476,165 research outputs found
Harnessing the power of neural networks for the investigation of solar-driven membrane distillation systems under the dynamic operation mode
Accurate modeling of solar-driven direct contact membrane distillation systems (DCMD) can enhance the commercialization of these promising systems. However, the existing dynamic mathematical models for predicting the performance of these systems are complex and computationally expensive. This is due to the intermittent nature of solar energy and complex heat/mass transfer of different components of solar-driven DCMD systems (solar collectors, MD modules and storage tanks). This study applies a machine learning-based approach to model the dynamic nature of a solar-driven DCMD system for the first time. A small-scale rig was designed and fabricated to experimentally assess the performance of the system over 20 days. The predictive capabilities of two neural network models: multilayer perceptron (MLP) and long short-term memory (LSTM) were then comprehensively examined to predict the permeate flux, efficiency and gain-output-ratio (GOR). The results showed that both models can efficiently predict the dynamic performance of solar-driven DCMD systems, where MLP outperformed the LSTM model overall, especially in the prediction of efficiency. Additionally, it was indicated that the accuracy of the models for the prediction of GOR can be significantly improved by increasing the size of the dataset
Pressure-driven modelling of water distribution systems
This paper presents a novel method to model water distribution systems (WDS) with insufficient pressure. Methods for the prediction of the performance of a WDS with pressure deficiencies are reviewed. The influence of imposed relationships between nodal heads and outflows is assessed and numerical results are given. A Newton-Raphson technique plus line search is employed for solving the governing equations. It is demonstrated that the approach offers superior results for the hydraulic performance of networks under abnormal operating conditions compared to demand-driven analysis-based models
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach
Learning kinetic systems from data is one of the core challenges in many
fields. Identifying stable models is essential for the generalization
capabilities of data-driven inference. We introduce a computationally efficient
framework, called CausalKinetiX, that identifies structure from discrete time,
noisy observations, generated from heterogeneous experiments. The algorithm
assumes the existence of an underlying, invariant kinetic model, a key
criterion for reproducible research. Results on both simulated and real-world
examples suggest that learning the structure of kinetic systems benefits from a
causal perspective. The identified variables and models allow for a concise
description of the dynamics across multiple experimental settings and can be
used for prediction in unseen experiments. We observe significant improvements
compared to well established approaches focusing solely on predictive
performance, especially for out-of-sample generalization
Performance-oriented model learning for data-driven MPC design
Model Predictive Control (MPC) is an enabling technology in applications
requiring controlling physical processes in an optimized way under constraints
on inputs and outputs. However, in MPC closed-loop performance is pushed to the
limits only if the plant under control is accurately modeled; otherwise, robust
architectures need to be employed, at the price of reduced performance due to
worst-case conservative assumptions. In this paper, instead of adapting the
controller to handle uncertainty, we adapt the learning procedure so that the
prediction model is selected to provide the best closed-loop performance. More
specifically, we apply for the first time the above "identification for
control" rationale to hierarchical MPC using data-driven methods and Bayesian
optimization.Comment: Accepted for publication in the IEEE Control Systems Letters (L-CSS
Towards Modelling and Analysing Non-Functional Properties of Systems of Systems
International audienceSystems of systems (SoS) are large-scale systems composed of complex systems with difficult to predict emergent properties. One of the most significant challenges in the engineering of such systems if how to predict their Non-Functional Properties (NFP) such as performance and security, and more specifically, how to model NFP when the overall system functionality is not available. In this paper, we identify, describe and analyse challenges to modelling and analysing the performance and security NFP of SoS. We define an architectural framework to SoS NFP prediction based on the modelling of system interactions and their impacts. We adopt an Event Driven Architecture to support this modelling, as it allows for more realistic and flexible NFP simulation, which enables more accurate NFP prediction. A framework integrating the analysis of several NFP allows for exploring the impacts of changes made to accommodate issues on one NFP on other NFPs
Uncertainty-aware deep learning for prediction of remaining useful life of mechanical systems
Remaining useful life (RUL) prediction is a problem that researchers in the prognostics and health management (PHM) community have been studying for decades. Both physics-based and data-driven methods have been investigated, and in recent years, deep learning has gained significant attention. When sufficiently large and diverse datasets are available, deep neural networks can achieve state-of-the-art performance in RUL prediction for a variety of systems. However, for end users to trust the results of these models, especially as they are integrated into safety-critical systems, RUL prediction uncertainty must be captured. This work explores an approach for estimating both epistemic and heteroscedastic aleatoric uncertainties that emerge in RUL prediction deep neural networks and demonstrates that quantifying the overall impact of these uncertainties on predictions reveal valuable insight into model performance. Additionally, a study is carried out to observe the effects of RUL truth data augmentation on perceived uncertainties in the model
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