Intelligent and Sustainable Cement Production : Transforming to Industry 4.0 Standards

This book captures the path of digital transformation that the cement enterprises are adopting progressively to elevate themselves to ‘Industry 4.0’ level. Digital innovations-based Internet of Things (IoT) and Artificial Intelligence (AI) are pertinent technologies for the cement enterprises as the manufacturing processes operate at very large scales with multiple inputs, outputs, and variables, resulting in the essentiality of big data management. Featuring contributions from cement industries worldwide, it covers various aspects of cement manufacturing from IoT, machine learning and data analytics perspective. It further discusses implementation of digital solutions in cement process and plants through case studies. Features: Present an up-to-date, consolidated view on modern cement manufacturing technology, applying new systems. Provides narration of complexity and variables in modern cement plants and processes. Discusses evolution of automation and computerization for the manufacturing processes. Covers application of ERP techniques to cement enterprises. Includes data-driven approaches for energy, environment, and quality management. This book aims at researchers and industry professionals involved in cement manufacturing, cement machinery and system suppliers, chemical engineering, process engineering, industrial engineering, and chemistry. 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 "This book captures the path of digital transformation that the cement enterprises are adopting progressively to elevate themselves to 'Industry 4.0' level. Digital innovations-based Internet of Things (IoT) and AI are pertinent technologies for the cement enterprises as the manufacturing processes operate at very large scales with multiple inputs, outputs, and variables, resulting in the essentiality of big data management. Featuring contributions from cement industries worldwide, it covers various aspects of cement manufacturing from IoT, Machine Learning and Data analytics perspective. It further discusses implementation of digital solutions in cement process and plants through case studies. Features: Present an up-to-date, consolidated view on modern cement manufacturing technology, applying new systems. Provides narration of complexity and variables in modern cement plants and process. Discusses evolution of automation and computerization for the process. Covers application of ERP techniques to cement enterprises. Includes data-driven approaches for energy, environment, and quality management. This book aims at Researchers and Industry professionals involved in Cement manufacturing, Cement Machinery and System Suppliers, Chemical Engineering, Process Engineering, Industrial Engineering, and Chemistry"-- Provided by publisher