Digitalization and Analytics for Smart Plant Performance
Theory and Applications
Inbunden, Engelska, 2021
2 559 kr
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Fri frakt för medlemmar vid köp för minst 249 kr.This book addresses the topic of integrated digitization of plants on an objective basis and in a holistic manner by sharing data, applying analytics tools and integrating workflows via pertinent examples from industry. It begins with an evaluation of current performance management practices and an overview of the need for a "Connected Plant" via digitalization followed by sections on "Connected Assets: Improve Reliability and Utilization," "Connected Processes: Optimize Performance and Economic Margin " and "Connected People: Digitalizing the Workforce and Workflows and Developing Ownership and Digital Culture," then culminating in a final section entitled "Putting All Together Into an Intelligent Digital Twin Platform for Smart Operations and Demonstrated by Application cases."
Produktinformation
- Utgivningsdatum2021-04-20
- Mått10 x 10 x 10 mm
- Vikt454 g
- SpråkEngelska
- Antal sidor544
- FörlagJohn Wiley & Sons Inc
- EAN9781119634034
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Frank (Xin X.) Zhu, PhD, is Senior Engineering Fellow and Leader of Engineering Innovations at Honeywell UOP. He has made significant contributions to the theoretical framework, the computational tools, and applications in the fields of data analytics, production planning, operation scheduling, process modeling, design and optimization. He has published seminal journal articles and widely used books especially in the areas of process operations, process design and energy systems. He holds 68 US patents, is the co-founder and chair of ECI International Conference: CO2 Summit, and the recipient of prestigious AICHE Energy Sustainability Award.
- Preface xiiiAcknowledgments xviiPart 1 Challenges and Opportunities For Digitalization 11 Challenges for Operation Excellence 31.1 Introduction 31.2 Operation Activities in a Process Plant 41.3 The Major Challenges Facing the Industries 51.4 The Methodology of Connected Plant 111.5 Digitalization Enabling Connected Plant 121.6 What is the Digitalization Journey? 181.7 Overview of the Book Structure 19References 212 Mission of Connected Plant 232.1 What is Connected Plant? 232.2 Major Functions of Connected Plant 242.3 Digital Twins: The Core of Connected Plant 272.4 Conclusions 32References 333 Data Analytics for Operation Excellence 353.1 Introduction 353.2 Process Data Overview: Characteristics and Attributes 373.3 Unique Attributes of Process Data Analytics 393.4 Model Types and Characteristics 403.5 First Principle Modeling and its Characteristics 423.6 Statistic Modeling and its Characteristics 453.7 Optimization Models 473.8 Artificial Intelligence (AI) and Machine Learning (ML) Models 503.9 Put All Together: Digital Twin as a Data Science Platform 55References 59Part 2 Model Thinking For Smart Operations 634 Statistics Basics 654.1 Introduction 654.2 Normal Distribution 654.3 Conditional Probability 724.4 Bayes’ Probability 734.5 Statistic Tests 75References 845 Advanced Statistic Modeling 855.1 Introduction 855.2 Distribution Models 855.3 Correlation Models 945.4 Advanced Modeling Techniques 1015.5 Data Mining 1065.6 Summary 107References 1076 Rigorous Process Modeling 1096.1 Introduction 1096.2 Reaction Kinetic Modeling 1106.3 Reactor Types and Modeling 1266.4 Integrated Kinetics and Reactor Modeling 1316.5 Catalyst Deactivation Root Causes and Modeling 1356.6 Distillation Modeling 1366.7 Process System Modeling and Simulation 1386.8 Separation Technology Overview 142References 1447 Linear Optimization Modeling 1477.1 Introduction 1477.2 Linear Optimization for Planning 1487.3 How to Deal with Nonlinear Terms? 