Development Of Data Driven Models For Chemical Engineering Systems
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Development Of Data Driven Models For Chemical Engineering Systems
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Author : Nusrat Parveen
language : en
Publisher: Mohammed Abdul Malik
Release Date : 2024-03-04
Development Of Data Driven Models For Chemical Engineering Systems written by Nusrat Parveen and has been published by Mohammed Abdul Malik this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-04 with Science categories.
Modeling of any system or a process is one of the significant but challenging tasks in engineering. The challenge is either due to the physical complexity of natural phenomenon or our limited knowledge of mathematics. Recently, data driven modeling (DDM) has been found to be a very powerful tool in helping to overcome those challenges, by presenting opportunities to build basic models from the observed patterns as well as accelerating the response of decision makers in facing real world problems. Since DDM is able to map causal factors and consequent outcomes from the observed patterns (experimental data), without deep knowledge of the complex physical process, these modeling techniques are becoming popular among engineers. Soft computing and statistical models are the two commonly employed data-driven models for predictive modeling. As far as the statistical data-driven models are concerned, these models could be employed in the life of modern engineering. But the accuracy and generalizability of these models is very poor. The soft computing data- driven modeling techniques have attracted the attention of many researchers across the globe to overcome the limitations of statistical methods. The statistical data-driven modeling techniques such as least-squares methods, the maximum likelihood methods and traditional artificial neural network (ANN) are based on empirical risk minimization (ERM) principle while the support vector machine (SVM) method is based on the structural risk minimization (SRM) principle. According to it, the generalization accuracy is optimized over the empirical error and the flatness of the regression function or the capacity of SVM. On the other hand, the ANN and other traditional regression models which are based on ERM principle minimize the empirical error and do not consider the capacity of the learning machines. This results in model over fitting i.e. high prediction accuracy for the training data set and low for the test (unseen) data, giving poor generalization performance. SVMs belong to the supervised machine learning theory and are applied to both nonlinear classification called support vector classification (SVC) and regression or SVR. SVM possesses many advantages over traditional neural networks.
Machine Learning And Hybrid Modelling For Reaction Engineering
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Author : Dongda Zhang
language : en
Publisher: Royal Society of Chemistry
Release Date : 2023-12-20
Machine Learning And Hybrid Modelling For Reaction Engineering written by Dongda Zhang and has been published by Royal Society of Chemistry this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-20 with Science categories.
The Development Of Data Driven Methods For Modelling And Optimisation Of Chemical Process Systems
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Author : Max Mowbray
language : en
Publisher:
Release Date : 2023
The Development Of Data Driven Methods For Modelling And Optimisation Of Chemical Process Systems written by Max Mowbray and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Applications Of Artificial Intelligence In Process Systems Engineering
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Author : Jingzheng Ren
language : en
Publisher: Elsevier
Release Date : 2021-06-05
Applications Of Artificial Intelligence In Process Systems Engineering written by Jingzheng Ren and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-05 with Technology & Engineering categories.
Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering
Advanced Data Analysis And Modelling In Chemical Engineering
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Author : Denis Constales
language : en
Publisher: Elsevier
Release Date : 2016-08-23
Advanced Data Analysis And Modelling In Chemical Engineering written by Denis Constales and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-23 with Technology & Engineering categories.
Advanced Data Analysis and Modeling in Chemical Engineering provides the mathematical foundations of different areas of chemical engineering and describes typical applications. The book presents the key areas of chemical engineering, their mathematical foundations, and corresponding modeling techniques. Modern industrial production is based on solid scientific methods, many of which are part of chemical engineering. To produce new substances or materials, engineers must devise special reactors and procedures, while also observing stringent safety requirements and striving to optimize the efficiency jointly in economic and ecological terms. In chemical engineering, mathematical methods are considered to be driving forces of many innovations in material design and process development. Presents the main mathematical problems and models of chemical engineering and provides the reader with contemporary methods and tools to solve them Summarizes in a clear and straightforward way, the contemporary trends in the interaction between mathematics and chemical engineering vital to chemical engineers in their daily work Includes classical analytical methods, computational methods, and methods of symbolic computation Covers the latest cutting edge computational methods, like symbolic computational methods
Development Of Multirate Data Driven Models For Chemical And Biological Processes
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Author : Jingwei Gan
language : en
Publisher:
Release Date : 2019
Development Of Multirate Data Driven Models For Chemical And Biological Processes written by Jingwei Gan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Data Driven And Model Based Methods For Fault Detection And Diagnosis
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Author : Majdi Mansouri
language : en
Publisher: Elsevier
Release Date : 2020-02-05
Data Driven And Model Based Methods For Fault Detection And Diagnosis written by Majdi Mansouri and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-05 with Technology & Engineering categories.
