Intelligent and Sustainable Cement Production

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editor
Contributors
Notation
Chapter 1: Contemporary Cement Plants: Scale, Complexity, and Operational Variables
1.1 Introduction
1.2 Scale and Scatter of Production
1.3 Complementary Role of Material Chemistry and Process Engineering
1.3.1 Key Features of Raw Materials Influencing the Process
1.3.2 Criticality of the Clinker-Making Stage
1.3.3 Material Chemistry in Clinker Grinding
1.4 Resource Efficiency and Material Flows in the Production Process
1.5 Thermal Energy Performance of the Kiln Systems
1.6 Cement Kilns for External Waste Management
1.7 Electrical Energy Performance
1.8 Pollutions and Emissions in Cement Manufacturing
1.9 Characteristic Features of Portland Cements
1.10 Modelling, Simulation, and Advances in Process and Quality Control Systems
1.11 Integration of Business, Management, and Production
1.12 Integrated Features of Contemporary Cement Plants
1.13 Concluding Observations
References
Chapter 2: Transforming Cement Manufacturing through Application of AI Techniques: An Overview
2.1 Preamble
2.2 Part 1: AI and Machine Learning Tools
2.2.1 Preliminaries
2.2.2 AI, Machine Learning, and Deep Learning: How Do They Differ?
2.2.2.1 Machine Learning
2.2.2.2 Deep Learning
2.2.2.3 How Does ML Work?
2.2.2.4 What Is a (Good) Algorithm?
2.2.3 The Machine Learning Implementation Process at the Developmental Level
2.2.3.1 Categorize the Problem
2.2.3.2 Understand and Clean the Data
2.2.3.3 Select the Best Algorithms and Optimize Them
2.2.4 The Machine Learning Implementation Process at Production Level
2.2.5 Is AI (or ML) a Future for Cement Manufacturing?
2.2.5.1 What Is Then the Potential of ML for the Cement Manufacturing Process?
2.3 Part 2: “AI” Inside the Cement Production Process
2.3.1 Main Components of a Cement Factory and Relevance of AI Applications
2.3.1.1 ML and Limestone Mining Operation
2.3.1.2 ML and Raw Mix Design
2.3.1.3 ML in Clinker Production
2.3.1.4 ML to Build a Free-Lime-in-Clinker Prediction Tool
2.3.2 Cement Grinding, Property Evaluation, and Hydration
2.3.2.1 Developing an ML Tool for Cement Compressive Strength and Setting Time Prediction
2.3.2.2 AI in the Laboratory: Semi-automatic Classification of Cementitious Materials Using Scanning Electron Microscope Images
2.3.3 ML in Field: An Example of Sound Analysis
2.4 Concluding Observations
2.5 Perspectives
Acknowledgments
References
Chapter 3: Process Automation to Autonomous Process in Cement Manufacturing: Basics of Transformational Approach
3.1 Introduction
3.2 Automation to Autonomy in Manufacturing: Basics and Approach
3.2.1 Steps Toward Achieving Autonomous Operation
3.2.2 Technological Imperatives for Transition
3.3 Expanding Concepts of Machine Learning
3.4 Current Process Control Infrastructure in Cement Plants
3.5 Advances in Process Control Strategy for Cement Manufacturing
3.6 Future Considerations in Proliferating APC Systems in Cement Manufacturing
3.7 Concluding Observations
References
Chapter 4: Electrical Systems for Sustainable Production in Cement Plants: A Perspective View
4.1 Introduction
4.2 Electrical Installations – Backdrop of Standards and Regulations
4.3 Power Supply and Receiving System
4.3.1 Incoming Voltage Considerations for Power Grid Supply
4.3.2 Approach for Determining the Power Requirements and Transformer Capacity
4.3.3 Selection of Transformers
4.4 Sourcing of Power
4.4.1 Power from Waste Heat Recovery Systems
4.4.2 Power from Renewables
4.5 Power Distribution System
4.5.1 Power System Design Considerations
4.5.2 Load Centre Substations
4.5.3 Voltage Selection for Power Distribution
4.5.4 MV Switchboards
4.5.5 LV Distribution Transformers
