Abir Chakravorty

Food Engineering Automation with Robotics and AI

• Gebundenes Buch

Produktbeschreibung

Revolutionize food manufacturing with the latest in automating technology Virtually every area of industry has been transformed by robotics and AI, which have automated production and increased efficiency in myriad ways. Until recently, food manufacturing was an exception to the trend. At present, however, the food manufacturing industry is in the process of a transformation which will see automation deliver the same levels of productivity and uniformity that have revolutionized other sectors of the economy. Food Engineering Automation with Robotics and AI is a comprehensive introduction to the areas of intersection between cutting-edge technologies and food manufacturing. Beginning with an overview of the basic principles of food engineering, the book then details applications of robotics and AI in this field, along with the way automation is integrated at every stage of food production. The structure of the book seamlessly blends theory and practice to maximize reader capacity to put its lessons into motion. Food Engineering Automation with Robotics and AI readers will also find: * Content aligning with several UN Sustainable Development Goals, including Zero Hunger; Industry, Innovation, and Infrastructure; and Responsible Consumption and Production * Real-world case studies throughout to show automating technologies revolutionizing food production * A consistent focus on sustainable food engineering, with attention to resource conservation, waste reduction, environmental impact mitigation, and more Food Engineering Automation with Robotics and AI is ideal for the growing, global market for food automation technologies in the coming years.

Produktdetails

• Verlag: John Wiley & Sons Inc
• Seitenzahl: 496
• Erscheinungstermin: 22. Oktober 2025
• Englisch
• ISBN-13: 9781394316564
• ISBN-10: 1394316569
• Artikelnr.: 72073429

Autorenporträt

Abir Chakravorty, PhD, M.Tech, works in the field of automation, robotics, and food equipment machinery design in the Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur. He received his PhD from the Department of Mechanical Engineering, IIT Kharagpur. He has three patents filed, one granted, fifteen and more journals, and a book published in his name.

