We used real plant data obtained from a water treatment plant in Peninsular Malaysia to develop a model to predict fluoride concentration in treated water. We validated the model with real plant data ...and demonstrated that the model yields good prediction, with mean absolute percentage error close to 10% or less. The validated model was then used to design an internal model control (IMC) tuned feedback controller, combined with a time‐varying feedforward control approach by exploiting the predictable and repetitive pattern of the inlet water flow rate. Simulation studies using random and real disturbance data show that the proposed control has successfully prevented over fluoridation in treated water. The validated model, due to its simplicity, is suitable for future online implementation. It can also be readily applied to model‐based control approaches (e.g., model predictive control) in future studies.
Empirical Modal Decomposition (EMD), and improved or modified techniques derived from EMD, collectively referred to as Empirical Modal Decomposition class (EMDC) techniques. EMDC techniques have a ...wide range of applications in building energy analysis, especially time–frequency analysis based noise cancellation in data-driven building energy prediction. However, there is a gap in the literature related to the choice of EMDC techniques in data-driven models. This paper provides a framework for a comprehensive comparison of EMD, Ensemble Empirical Mode Decomposition (EEMD), Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) techniques for building heat consumption prediction modeling. A real building is used as an example to compare the noise cancellation potential of these techniques and the prediction accuracy under various data-driven models. The results demonstrated that noise cancellation using the EMDC techniques significantly improves Signal-Noise Ratio, regularity, and consistency with the original signal trend. The prediction models trained using the noise-cancelled data have the Root Mean Squared Error and the Mean Absolute Error reductions of 22.5 % and 31.3 % on average, respectively. Meanwhile, the predicted signals of the models inherit the noise cancellation benefits of the noise-cancelled training data.
Understanding complex living systems, which are fundamentally constrained by physical phenomena, requires combining experimental data with theoretical physical and mathematical models. To develop ...such models, collaborations between experimental cell biologists and theoreticians are increasingly important but these two groups often face challenges achieving mutual understanding. To help navigate these challenges, this Perspective discusses different modelling approaches, including bottom-up hypothesis-driven and top-down data-driven models, and highlights their strengths and applications. Using cell mechanics as an example, we explore the integration of specific physical models with experimental data from the molecular, cellular and tissue level up to multiscale input. We also emphasize the importance of constraining model complexity and outline strategies for crosstalk between experimental design and model development. Furthermore, we highlight how physical models can provide conceptual insights and produce unifying and generalizable frameworks for biological phenomena. Overall, this Perspective aims to promote fruitful collaborations that advance our understanding of complex biological systems.
Battery systems are becoming an increasingly attractive alternative for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and ...manoeuvring is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests — annual capacity tests. However, this paper discusses data-driven state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. Thus, this paper presents a comprehensive review of different data-driven approaches to state of health modelling, and aims at giving an overview of current state of the art. More than 300 papers have been reviewed, most of which are referred to in this paper. Moreover, some reflections and discussions on what types of approaches can be suitable for modelling and independent verification of state of health for maritime battery systems are presented.
•Review of data-driven approaches to capacity and state of health modelling.•More than 300 papers have been reviewed.•The use for independent verification of maritime battery systems are discussed.•Different models are categorized in different approaches.•Discusses the appropriateness of various approaches for maritime applications.
There have been a number of digital twin (DT) frameworks proposed for multiple disciplines in recent years. However, there is a need to develop systematic methodologies to improve our ability to ...produce DT solutions for the nuclear fuel industry considering specific requirements and conditions exclusive to the nuclear fuel manufacturing cycle. A methodology tailored for nuclear fuel production is presented in this paper. Due to the nature of the chemical processes involved in fuel manufacturing, we highlight the importance of using a combination of physics-based and data-driven modelling. We introduce key technologies for DT construction and the technical challenges for DT are discussed. Furthermore, we depict typical application scenarios, such as key stages of the nuclear manufacturing cycle. Finally, a number of technology issues and research questions related to DT and nuclear fuel manufacturing are identified.
