Hejazian, Mahsa (author), Pedrammehr, Siamak (author), Pakzad, Sajjad (author), Ettefagh, Mir Mohammad (author), Fotouhi, M. (author)

Gantry cranes play a pivotal role in various industrial applications, and their reliable operation is paramount. While routine inspections are standard practice, certain defects, particularly in less accessible components, remain challenging to detect early. In this study, first a finite element model is presented, and the damage is introduced...

journal article 2024

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Micromechanics-based deep-learning for composites: Challenges and future perspectives

Mirkhalaf, Mohsen (author), Rocha, I.B.C.M. (author)

During the last few decades, industries such as aerospace and wind energy (among others) have been remarkably influenced by the introduction of high-performance composites. One challenge, however, for modeling and designing composites is the lack of computational efficiency of accurate high-fidelity models. For design purposes, using...

journal article 2024

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Label synchronization for Hybrid Federated Learning in manufacturing and predictive maintenance

Llasag Rosero, Raúl (author), Silva, Catarina (author), Ribeiro, Bernardete (author), Santos, Bruno F. (author)

Artificial Intelligence (AI) is transforming the future of industries by introducing new paradigms. To address data privacy and other challenges of decentralization, research has focused on Federated Learning (FL), which combines distributed Machine Learning (ML) models from multiple parties without exchanging confidential information....

journal article 2024

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Forecasting electricity demand of municipalities through artificial neural networks and metered supply point classification

Mateo-Barcos, S. (author), Ribo-Perez, D.G. (author), Rodríguez-García, J. (author), Alcázar-Ortega, M. (author)

This study develops a methodology to characterise and forecast large consumers’ electricity demand, particularly municipalities, with hundreds of different metered supply points based on the previous characterisation of facilities’ consumption. Demand forecasting allows consumers to improve their participation in electricity markets and...

journal article 2024

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Self-Supervised Neuromorphic Perception for Autonomous Flying Robots

Paredes-Vallés, Federico (author)

In the ever-evolving landscape of robotics, the quest for advanced synthetic machines that seamlessly integrate with human lives and society becomes increasingly paramount. At the heart of this pursuit lies the intrinsic need for these machines to perceive, understand, and navigate their surroundings autonomously. Among the senses, vision...

doctoral thesis 2023

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Black-Box Online Aerodynamic Performance Optimization for a Seamless Wing with Distributed Morphing

Ruland, Oscar (author), Mkhoyan, T. (author), De Breuker, R. (author), Wang, Xuerui (author)

journal article 2023

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Promises of Deep Kernel Learning for Control Synthesis

Reed, Robert (author), Laurenti, L. (author), Lahijanian, Morteza (author)

Deep Kernel Learning (DKL) combines the representational power of neural networks with the uncertainty quantification of Gaussian Processes. Hence, it is potentially a promising tool to learn and control complex dynamical systems. In this letter, we develop a scalable abstraction-based framework that enables the use of DKL for control...

journal article 2023

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To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration

Ottati, F. (author), Gao, Chang (author), Chen, Qinyu (author), Brignone, Giovanni (author), Casu, Mario R. (author), Eshraghian, Jason K. (author), Lavagno, Luciano (author)

As deep learning models scale, they become increasingly competitive from domains spanning from computer vision to natural language processing; however, this happens at the expense of efficiency since they require increasingly more memory and computing power. The power efficiency of the biological brain outperforms any large-scale deep...

journal article 2023

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Enhancing early-stage energy consumption predictions using dynamic operational voyage data: A grey-box modelling investigation

Odendaal, Kirsten (author), Alkemade, Aaron (author), Kana, A.A. (author)

The adverse human contribution to global climate change has forced the yachting industry to acknowledge the need to reduce its environmental impact due to the client's increasing pressure and potential future regulations to limit the ecological effects. Unfortunately, current real-world data presents a significant disparity between predicted...

journal article 2023

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Quantitative Damage Monitoring of Filament Wound Composites by Using Machine Learning-Based Techniques

Bani Mohammad Ali, Amir (author), Valizadeh Sotubadi, Saleh (author), Alimirzaei, Sajad (author), Ahmadi Najafabadi, Mehdi (author), Pahlavan, Lotfollah (author)

Composite structures in transportation industries have gained significant attention due to their unique characteristics, including high energy absorption. Non-destructive testing methods coupled with machine learning techniques offer valuable insights into failure mechanisms by analyzing basic parameters. In this study, damage monitoring...

journal article 2023

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How does “listening” help fracture understanding?