Inhaltsangabe

About the Book xix
Preface xxi
Acknowledgments xxiii
About the Author xxv
Acronyms xxvii
Introduction xxix
1 Robotics and Automation in Food Processing, Developments Prompted by the
Present Era Industrial Revolution 1
1.1 Abridgement of the Existing Obstacles and Opportunities Presented by
the Present Era Industrial Revolution in the Food Processing Sector 1
1.1.1 Current Challenges in Food Processing 2
1.1.2 Technologies in Agricultural Automation 2
1.2 Adoption of Technology in Food Processing and Key Industry Technologies
3
1.2.1 Technologies in the Present Era Food Processing 3
1.3 Definition of Robot and Its Various Classification 4
1.3.1 Automation and Its Various Types 4
1.3.2 Overview of Industry 4.0 in Food Processing 7
1.3.3 Driver for Adoption 8
1.3.4 Challenges 9
1.3.5 Adoption Outcomes 9
1.3.6 Future Directions 10
1.4 Use of Robotics in Food Processing 10
1.4.1 Key Benefits of Robotics in Food Processing 10
1.4.2 Application of Robotics 10
1.4.3 Types of Robots Used 11
1.4.4 Challenges and Considerations 11
1.5 Use of Smart Sensors in Food Processing 11
1.5.1 Data Processing and Control 12
1.5.2 Environmental Monitoring 12
1.5.3 Benefits of Smart Sensors 12
1.6 Applications of AI in Food Processing 13
1.6.1 Case Study 1: Dairy Processing Plant 15
1.6.2 Case Study 2: Meat Processing Facility 15
1.6.3 Key Technologies 15
1.6.4 Implementation Action Steps 15
1.6.5 Compliance and Reporting 15
1.7 Homogeneous Transformation Matrices 16
1.8 Denavit-Hartenberg Parameters 24
1.9 Degrees of Freedom 25
1.10 Applications of IoT in Food Processing 26
1.10.1 Reviewing IoT Implementation in FSCs 26
1.10.2 Benefits of Integration of IoT in FSCs 27
1.10.3 Case Study: Simulation of a Brewery 28
1.10.4 Challenges and Considerations 28
1.11 Prediction Models in Measuring Color and Food Quality 28
1.11.1 Introduction to Color Measurement of Food Quality 28
1.11.2 Color Measurement with Predictive Models 28
1.11.3 Performance of Prediction Models 29
1.11.4 Challenges and Concerns 30
1.12 AI/ ML Prediction Modeling in Food Processing and Food Safety 30
1.12.1 Introduction 30
1.13 AI/ ML Prediction Modeling in Food Processing and Food Safety 31
1.14 Case Studies 32
1.15 Review Questions 33
Bibliography 34
2 Automation in Food Processing Applications 37
2.1 Automatic Control of Food Chilling and Freezing 39
2.1.1 Installation of IoT Sensors 39
2.1.2 Data Acquisition and Transmission 39
2.1.3 Data Preprocessing and Analysis 39
2.1.4 Model Training and Prediction 39
2.1.5 Alerting System 39
2.1.6 Continuous Improvement 40
2.1.7 Future Improvements 41
2.2 Introduction: Main Forces Behind and Obstacles to Automated Food
Freezing and Chilling Control 41
2.2.1 Advanced Sensor Technology 41
2.2.2 Automations 41
2.2.3 Energy Management Techniques 41
2.3 Challenges to Automation in Food Chilling and Freezing Include 42
2.4 Automation in Refrigerated Food Retail Display 42
2.5 Automation of Refrigeration and Freezing Operations in Food Catering 44
2.5.1 Main Findings 44
2.5.2 Flow of the Automation Process in Temperature Control System 44
2.6 Automation in Refrigerated Food Transport Systems 45
2.7 Automation in Food Chilling and Freezing Systems 47
2.7.1 Introduction to Smart Freezing Technology 47
2.7.2 Freezing Technology Applications 47
2.7.3 Challenges and Innovations 47
2.7.4 Quality and Nutritional Value 48
2.8 Automation in Food Cold Storage Systems 48
2.9 Advances in Research and Future Trends 49
2.10 Case Study 50
2.11 Review Questions 52
Bibliography 52
3 Robot Types and Their Classification 55
3.1 Categories of Robots 56
3.2 Design of IoT-based Pick-and-place Robot 57
3.2.1 Design of an Affordable IoT Open-Source Robot Arm for Online Teaching
57
3.3 Direct Kinematics 57
3.4 Inverse Kinematics 60
3.5 Inverse Kinematics of a T hree-degrees-of-freedom Spatial Manipulator
with a Unique Geometry 60
3.5.1 Workspace Analysis 63
3.5.2 Inverse Kinematics 64
3.6 Splines in Robotics 65
3.6.1 Linear Splines 66
3.6.2 Cubic Splines 67
3.7 Automation and Its Types 70
3.7.1 Levels of Automation 71
3.8 Basic Image Processing Techniques 73
3.8.1 Binary Image 73
3.8.2 Gray Scale Image 74
3.8.3 RGB Color Image 74
3.8.4 RGBA Image 74
3.8.5 Image Acquisition 74
3.8.6 Image Enhancement 75
3.8.7 Image Restoration 75
3.8.8 Color Image Processing 75
3.8.9 Wavelets and Multi-resolution Processing 75
3.8.10 Image Compression 75
3.8.11 Morphological Processing 75
3.8.12 Image Segmentation 76
3.8.13 Representation and Description 76
3.8.14 Object Detection and Recognition 76
3.8.15 Knowledge Base 76
3.9 Robot Kinematics 78
3.9.1 Forward and Inverse Kinematics for a 6-DoF Manipulator 84