4.5.6 Main LV Distribution Boards
4.5.7 Intelligent Motor Control Centres
4.5.8 Variable Frequency Drives
4.5.8.1 LV Drives
4.5.8.2 MV Drives
4.5.8.3 Energy Losses Due to Harmonics
4.5.9 Earthing/Grounding
4.5.10 Battery with Charger
4.5.11 Power Factor Improvement
4.5.12 Plant Lighting
4.5.13 Power, Control, and Instrumentation Cables
4.5.14 Power Supply from an Emergency Generator
4.6 Energy Efficient Motors and Drives
4.6.1 Types of Motors
4.6.2 Protective Enclosure of Motors
4.7 Electrical Energy Conservation
4.8 Control, Automation and Information System for Power Distribution
4.8.1 Automation of Electrical Distribution System
4.9 Process Signal Communication, Integration and Automation
4.9.1 Communication Protocols
4.9.2 Industrial Wireless Communication
4.10 Advanced Process Control and Emergence of AI Techniques
4.10.1 AI Applications to Electrical Systems
4.11 Internet of Things and Data Processing Infrastructure
4.12 Concluding Observations
References
Chapter 5: Data-Driven Thermal Energy Management Including Alternative Fuels and Raw Materials Use for Sustainable Cement Manufacturing
5.1 Introduction
5.2 Description of the Thermal Process
5.3 Sustainability in Cement Production through the Use of Alternative Resources
5.4 Alternative Raw Materials for Pyroprocessing
5.4.1 Naturally Occurring Alternative Raw Materials
5.4.2 Industrial Waste as Alternative Raw Materials
5.5 Alternative Fuels for Pyroprocessing
5.6 Storing, Dosing, and Conveying of Alternative Fuels
5.7 Operational Considerations in Using Alternative Fuels
5.8 Adapting the Plant and Equipment to AF Combustion
5.8.1 Criteria for Selecting Firing Locations
5.8.2 Design Features of Pyroprocess Equipment
5.8.3 Rotary Drum Reactor for Burning Coarser Fuels
5.8.4 NOx Control Technologies
5.8.5 Process Instruments
5.9 Conventional Approaches for Process Optimization
5.9.1 Fuzzy Logic Control Philosophy
5.9.2 Model Predictive Control
5.9.3 Limitations of Conventional Automation Systems
5.10 Implementation Plan for Industry 4.0 Tools in Cement Plants
5.11 Integrated Robotic Laboratory for Quality Control
5.12 Advanced Process Control Systems Based on Artificial Intelligence
5.13 AI-Based APC for Thermal Process
5.13.1 Kiln Control Module
5.13.2 Calciner Module with Alternative Fuels Controller
5.13.3 Cooler Control Module
5.14 Evaluation and Implementation of Advanced Process Control System
5.15 Collaborative Operation in Data-driven Ecosystem
5.16 Concluding Observations
References
Chapter 6: Control of Cement Composition and Quality: Potential Application of AI Techniques
6.1 Introduction: Quality Control in Cement Plants
6.2 Quality Control Practices in Cement Manufacturing
6.3 Data Collection Methods for Cement Production Quality Control
6.3.1 Analytical Methods
6.3.2 Physical Methods
6.4 Quality Control Alongside the Process
6.4.1 Sampling Importance
6.4.2 Quarry and Raw Milling
6.4.3 Hot Meal and Clinker
6.4.4 Cement
6.5 Essence of Artificial Intelligence or Machine Learning
6.6 Relevance and Limitations of AI
6.7 Quality Deviations: Causes and Potential Use of ML
6.7.1 Materials From Own Quarries
6.7.2 Purchased Materials and Combustibles
6.7.3 Raw Mix
6.7.4 Raw Meal
6.7.5 Kiln Feed
6.7.6 Hot Meal
6.7.7 Fuel Preparation
6.7.8 Clinker
6.7.9 Dispatched Cement
6.8 Application of ANN to Final Product Quality
6.8.1 Strength Prediction: Traditional Statistics vs. Predictive ANN
6.8.2 Deviations in Ground Cement: Influence of SO 3 Level
6.9 Concluding Remarks
References
Chapter 7: Asset Performance Monitoring and Maintenance Management in Cement Manufacturing
7.1 Introduction
7.2 Basics of Asset Performance Approach in Industrial Environment
