Data Science-Based Full-Lifespan Management of Lithium-Ion Battery

This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers

Data-driven modelling and monitoring of industrial processes with applications in nuclear waste vitrification

PhD ThesisProcess models are critical for process monitoring, control, and optimisation. With the increasing amount of process data and advancements in computational hardware, data-driven models are a good alternative to mechanistic models, which often have inaccuracies or are too costly to develop. One problem with data-driven models is the difficulty in ensuring that the models perform well on new data and produce accurate predictions in complex situations, which are frequently encountered in the process industry. Within this context, part of this thesis explores developing better data-driven models through using a latent variable technique, known as slow feature analysis, as a pre-processing step to regression. Slow feature analysis extracts slow varying features that contain underlying trends in the data, which can improve model performance through providing more meaningful information to regression, reducing noise, and reducing dimensionality. Firstly, the effectiveness of combining linear slow feature analysis with a neural network is demonstrated on two industrial case studies of soft sensor development and is compared with conventional techniques, such as neural networks and integration of principal component analysis with a neural network. It is shown that integration of slow feature analysis with neural networks can significantly improve model performance. However, linear slow feature analysis can fail to extract the driving forces behind data in nonlinear situations such as batch processes. Therefore, using kernel slow feature analysis with a neural network is proposed to further enhance process model performance. A numerical example was used to demonstrate the effective extraction of driving forces in a nonlinear case where linear slow feature analysis cannot. Model generalisation performance was improved using the proposed method on both this numerical example, and an industrial penicillin process case study. Dealing with radioactive nuclear waste is an important obstacle in nuclear energy. Sellafield Ltd have a nuclear waste vitrification plant which converts high-level nuclear waste into a more stable, lower volume glass form, which is more appropriate for long term storage in sealed containers. This thesis presents three applications of data-driven modelling to this nuclear waste vitrification process. A predictive model of the pour rate of processed nuclear waste into containers, an early detection system for blockages in the dust scrubber, and a model of the long-term chemical durability of the stored glass waste. These applications use the previously developed slow feature analysis methods, as well as other data-driven techniques such as extreme learning machine and bootstrap aggregation, for enhancing the model performance.Engineering and Physical Sciences Research Council (EPSRC) and Sellafield Lt

Data Science-Based Full-Lifespan Management of Lithium-Ion Battery

This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers

Water quality management using hybrid machine learning and data mining algorithms: An indexing approach

One of the key functions of global water resource management authorities is river water quality (WQ) assessment. A water quality index (WQI) is developed for water assessments considering numerous quality-related variables. WQI assessments typically take a long time and are prone to errors during sub-indices generation. This can be tackled through the latest machine learning (ML) techniques that are renowned for superior accuracy. In this study, water samples were taken from the wells in the study area (North Pakistan) to develop WQI prediction models. Four standalone algorithms, i.e., random trees (RT), random forest (RF), M5P, and reduced error pruning tree (REPT), were used in this study. In addition, 12 hybrid data-mining algorithms (combination of standalone, bagging (BA), cross-validation parameter selection (CVPS), and randomizable filtered classification (RFC)) were also used. Using the 10-fold cross-validation technique, the data were separated into two groups (70:30) for algorithm creation. Ten random input permutations were created using Pearson correlation coefficients to identify the best possible combination of datasets for improving the algorithm prediction. The variables with very low correlations performed poorly, whereas hybrid algorithms increased the prediction capability of numerous standalone algorithms. Hybrid RT-Artificial Neural Network (RT-ANN) with RMSE = 2.319, MAE = 2.248, NSE = 0.945 and PBIAS = -0.64, outperformed all other algorithms. Most algorithms overestimated WQI values except for BA-RF, RF, BA-REPT, REPT, RFC-M5P, RFC-REPT, and ANN- Adaptive Network-Based Fuzzy Inference System (ANFIS)

Digital soil mapping of soil physical and chemical properties using proximal and remote sensed data in Australian cotton growing areas