3.10 AI/ML Basics for Food Processing 92
3.10.1 Artificial Neural Network 92
3.10.2 Solved Example A 100
3.10.3 Solved Example B 103
3.10.4 Convolutional Neural Network 104
3.11 Mapping, Navigation, and Obstacle Avoidance 105
3.12 Fuzzy Logic Control Case Study in Food Processing 107
3.13 Dijkstra's Algorithm for a Grid Map 108
3.14 Path Planning with the A* Algorithm 109
3.14.1 What Is A* Search Algorithm? 110
3.14.2 Why Do We Use the A* Search Algorithm? 110
3.14.3 Understanding the A* Search Algorithm 111
3.15 Path Following and Obstacle Avoidance 113
3.16 Fuzzy Logic Control 114
3.16.1 Advantages of Fuzzy Logic Systems 116
3.16.2 Problem with Fuzzy Logic Systems 116
3.16.3 Applications 116
3.17 Robotic Motion and Odometry 117
3.17.1 Key Concepts in Robotic Motion Kinematic Modeling 117
3.17.2 Motion Control 117
3.17.3 Implementation of Kinematics and Odometry Support 117
3.17.4 Significance of Odometry Definition 117
3.17.5 Data Acquisition 118
3.17.6 Performance 118
3.17.7 Future Work 118
3.18 Gripper: Its Various Types and Applications 119
3.18.1 Based on the Motion of the Gripper Jaws 119
3.18.2 Gripper Type 2: Number of Fingers 121
3.18.3 Gripper Type 3: Internal or External 121
3.18.4 Other Types of Grippers 121
3.19 Robotics and Automation for Packaging and Handling 129
3.19.1 Mobile Robotics and Packaging Optimization Deliveries 129
3.19.2 Robotic Grasping and Control Strategies 132
3.20 Review Questions 132
Bibliography 134
4 AI/ML and Robotics Applications in Food Safety, Quality, and Challenges
137
4.1 Food Safety and Quality Detection Using Robotics and AI/ML 139
4.1.1 Explanation of the Workflow 141
4.2 Automation of Fruit and Vegetable Sorting, Grading 142
4.3 Prediction Models in Measuring Color and Food Quality 144
4.3.1 TTI Fruit Quality Model 145
4.4 Data Management and Its Importance 145
4.5 Ethics and Safety 145
4.5.1 AI in Food Safety Surveillance 146
4.5.2 Ethical Issues 146
4.6 Adoption of Automation in Food Process Engineering 146
4.6.1 Effect on Food Processing 146
4.7 Automation, Economic Return, Food Manufacturing Sector, Increased
Safety Assurance, and Quality of Products 147
4.8 AI, Robotics, and Automation for UN Sustainable Development Goals 147
4.8.1 Function of AI in Monitoring and Evaluation 147
4.8.2 Overcoming Global Challenges 148
4.8.3 Ethical Considerations 148
4.9 AI in Food Supply Chain, Tracing/ Warehouse/ Distribution 150
4.9.1 AI Applications in Food Supply Chain Management 150
4.9.2 Challenges and Considerations 150
4.10 Data Analysis, Discrete and Continuous Data on Classification and
Regression 150
4.10.1 Classification Techniques Decision Trees 150
4.11 Case Studies 151
4.11.1 Introduction 151
4.11.2 Objective 151
4.11.3 Methodology 151
4.11.4 Summary of Key Findings 151
4.11.5 Challenges 152
4.12 Review Questions 153
Bibliography 154
5 Application of Robotics in Processing Meat, Fish, and Poultry 155
5.1 Introduction 155
5.2 Application of Robotics in Processing Meat, Fish, and Poultry 156
5.2.1 Utilizing Computer Vision and Robotics for Fish Processing 160
5.3 Robotic Manipulator in Food Processing 188
5.3.1 Review of Different Options 188
5.3.2 Innovative Design of Robots 189
5.3.3 Kinematic Analysis and Workspace 191
5.3.4 Inverse Kinematic Analysis 193
5.4 End Effectors in Food Industry 195
5.4.1 Robotic Grippers for Large and Soft Object Manipulation 195
5.4.2 A Robotic Gripper with Scooping and Binding Capabilities for Managing
Diverse Food Items 197
5.4.3 Beam Model Analysis 201
5.4.4 Determining Binding Force Estimation 202
5.5 Robotic and Vision Systems in Use for Fish Processing 204
5.5.1 Handling, Sorting, and Inspection in Food Industry 210
5.5.2 Materials and Methods 212
5.5.3 AI and IoT Applications in Food Processing Case Studies 222
5.5.4 Case Study: Real-time Sorting of Broiler Chicken Meat 225
5.5.5 Case Study: Fish-robot-based IoT Platform 226
5.5.6 3D Swimming Model for the FishBot 227
5.6 Review Questions 229
Bibliography 230
6 Principles of Fuzzy PID and Its Algorithm 233
6.1 Fresh Food Atmosphere Packaging Gas Distribution System Utilizing Fuzzy
PID Control 233
6.1.1 System Structure 234
6.1.2 Performance Evaluation 234
6.1.3 Key Take-aways 234
6.1.4 Case Study: Fresh Food Gas Distribution Systems Improvement 234
6.1.5 Principle of Fuzzy PID 236
6.1.6 Fuzzy Linguistic Variables and Fuzzy Design 238
6.2 A Fuzzy-PID Controller for Temperature Control in a Shell and Tube Heat
Exchanger Simulation 238
6.2.1 Mathematical Model 239
6.2.2 Fuzzy PID Controller Simulation Analysis 241
6.2.3 PID Control of Milk Pasteurization 244