7.3 Practices of Technical Performance Monitoring in Cement Plants
7.3.1 Large Cement Manufacturing Groups
7.3.2 Small- and Medium-Sized Cement Groups
7.4 Key Assets in Cement Manufacturing and Their Performance Monitoring Aspects
7.4.1 Preheater with Precalciner
7.4.2 Rotary Kiln
7.4.3 Clinker Cooler
7.4.4 Refractory Lining
7.4.5 Bag Filters
7.4.6 Mill Separators
7.4.7 Process Fans
7.4.8 Power Transformers
7.4.9 Motors and Drives
7.4.10 Power Distribution System
7.4.11 Variable Frequency Drives
7.4.12 Gearbox
7.4.13 Belt Conveyor
7.4.14 Compressors
7.4.15 Process Pumps
7.4.16 Bucket Elevators
7.4.17 Couplings
7.4.18 Bearings
7.5 Current Monitoring Practices for Energy Efficiency Assessment
7.6 Recent Application of AI-Based Components for Asset Performance Monitoring
7.7 Advances in Maintenance Strategies and Practices
7.8 Near-Term Prospects
7.9 Concluding Observations
References
Annexures
Annexure 1. Limestone Crusher Section
Annexure 2. Raw Mill Section (VRM)
Annexure 3. Raw Mill (Ball Mill)
Annexure 4. Pyro Section
Annexure 5. Cement milling – Ball Mill
Annexure 6. Utilities
Annexure 7. Captive Power Plant – Process
Annexure 8. Captive Power Plant – Electrical
Chapter 8: Digital Twin and Its Variants for Advancing Digitalization in Cement Manufacturing
8.1 Introduction
8.2 History of the Digital Twin Concept
8.2.1 Manifestations of the Digital Twin Concept
8.2.2 Indicative Developmental Trends of Digital Twins as Reflected in a Set of Publications
8.3 Interlinking Digital Twins and Product Lifecycle
8.4 Adopting Digital Twin Technology in Manufacturing
8.4.1 Strategic Approach for Adoption
8.5 Functionality and Structural Configuration of Digital Twins in Manufacturing
8.5.1 Process and System Optimization
8.5.2 Tentative Structural Configuration of Digital Twins
8.6 Digital Twins Driving the Pilot Production Environment
8.6.1 Modelling Approach in the Pilot Facility
8.7 Relevance of Digital Twins for Cement Manufacturing
8.7.1 Information Density Consideration
8.7.2 Digital Twin Options in Cement Manufacturing
8.8 Technological Preparedness with Enabling Tools
8.8.1 Salient Technology Requirements
8.8.2 IoT Platforms and Enabling Tools for Digital Twins
8.9 Digital Twins for Learning and Training
8.10 Concluding Observations
References
Chapter 9: Developments in Application of Sensors to Sustainable Manufacturing of Cement
9.1 Introduction
9.2 Overview of Sensors Applications
9.2.1 Data Processing and Communication
9.2.2 Smart Sensors
9.3 Sensors Application in Cement Manufacturing
9.3.1 Location of Temperature Sensors
9.3.2 Clinker Cooler
9.3.3 Analysis and Monitoring of Gas Emissions
9.3.4 On-Stream Analysis of In-Process Solids
9.3.5 Sensors in Mining, Crushing and Pre-blending
9.4 Soft Sensors in Process Industry
9.4.1 Basic Design Approach for Soft Sensors
9.5 Soft Sensors for Cement Manufacture
9.6 Soft Sensor Development in the AI Environment
9.6.1 Data Collection and Processing
9.6.2 Telemetry Endpoint
9.7 Future Role of Soft Sensors in Intelligent Cement Production
9.8 Concluding Observations
References
Chapter 10: Integrated Enterprise Resource Planning in Sustainable Cement Production
10.1 Introduction
10.2 Underpinning Theories
10.2.1 Sustainable Business Development
10.2.2 Enterprise Resource Planning
10.2.3 Big Data and Predictive Analytics
10.2.4 Integrated Enterprise Resource Planning
10.3 Resources for Building BDPA Capability
10.3.1 Tangible Resources
10.3.2 Human Resources
10.3.3 Technical Skills
10.3.4 Management Skills
10.3.5 Intangible Resources
10.3.6 Data-Driven Culture
10.4 Developing IERP in Cement Industry
10.4.1 Sampling Design and Data Collection
10.4.2 Structural Self-Interaction Matrix
10.4.3 Final Reachability Matrix
10.4.4 Level Partitioning