In Australian cotton-growing areas, information of soil physical and chemical properties is required as they decide soil structure, nutrient availability and water holding capacity. However, using conventional laboratory methods to determine these properties is impractical as they are time-consuming and costly. This is especially the case when considering samples from different depths and across heterogenous fields and districts. Thus, there is a need for efficient and affordable methods to enable data generation. To answer this need, digital soil mapping (DSM) can be used, in which limited laboratory measured soil data is coupled with cheaper-to acquire digital data through models and then the model and spatially exhaustive digital data are used to predict soil properties on unsampled locations. This thesis evaluates DSM methods for the prediction of soil physical (e.g., clay content) and chemical (e.g., cation exchange capacity [CEC] and exchangeable [exch.] cations) properties at various depths across cotton growing areas in south-eastern Australia, at field and district scales. Chapter 1 is the general introduction where research problems are defined, and research objectives are introduced. To point out gaps in the application of DSM on the prediction of soil properties, Chapter 2 comprehensively reviews DSM concepts, the applicability of proximally (e.g., electromagnetic induction (EM), visible near-infrared spectroscopy (vis-NIR)) or remotely (e.g., γ-ray spectrometer) sensed digital data for prediction of soil properties at various depths and the modelling techniques. The first research chapter (Chapter 3) compares various strategies to build the vis-NIR spectral library for clay content prediction at two depths across seven cotton growing areas using Cubist model. The results show that the area-specific vis-NIR library achieve the best results. The improvement in model performance is possible using spiking. The Chapter 4 compares multivariate methods for estimating clay content and its uncertainty map at two depths and the effect of weighted model averaging is evaluated. The results show that random forest (RF) model generally performs the best and model averaging could further improve the prediction accuracy. The Chapter 5 evaluates the potential of vis-NIR as a tool for the simultaneous prediction of soil physical and chemical properties across cotton growing areas and considering two calibration models. The results show that satisfactory predictions of clay and CEC are achieved with silt and sand prediction moderate, while the prediction of pH and exchangeable sodium percentage (ESP) are unsatisfactory. A multi-depth vis-NIR library generally performs better than depth-specific libraries on prediction of soil properties. The Chapter 6 builds a topsoil (0 – 0.3 m) vis-NIR spectral library to predict topsoil exch. cations considering four different calibration models and explores the applicability of the topsoil library to predict exch. cations at deeper depths considering spiking or not. The results show that the vis-NIR could provide satisfactory prediction of exch. calcium and magnesium. Topsoil spectral library could be used to predict exch. cations at deeper depth with spiking further improving the result. The Chapter 7 estimates spatial variation of CEC at various depths using quasi-3d joint inversion of EM38 and EM31 data in an irrigated cotton field. The results indicate that the joint-inversion approach developed in this study could generate accurate 3D predictions of soil CEC in the cotton growing field. This thesis explores DSM methods for the prediction of soil physical and chemical properties in Australian cotton growing areas and the results deliver new evidence of the potential to use proximally and remotely sensed digital data and state-to-art models for rapid and efficient generation of soil information. New findings will serve to advance the existing knowledge on application of DSM at field and district scales

Advances in Binders for Construction Materials

The global binder production for construction materials is approximately 7.5 billion tons per year, contributing ~6% to the global anthropogenic atmospheric CO2 emissions. Reducing this carbon footprint is a key aim of the construction industry, and current research focuses on developing new innovative ways to attain more sustainable binders and concrete/mortars as a real alternative to the current global demand for Portland cement.With this aim, several potential alternative binders are currently being investigated by scientists worldwide, based on calcium aluminate cement, calcium sulfoaluminate cement, alkali-activated binders, calcined clay limestone cements, nanomaterials, or supersulfated cements. This Special Issue presents contributions that address research and practical advances in i) alternative binder manufacturing processes; ii) chemical, microstructural, and structural characterization of unhydrated binders and of hydrated systems; iii) the properties and modelling of concrete and mortars; iv) applications and durability of concrete and mortars; and v) the conservation and repair of historic concrete/mortar structures using alternative binders.We believe this Special Issue will be of high interest in the binder industry and construction community, based upon the novelty and quality of the results and the real potential application of the findings to the practice and industry