de Araujo Alves Lima, Rosemere (author), Tao, R. (author), Bernasconi, A. (author), Carboni, Michele (author), Carrere, Nicolas (author), Teixeira De Freitas, S. (author)

Understanding the relationship between the sensors’ outputs and the damage evolution within the joints is becoming increasingly crucial to improving structural health monitoring systems and collecting data to improve the joint’s design. Therefore, a study of the acoustic emission method associated with visual fracture evaluation was proposed to...

poster 2023

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Efficient Embedded Element Pattern Prediction via Machine Learning: A Case Study with Planar Non-Uniform Sub-Arrays

Onat, N.B. (author), Roldan Montero, I. (author), Fioranelli, F. (author), Yarovoy, Alexander (author), Aslan, Y. (author)

Efficient prediction of embedded element patterns (EEPs) is including the mutual coupling (MC) effects in the optimization of irregular planar arrays is studied for the first time in the literature. An ANN-based methodology is used to predict the pattern of each element in the whole visible space for a flexible planar array topology in...

conference paper 2023

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Using artificial neural networks to accelerate flowsheet optimization for downstream process development

Keulen, D. (author), van der Hagen, Erik (author), Geldhof, Geoffroy (author), Le Bussy, Olivier (author), Pabst, Martin (author), Ottens, M. (author)

An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches enable to screen a broad range of the design-space, in contrast to traditional statistical or...

journal article 2023

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Rapid extraction of pavement aggregate gradation based on point clouds using deep learning networks

Chen, Siyu (author), Chen, Can (author), Ma, Tao (author), Han, Chengjia (author), Luo, Haoyuan (author), Wang, Siqi (author), Gao, Y. (author), Yang, Yaowen (author)

Usage of asphalt mixture with poor gradation will most likely lead to pavement deficiency. There is a growing need for rapid and non-destructive methods to extract pavement aggregate gradation. In this study, a deep learning-based method that utilizes point clouds data for gradation extraction was proposed. Firstly, a data enhancement...

journal article 2023

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A comparative neural networks and neuro-fuzzy based REBA methodology in ergonomic risk assessment: An application for service workers

Yalcin Kavus, Bahar (author), Gülüm Taş, P. (author), Taskin, Alev (author)

Non-ergonomic working conditions are the leading causes of musculoskeletal disorders that seriously affect human health. REBA is widely used tool due to its convenience and consideration of all body parts. However, it heavily relies on the subjective judgments of the assessor, leading to inconsistencies in results, and lacks sensitivity in...

journal article 2023

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Physically recurrent neural networks for path-dependent heterogeneous materials: Embedding constitutive models in a data-driven surrogate

Alves Maia, M. (author), Rocha, I.B.C.M. (author), Kerfriden, P. (author), van der Meer, F.P. (author)

Driven by the need to accelerate numerical simulations, the use of machine learning techniques is rapidly growing in the field of computational solid mechanics. Their application is especially advantageous in concurrent multiscale finite element analysis (FE²) due to the exceedingly high computational costs often associated with it...

journal article 2023

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Design of Artificial Neural Network for predicting the reduction in permeability of porous media as a result of polymer gel injection

Kamel Targhi, Elahe (author), Emami Niri, Mohammad (author), Zitha, P.L.J. (author)

Cross-linked polymer gel is widely used in the oil and gas industry to block high permeability conduits and reduce water cut. The complex nature of this fluid, especially regarding flow in porous media, makes its numerical simulation very time-consuming. This study presents an approach to designing an Artificial Neural Network (ANN) model...

journal article 2023

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The Seven-League Scheme: Deep Learning for Large Time Step Monte Carlo Simulations of Stochastic Differential Equations

Liu, S. (author), Grzelak, L.A. (author), Oosterlee, C.W. (author)

We propose an accurate data-driven numerical scheme to solve stochastic differential equations (SDEs), by taking large time steps. The SDE discretization is built up by means of the polynomial chaos expansion method, on the basis of accurately determined stochastic collocation (SC) points. By employing an artificial neural network to learn these...

journal article 2022

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Neural-Network Decoders for Quantum Error Correction Using Surface Codes: A Space Exploration of the Hardware Cost-Performance Tradeoffs

Overwater, R.W.J. (author), Babaie, M. (author), Sebastiano, F. (author)

Quantum error correction (QEC) is required in quantum computers to mitigate the effect of errors on physical qubits. When adopting a QEC scheme based on surface codes, error decoding is the most computationally expensive task in the classical electronic back-end. Decoders employing neural networks (NN) are well-suited for this task but their...

journal article 2022