10.4.5 Fuzzy MICMAC Analysis
10.4.6 Theoretical Model for IERP in the Cement Industry
10.5 Concluding Recommendations
References
Chapter 11: Implementation of Digital Solutions in Cement Process and Plants
11.1 Introduction
11.2 Digitalization Approach for Cement Plant – Mine to Packer
11.2.1 Smart Machines
11.2.2 Plant Control System
11.2.3 Process Optimization
11.2.4 Quality Management Systems
11.2.5 Plant/Enterprise Management Systems
11.2.6 IoT Platform Foundations
11.2.7 Connected Asset Insights
11.2.8 Connected Asset Health
11.2.9 Connected Operation
11.2.10 Connected People
11.2.11 Connected (Business) Process
11.2.12 Connected Innovation
11.3 Smart Machines and Digitalization of Plant Control Systems
11.3.1 Smart Machines
11.3.2 Essentials of Plant Control Systems
11.3.3 Upgrading the Existing Plant Control Systems
11.4 Process Optimization Solutions
11.4.1 Ball Mill Application
11.4.2 Multi-Fuel Application
11.4.3 Kiln and Cooler Application
11.4.4 Vertical Roller Mill Application
11.5 Quality Management Systems
11.5.1 Sampling and Transport
11.5.2 Sample Preparation
11.5.3 Sample Analysis
11.5.4 Laboratory/Quality Control Software
11.5.5 Quality Optimization Software
11.5.5.1 BlendExpert – Pile Control
11.5.5.2 BlendExpert – Mill
11.6 Automated Cement Laboratories
11.7 Plant/Enterprise Management Systems
11.7.1 Enterprise Asset Management System
11.7.2 Enterprise Resource Planning and Its Components
11.7.3 Management Information Systems
11.8 Foundation of IoT Platform
11.8.1 Edge Gateway or Field Agent
11.8.2 Cloud Platform
11.9 Connected Asset Insights
11.9.1 Assets Performance Insights
11.9.2 Exploratory Workspace
11.9.3 Connected Asset Health
11.9.3.1 Smart kiln – Online Condition Monitoring
11.9.3.2 Condition Monitoring Services for Vertical Roller Mills
11.9.3.3 Mill Case Studies
11.9.3.4 Filter Bag – Online Condition Monitoring
11.10 Connected Operation – Pyroprocessing Section
11.10.1 AI in Process Optimization
11.10.2 Predicting the Kiln Red Spot by Using Temperature Anomaly
11.10.3 Monitoring Kiln Coating Stability Index
11.11 AI-Enabled Intelligent Production Management System
11.12 Connected People
11.12.1 Remote Operations-Control Room
11.12.2 Remote Troubleshooting Support
11.13 Application of Drone Technology
11.14 Connected Business Process
11.14.1 Automated Diagnostic Solutions
11.14.2 Asset Performance Management
11.14.3 Remote Plant Operations
11.15 Connected Innovation
11.15.1 Digital Twin
11.15.2 Augmented Reality/Virtual Reality
11.16 Concluding Observations
Acknowledgments
Chapter 12: Technological Forecasting for Commercializing Novel Low-Carbon Cement and Concrete Formulations
12.1 Introduction
12.1.1 Portland Cement and CO 2 Emissions
12.2 Supplementary Cementitious Materials
12.2.1 Traditional Supplementary Cementitious Materials
12.2.2 Non-Traditional or Alternative Supplementary Cementitious Materials
12.3 Alternative Non-Clinker-Based Binders
12.3.1 Alkali-Activated Binders (Geopolymers)
12.3.2 Magnesium Carbonate-Based Cements
12.3.3 C-S-H-Based Binder (Celitement ®)
12.3.3.1 Performance and Durability of Celitement
12.4 Alternative Clinker-Based Binders
12.4.1 Belite-Rich Cement
12.4.2 Sulfoaluminate Belite Cement
12.5 Calcium Silicate Cement and Concrete: Solidia Technologies
12.5.1 Energy Savings
12.5.2 Carbon Dioxide Emissions Reductions
12.5.3 Industrial Production
12.5.4 Curing Process of Solidia Concrete
12.5.5 Performance and Durability of Solidia Concrete
12.5.6 Application and Performance in Pavers
12.6 Direct Utilization of CO 2 in Concrete
12.6.1 CarbonCure Technologies
12.6.2 Calera Cement/Blue Planet Aggregate
12.7 Comparative SWOT Analysis
12.8 Concluding Remarks
References
Epilogue
Index