Predictive Modeling of Dynamic Processes provides an overview of hydrocode technology, applicable to a variety of industries and areas of engineering design. Covering automotive crash, blast impact, and hypervelocity impact phenomena, this volume offers readers an in-depth explanation of the fundamental code components. Chapters include informative introductions to each topic, and explain the specific requirements pertaining to each predictive hydrocode. Successfully blending crash simulation, hydrocode technology and impact engineering, this volume fills a gap in the current competing literature available.

This work provides an overview of hydrocode technology, applicable to a variety of industries and areas of engineering design. It successfully blends crash simulations with hydrocode technology, and offers an explanation of the fundamental code components.

Inspired by the leading authority in the field, the Centre for Process Systems Engineering at Imperial College London, this book includes theoretical developments, algorithms, methodologies and tools in process systems engineering and applications from the chemical, energy, molecular, biomedical and other areas. It spans a whole range of length scales seen in manufacturing industries, from molecular and nanoscale phenomena to enterprise-wide optimization and control. As such, this will appeal to a broad readership, since the topic applies not only to all technical processes but also due to the interdisciplinary expertise required to solve the challenge. The ultimate reference work for years to come.

A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor. Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a “data-driven” approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.

Personalized Predictive Modeling in Diabetes features state-of-the-art methodologies and algorithmic approaches which have been applied to predictive modeling of glucose concentration, ranging from simple autoregressive models of the CGM time series to multivariate nonlinear regression techniques of machine learning. Developments in the field have been analyzed with respect to: (i) feature set (univariate or multivariate), (ii) regression technique (linear or non-linear), (iii) learning mechanism (batch or sequential), (iv) development and testing procedure and (v) scaling properties. In addition, simulation models of meal-derived glucose absorption and insulin dynamics and kinetics are covered, as an integral part of glucose predictive models. This book will help engineers and clinicians to: select a regression technique which can capture both linear and non-linear dynamics in glucose metabolism in diabetes, and which exhibits good generalization performance under stationary and non-stationary conditions; ensure the scalability of the optimization algorithm (learning mechanism) with respect to the size of the dataset, provided that multiple days of patient monitoring are needed to obtain a reliable predictive model; select a features set which efficiently represents both spatial and temporal dependencies between the input variables and the glucose concentration; select simulation models of subcutaneous insulin absorption and meal absorption; identify an appropriate validation procedure, and identify realistic performance measures. Describes fundamentals of modeling techniques as applied to glucose control Covers model selection process and model validation Offers computer code on a companion website to show implementation of models and algorithms Features the latest developments in the field of diabetes predictive modeling

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download.

Big data is a cornerstone component of the fourth industrial revolution, which calls onengineers and researchers to fully utilize data in order to make smart decisions and enhance the efficiency of industrial processes as well as control systems. In practice, industrial process control systems typically rely on a data-driven model (often linear) with parameters that are determined by industrial/simulation data. However, in some scenarios, such as in profit-critical or quality-critical control loops, first-principles concepts that are based on the underlying physico-chemical phenomena may also need to be employed in the modeling phase to improve data-based process models. Hence, process systems engineers still face significant challenges when it comes to modeling large-scale, complicated nonlinear processes. Modeling will continue to be crucial since process models are essential components of cutting-edge model-based control systems, such as model predictive control (MPC). Machine learning models have a lot of potential based on their success in numerousapplications. Specifically, recurrent neural network (RNN) models, designed to account for every input-output interconnection, have gained popularity in providing approximation of various highly nonlinear chemical processes to a desired accuracy. Although the training error of neural networks that are dense and fully-connected may often be made sufficiently small, their accuracy can be further improved by incorporating prior knowledge in the structure development of such machine learning models. Physics-based recurrent neural networks modeling has yielded more reliable machine learning models than traditional, fully black-box, machine learning modeling methods. Furthermore, the development of systematic and rigorous approaches to integrate such machine learning techniques into nonlinear model-based process control systems is only getting started. In particular, physics-based machine learning modeling techniques can be employed to derive more accurate and well-conditioned dynamic process models to be utilized in advanced control systems such as model predictive control. Along with Lyapunov-based stability constraints, this scheme has the potential to significantly improve process operational performance and dynamics. Hence, investigating the effectiveness of this control scheme under the various long-standing challenges in the field of process systems engineering such as incomplete state measurements, and noise and uncertainty is essential. Also, a theoretical framework for constructing and assessing the generalizability of this type of machine learning models to be utilized in model predictive control systems is lacking. In light of the aforementioned considerations, this dissertation addresses the incorporation ofprior process knowledge into machine learning models for model predictive control of nonlinear chemical processes. The motivation, background and outline of this dissertation are first presented. Then, the use of machine learning modeling techniques to construct two different data-driven state observers to compensate for incomplete process measurements is presented. The closed-loop stability under Lyapunov-based model predictive controllers is then addressed. Next, the development of process-structure-based machine learning models to approximate large, nonlinear chemical processes is presented, with the improvements yielded by this approach demonstrated via open-loop and closed-loop simulations. Subsequently, the reliability of process-structure-based machine learning models is investigated in the presence of different types of industrial noise. Two novel approaches are proposed to enhance the accuracy of machine learning models in the presence of noise. Lastly, a theoretical framework that connects the accuracy of an RNN model to its structure is presented, where an upper bound on a physics-based RNN model's generalization error is established. Nonlinear chemical process examples are numerically simulated or modeled in Aspen Plus Dynamics to illustrate the effectiveness and performance of the proposed control methods throughout the dissertation.

Dynamics is what characterizes virtually all phenomenae we face in the real world, and processes that proceed in practically all kinds of inanimate and animate systems, notably social systems. For our purposes dynamics is viewed as time evolution of some characteristic features of the phenomenae or processes under consideration. It is obvious that in virtually all non-trivial problems dynamics can not be neglected, and should be taken into account in the analyses to, first, get insight into the problem consider, and second, to be able to obtain meaningful results. A convenient tool to deal with dynamics and its related evolution over time is to use the concept of a dynamic system which, for the purposes of this volume can be characterized by the input (control), state and output spaces, and a state transition equation. Then, starting from an initial state, we can find a sequence of consecutive states (outputs) under consecutive inputs (controls). That is, we obtain a trajectory. The state transition equation may be given in various forms, exemplified by differential and difference equations, linear or nonlinear, deterministic or stochastic, or even fuzzy (imprecisely specified), fully or partially known, etc. These features can give rise to various problems the analysts may encounter like numerical difficulties, instability, strange forms of behavior (e.g. chaotic), etc. This volume is concerned with some modern tools and techniques which can be useful for the modeling of dynamics. We focus our attention on two important areas which play a key role nowadays, namely automation and robotics, and biological systems. We also add some new applications which can greatly benefit from the availability of effective and efficient tools for modeling dynamics, exemplified by some applications in security systems.