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Author: Zaidi Salim
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 44
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Book Description
In this study, a new multivariate method to monitor continuous processes is developed based on the Process Control Analysis (PCA) framework. The objective of the study is to develop A new MSPM method and analyze the monitoring performance of system A and B. In industrial practice, monitoring process are usually performed based on an approximate model. As the number of variables increases, the fault detection performance tends to be slow in progression, as well as, introduce greater complexity in the later stages especially in fault identification and diagnosing. These research implements and analyzes Multiple Linear Regression (MLR) method to a continuous process which simplify the number of variables used. This research also based on the conventional MSPM technique. After that, the developed method was analyzed and finally, all the performance result of the developed method was compared with the conventional method. The monitoring results clearly demonstrate the superiority of the proposed method. The MLR methods show that the fault detection performance improved and better than the conventional method.
Author: Zaidi Salim
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 44
Get Book Here
Book Description
In this study, a new multivariate method to monitor continuous processes is developed based on the Process Control Analysis (PCA) framework. The objective of the study is to develop A new MSPM method and analyze the monitoring performance of system A and B. In industrial practice, monitoring process are usually performed based on an approximate model. As the number of variables increases, the fault detection performance tends to be slow in progression, as well as, introduce greater complexity in the later stages especially in fault identification and diagnosing. These research implements and analyzes Multiple Linear Regression (MLR) method to a continuous process which simplify the number of variables used. This research also based on the conventional MSPM technique. After that, the developed method was analyzed and finally, all the performance result of the developed method was compared with the conventional method. The monitoring results clearly demonstrate the superiority of the proposed method. The MLR methods show that the fault detection performance improved and better than the conventional method.
Author: Fouzi Harrou
Publisher: Elsevier
ISBN: 0128193662
Category : Technology & Engineering
Languages : en
Pages : 330
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Book Description
Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches – such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches – to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems. - Uses a data-driven based approach to fault detection and attribution - Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems - Familiarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methods - Includes case studies and comparison of different methods
Author: Xiangyu Kong
Publisher: Springer Nature
ISBN: 981998775X
Category :
Languages : en
Pages : 324
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Book Description
Author: Ch. Venkateswarlu
Publisher: Elsevier
ISBN: 0323900682
Category : Technology & Engineering
Languages : en
Pages : 400
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Book Description
Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimators with a special emphasis on their development and applications to process monitoring, fault diagnosis and control. The design and analysis of different state estimators are highlighted with a number of applications and case studies concerning to various real chemical and biochemical processes. The book starts with the introduction of basic concepts, extending to classical methods and successively leading to advances in this field. Design and implementation of various classical and advanced state estimation methods to solve a wide variety of problems makes this book immensely useful for the audience working in different disciplines in academics, research and industry in areas concerning to process monitoring, fault diagnosis, control and related disciplines. - Describes various classical and advanced versions of mechanistic model based state estimation algorithms - Describes various data-driven model based state estimation techniques - Highlights a number of real applications of mechanistic model based and data-driven model based state estimators/soft sensors - Beneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas
Author: Geoffrey Vining
Publisher: CRC Press
ISBN: 1482276763
Category : Business & Economics
Languages : en
Pages : 504
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Book Description
Demonstrates ways to track industrial processes and performance, integrating related areas such as engineering process control, statistical reasoning in TQM, robust parameter design, control charts, multivariate process monitoring, capability indices, experimental design, empirical model building, and process optimization. The book covers a range o
Author: Manuel E. Peña-Rodríguez
Publisher: Quality Press
ISBN: 1953079083
Category : Technology & Engineering
Languages : en
Pages : 117
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Book Description
The concept of process monitoring and improvement applies to any type of industry: automotive, textiles, food, pharmaceuticals, biologics, medical devices, electronics, aerospace, banking, educational institutions, service providers, and so on. The focus of this book is to identify and apply different process monitoring and improvement tools in any organization. This book is aimed at engineers, scientists, analysts, technicians, managers, supervisors, and all other professionals responsible to measure and improve the quality of their processes. Many times, these professionals do not have a formal education on the use of these tools but learn about them throughout the different improvement projects in which they are involved in their work environment. This book is intended to fill the gap between the lack of formal education in the tools and the need to implement those tools in an improvement project. The book can also be used as a refresher course for those professionals who did learn about these tools as part of their educational background.
Author: R. Berber
Publisher: Springer Science & Business Media
ISBN: 9401101353
Category : Technology & Engineering
Languages : en
Pages : 814
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Book Description
Model based control has emerged as an important way to improve plant efficiency in the process industries, while meeting processing and operating policy constraints. The reader of Methods of Model Based Process Control will find state of the art reports on model based control technology presented by the world's leading scientists and experts from industry. All the important issues that a model based control system has to address are covered in depth, ranging from dynamic simulation and control-relevant identification to information integration. Specific emerging topics are also covered, such as robust control and nonlinear model predictive control. In addition to critical reviews of recent advances, the reader will find new ideas, industrial applications and views of future needs and challenges. Audience: A reference for graduate-level courses and a comprehensive guide for researchers and industrial control engineers in their exploration of the latest trends in the area.
Author: Marcos Quiñones-Grueiro
Publisher: Springer Nature
ISBN: 3030547388
Category : Technology & Engineering
Languages : en
Pages : 153
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Book Description
This book examines recent methods for data-driven fault diagnosis of multimode continuous processes. It formalizes, generalizes, and systematically presents the main concepts, and approaches required to design fault diagnosis methods for multimode continuous processes. The book provides both theoretical and practical tools to help readers address the fault diagnosis problem by drawing data-driven methods from at least three different areas: statistics, unsupervised, and supervised learning.
Author: Ludovic Montastruc
Publisher: Elsevier
ISBN: 032395880X
Category : Technology & Engineering
Languages : en
Pages : 1760
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Book Description
32nd European Symposium on Computer Aided Process Engineering: ESCAPE-32 contains the papers presented at the 32nd European Symposium of Computer Aided Process Engineering (ESCAPE) event held in Toulouse, France. It is a valuable resource for chemical engineers, chemical process engineers, researchers in industry and academia, students and consultants for chemical industries who work in process development and design. - Presents findings and discussions from the 32nd European Symposium of Computer Aided Process Engineering (ESCAPE) event
Author: Sylvia Fruhwirth-Schnatter
Publisher: CRC Press
ISBN: 0429508247
Category : Computers
Languages : en
Pages : 522
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Book Description
Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.