•Digital Twin systems based on physics-based models and data-driven models are proposed for nuclear fuel manufacturing applications.•Reaction kinetics-based simulations for nuclear fuel Digital Twin solutions are feasible since most fuel production processes are chemical reaction-based.•A methodology based on two phases, i.e., development phase and deployment phase, is proposed.•Challenges and opportunities are discussed and proposed, including data standardisation, online enrichment, cloud computing, nuclear industry security, and condition responsiveness and control.
Environmental Data Science Gibert, Karina; Horsburgh, Jeffery S.; Athanasiadis, Ioannis N. ...
Environmental modelling & software : with environment data news,
08/2018, Letnik:
106
Journal Article
Recenzirano
Environmental data are growing in complexity, size, and resolution. Addressing the types of large, multidisciplinary problems faced by today's environmental scientists requires the ability to ...leverage available data and information to inform decision making. Successfully synthesizing heterogeneous data from multiple sources to support holistic analyses and extraction of new knowledge requires application of Data Science. In this paper, we present the origins and a brief history of Data Science. We revisit prior efforts to define Data Science and provide a more modern, working definition. We describe the new professional profile of a data scientist and new and emerging applications of Data Science within Environmental Sciences. We conclude with a discussion of current challenges for Environmental Data Science and suggest a path forward.
•Data Science connects data with decisions by producing actionable knowledge from data and bridging the Hammond’s Fact Gap.•Environmental Sciences can benefit from the added value given by Data Science, which is strategic in complex systems.•Historical and contemporary views of Environmental Data Science and emerging environmental applications are given.•Data Science requires new professional skills. Multidisciplinary working teams are very convenient.•Environmental Data Science involve signifficant challenges, making a promising research field in short, mid and long term.
Linear power flow model is advantageous for the fast operational analysis and the efficient optimization of the power systems. In this letter, we propose a hybrid physical model-driven and ...data-driven approach for linearizing power flow model. In this proposed approach, the linear power flow model contains two parts, i.e., the existing physical-equation-based linear power flow model and the linearized error model. The linearized errors are obtained by the partial least squares regression based data-driven approach. The proposed linear power flow model can retain the useful inherent information from the physical model and utilize the ability of data analysis to extract the inexplicit linear relationship. Simulations on the four test systems have validated that the proposed hybrid linear model exhibits a much better performance on the branch power flow calculation than other linear power flow models.
•New convolutional model achieves state-of-the-art results on ETH and TrajNet datasets.•Random rotations and Gaussian noise are the best data augmentation techniques.•Coordinates with the origin in ...the last observation point better represent trajectory.
Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. In this work, we propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model. This new model outperforms recurrent models, and it achieves state-of-the-art results on the ETH and TrajNet datasets. We also present an effective system to represent pedestrian positions and powerful data augmentation techniques, such as the addition of Gaussian noise and the use of random rotations, which can be applied to any model. As an additional exploratory analysis, we present experimental results on the inclusion of occupancy methods to model social information, which empirically show that these methods are ineffective in capturing social interaction.
With the rapid development of computational techniques and scientific tools, great progress of data-driven analysis has been made to extract governing laws of dynamical systems from data. Despite the ...wide occurrences of non-Gaussian fluctuations, the effective data-driven methods to identify stochastic differential equations with non-Gaussian Lévy noise are relatively few so far. In this work, we propose a data-driven approach to extract stochastic governing laws with both (Gaussian) Brownian motion and (non-Gaussian) Lévy motion, from short bursts of simulation data. Specifically, we use the normalizing flows technology to estimate the transition probability density function (solution of non-local Fokker–Planck equations) from data, and then substitute it into the recently proposed non-local Kramers–Moyal formulae to approximate Lévy jump measure, drift coefficient and diffusion coefficient. We demonstrate that this approach can learn the stochastic differential equation with Lévy motion. We present examples with one- and two-dimensional decoupled and coupled systems to illustrate our method. This approach will become an effective tool for discovering stochastic governing laws and understanding complex dynamical behaviours.
This article is part of the theme issue ‘Data-driven prediction in dynamical systems’.
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