Data-Driven and Model-Based Methods for Fault Detection and Diagnosis covers techniques that improve the quality of fault detection and enhance monitoring through chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with a review of relevant literature, proceeds with a detailed description of developed methodologies, and then discusses the results of developed methodologies, and ends with major conclusions reached from the analysis of simulation and experimental studies. The book is an indispensable resource for researchers in academia and industry and practitioners working in chemical and environmental engineering to do their work safely. Outlines latent variable based hypothesis testing fault detection techniques to enhance monitoring processes represented by linear or nonlinear input-space models (such as PCA) or input-output models (such as PLS) Explains multiscale latent variable based hypothesis testing fault detection techniques using multiscale representation to help deal with uncertainty in the data and minimize its effect on fault detection Includes interval PCA (IPCA) and interval PLS (IPLS) fault detection methods to enhance the quality of fault detection Provides model-based detection techniques for the improvement of monitoring processes using state estimation-based fault detection approaches Demonstrates the effectiveness of the proposed strategies by conducting simulation and experimental studies on synthetic data
Modeling And Simulation In Chemical Engineering
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Author : Christo Boyadjiev
language : en
Publisher: Springer Nature
Release Date : 2021-12-08
Modeling And Simulation In Chemical Engineering written by Christo Boyadjiev and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-08 with Science categories.
This book presents a theoretical analysis of the modern methods used for modeling various chemical engineering processes. Currently, the two primary problems in the chemical industry are the optimal design of new devices and the optimal control of active processes. Both of these problems are often solved by developing new methods of modeling. These methods for modeling specific processes may be different, but in all cases, they bring the mathematical description closer to the real processes by using appropriate experimental data. In this book, the authors detail a new approach for the modeling of chemical processes in column apparatuses. Further, they describe the types of neural networks that have been shown to be effective in solving important chemical engineering problems. Readers are also presented with mathematical models of integrated bioethanol supply chains (IBSC) that achieve improved economic and environmental sustainability. The integration of energy and mass processes is one of the most powerful tools for creating sustainable and energy efficient production systems. This book defines the main approaches for the thermal integration of periodic processes, direct and indirect, and the recent integration of small-scale solar thermal dryers with phase change materials as energy accumulators. An exciting overview of new approaches for the modeling of chemical engineering processes, this book serves as a guide for the important innovations being made in theoretical chemical engineering.
Machine Learning In Chemistry
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Author : Edward O. Pyzer-Knapp
language : en
Publisher:
Release Date : 2020-10-22
Machine Learning In Chemistry written by Edward O. Pyzer-Knapp and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-22 with Science categories.
Atomic-scale representation and statistical learning of tensorial properties -- Prediction of Mohs hardness with machine learning methods using compositional features -- High-dimensional neural network potentials for atomistic simulations -- Data-driven learning systems for chemical reaction prediction: an analysis of recent approaches -- Using machine learning to inform decisions in drug discovery : an industry perspective -- Cognitive materials discovery and onset of the 5th discovery paradigm.
Dynamic Model Development Methods Theory And Applications
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Author : S. Macchietto
language : en
Publisher: Elsevier
Release Date : 2003-08-04
Dynamic Model Development Methods Theory And Applications written by S. Macchietto and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-04 with Technology & Engineering categories.
Detailed mathematical models are increasingly being used by companies to gain competitive advantage through such applications as model-based process design, control and optimization. Thus, building various types of high quality models for processing systems has become a key activity in Process Engineering. This activity involves the use of several methods and techniques including model solution techniques, nonlinear systems identification, model verification and validation, and optimal design of experiments just to name a few. In turn, several issues and open-ended problems arise within these methods, including, for instance, use of higher-order information in establishing parameter estimates, establishing metrics for model credibility, and extending experiment design to the dynamic situation. The material covered in this book is aimed at allowing easier development and full use of detailed and high fidelity models. Potential applications of these techniques in all engineering disciplines are abundant, including applications in chemical kinetics and reaction mechanism elucidation, polymer reaction engineering, and physical properties estimation. On the academic side, the book will serve to generate research ideas. Contains wide coverage of statistical methods applied to process modelling Serves as a recent compilation of dynamic model building tools Presents several examples of applying advanced statistical and modelling methods to real process systems problems