1517.4 Delta Vector as Linear Approximation of Nonlinear Yield Models 1547.5 Successive Linear Programing (SLP) Approach 159References 1608 Nonlinear Optimization Modeling 1618.1 Introduction 1618.2 Successive Quadratic Programming (SQP) Approach 1628.3 Local Versus Global Optimum 1628.4 Optimality Conditions 1668.5 Nonlinear Process Optimization Model 1678.6 Stochastic Programming 1718.7 Simulation-Based Optimization 1788.8 A Case Study for Process Optimization 1808.9 Concluding Remarks 188References 1909 Process Control and APC Modeling 1939.1 Introduction 1939.2 Process Modeling in Control 1949.3 Regulatory Control: Managing Individual Variables 2079.4 PID Controller Modeling 2119.5 Advanced Process Control (APC) 221References 23310 AI and Machine Learning Modeling 235Amit Gupta and Frank (Xin X.) Zhu10.1 Introduction 23510.2 Artificial Neural Networks 23510.3 Key Concept in ML: Perceptron 23810.4 Machine Learning 24210.5 Ml Applications in the Process Industry 246References 248Part 3 Connected Plant For Smart Operations 25111 Connected Metering and Measurements 253Martin Bragg11.1 Introduction 25311.2 Review of Metering Devices 25411.3 Connected Metering 25811.4 Positive-Unexpected Consequences of the Digital Economy 26711.5 The Outlook for Connected Metering 26911.6 Conclusions 273References 27412 Connected Asset and Safety Management 275Frank (Xin X.) Zhu and Tony Downes12.1 Introduction 27512.2 Review of Different Maintenance Strategies 27612.3 The Concept of Operating Windows 28012.4 The Major Gaps in Current Asset Management 28312.5 Digitalized Asset Management 28412.6 Process Safety Management 29012.7 Case Study: APM Drives Capacity Improvement 299Reference 30113 Integrated Production Planning and Process Control 30313.1 Introduction 30313.2 Current Practice in Site-Wide Optimization and Control 30413.3 Simultaneous Approach for Site-Wide Optimization and Control 30413.4 General Decomposition Strategy 30913.5 MPC-Based Integration Approach 31413.6 Rigorous Model-Based Integration Approach 32213.7 Comparison Between the MPC and Rigorous Model-Based Approaches 324References 32514 Digitalizing the Energy Management 32714.1 Introduction 32714.2 The Concept of Energy Intensity 32814.3 Energy Benchmarking for Processes 33714.4 The Concept of Key Indicators 34014.5 Set Up Targets for Key Indicators 34614.6 Economic Evaluation for Key Indicators 35014.7 Site-Wide Energy Management Strategy 35414.8 Digital Twin for Energy Management 36014.9 Establishing Energy Management System 361References 36515 Integrating the Workflows 367Frank (Xin X.) Zhu and Joe Ritchie15.1 Introduction 36715.2 Key Elements of Industrial Supply Chain 36815.3 Little Integration of Supply Chain Work Processes 38115.4 Gaps Existing in Current Supply Chain Management 38215.5 Integrated Work Process for Supply Chain Management 38315.6 Supply Chain Digital Twin: One Platform for Workflow Integration and Automation 38515.7 Integration of Engineering Models with Supply ChainDigital Twin 387References 38816 Digitalizing the Workforce 389Rohan McAdam16.1 Introduction 38916.2 Enabling the Workforce 39016.3 Empowering the Workforce 39816.4 Digitalization Challenges 41016.5 Summary 416References 416Part 4 Digital Solutions For Smart Operations 41917 Honeywell Forge: The Platform for Connected Plant 421Matt Burd and Frank (Xin X.) Zhu17.1 Honeywell Forge: A Digital Platform for Connected Plant 42117.2 IIoT for Data Infrastructure 42117.3 How It Works? 42317.4 Intelligent Models Behind Digital Twins in Honeywell Forge 42917.5 Cybersecurity 434Reference 43618 Digital Reediness Assessment and Six-Step Digitalization Journey 43718.1 Introduction 43718.2 Digital Readiness Assessment 43818.3 The Six-Step Digitalization Journey 44918.4 Recommendations: A Digital Transformation Management System 45418.5 Establishing a Digital Transformation Management System 455References 45719 Digital Project Evaluation and Development 45919.1 Introduction 45919.2 Business Case Evaluation 45919.3 Digital Project Development Steps 46119.4 Remarks on Digital Project Development 46519.5 S-Curve for Project Review and Management 46919.6 Basics of Economic Analysis 472Reference 47520 Application Case Studies 47720.1 Introduction 47720.2 Application Cases from Digital Twins 47720.3 Applications from Other Digital Projects 481References 506Index 507