6.3 An Introduction to Automated Process Control in the Food Sector Using
Supervisory Control and Data Acquisition and Associated Systems 249
6.3.1 Overview and History of SCADA 250
6.3.2 SCADA Standards and Applications 250
6.4 Introduction to SCADA 250
6.4.1 Main Elements of SCADA 250
6.4.2 Applications of SCADA 251
6.5 SCADA and Its History 251
6.5.1 Historical Development 1960s-1970s: First Industrial Process Control
Introduction; Initial "SCADA" Terminology Initiated 251
6.5.2 SCADA Components and Functionality 251
6.6 SCADA Standards and Applications 252
6.7 SCADA in Food Processing 252
6.8 Laboratory Study: Implementation of SCADA 253
6.9 Future Trends in SCADA 254
6.9.1 Case Study 254
6.10 Review Questions 258
Bibliography 258
7 Robots, Ladder Elements, Logic Gates, 3D Food Printing, SCADA, Automation
Pyramid in Food Processing 259
7.1 3D Printing Technology: The New Era for Food Customization and
Elaboration 259
7.1.1 Why Print Food? Are There Any Benefits? 262
7.1.2 Novel Food Structuring Using a Broad Range of Alternative Food
Ingredients 262
7.2 Environmentally Friendly and Sustainable Technology 265
7.2.1 Promoting Higher Social Binding Through Food Messaging 265
7.2.2 3D Food Printing Technology 266
7.3 Autonomous Mobile Robots 266
7.3.1 Humanoid Robots 269
7.3.2 Educational Robot 271
7.3.3 The Generic Robot 273
7.3.4 Automation Pyramid 274
7.3.5 Difference Between RTU, PLC, and SCADA, HMI Case Studies 275
7.3.6 Ladder Elements, Logic Gates in PLC Programming 280
7.3.7 Fiber Optic Sensors and Refractometry 283
7.3.8 Sensors in IoT 284
7.3.9 What Is 3D Printing? 287
7.3.10 Case Studies, Retrofitting Process Automation 288
7.4 Review Questions 291
Bibliography 291
8 PID Control Techniques in Food Processing 293
8.1 Process Control Methods in the Food Industry 294
8.1.1 Methods of Process Control in the Food Sector 295
8.2 Industrial PID Control 299
8.2.1 Case Study: PID Control in Industrial Processes Introduction 299
8.2.2 Industrial Process Dynamics 299
8.2.3 Tuning PID Controllers 299
8.2.4 Applications of PID Control 300
8.3 Variations of PID Controller Algorithms 303
8.3.1 Process Loop Issues-A Summary Checklist 305
8.3.2 Case Study: Shell Heavy Oil Fractionator (Nonlinear Process Modeling)
305
8.4 Three-term Control, or Proportional, Integral, or Derivative Control
306
8.4.1 Elements of Control 306
8.5 Parallel PID Controllers in Food Processing 307
8.5.1 General Structure for Parallel Cascade Control and Corresponding IMC
Structure 307
8.5.2 Development of a Nonlinear PID Controller and Adjustment Guidelines
for First-order Plus Time Delay Systems 322
8.5.3 Series PID Controllers in Food Processing 339
8.5.4 Simple PID Tuning in Food Industry 340
8.5.5 PID Controller Implementation Issues 342
8.5.6 Bandwidth-limited Derivative Control 343
8.5.7 Proportional and Derivative Kick 345
8.5.8 Reverse-acting Controllers 346
8.5.9 Industrial PID Control 348
8.5.10 Traditional Industrial PID Terms 349
8.5.11 Industrial PID Structures and Nomenclature 350
8.5.12 The Process Controller Unit 350
8.5.13 Supervisory Control and the SCADA PID Controller 352
8.5.14 Case Study 353
8.6 Review Questions 359
Bibliography 360
9 Online Spectroscopy Techniques to Access Food Quality and Safety, SCADA
and HMI Examples 363
9.1 AI/ML Techniques for Food Safety Case Studies 364
9.1.1 Support Vector Machine 367
9.1.2 Decision Tree 367
9.1.3 ML Algorithm Validation 368
9.1.4 Risk Prediction Models Using ML 369
9.1.5 The RF Algorithm 371
9.1.6 The Structure of the MC-RF Model 374
9.1.7 The Evaluation Index of the Food Safety Risk Prediction Model 377
9.1.8 Early Warning Analysis 379
9.2 Sensors for Automated Food Process Control 381
9.2.1 Methods Adopted 381
9.2.2 Capacitance Measurements 383
9.2.3 Sensor Characterization 384
9.2.4 Applications of Sensors in Automated Food Process Control 390
9.2.5 Robotics and Automation for Food Packaging and Handling 395
9.2.6 Trends in Packaging and Materials, Active Packaging, Material
Handling Systems 399
9.2.7 Controls, Supervisory Control, Online Instrumentation, and Data
Acquisition 401
9.2.8 Nondestructive Inspection 406
9.2.9 Multispectral, Hyperspectral, and X-ray-based Systems 409
9.2.10 Optical Sensors and Online Spectroscopy for Automated Quality and
Safety Inspection of Food Products 410
9.2.11 Optical Sensing and Spectroscopic Techniques 412
9.2.12 Spectroscopic Techniques, IR Spectroscopy, Raman Spectroscopy, and
NIR Spectroscopy 413
9.2.13 Applications in Food Industry 415
9.2.14 Applications of Image Sensing Technology 416
9.2.15 Case Studies 417
9.3 Review Questions 420
Bibliography 421
10 Case Studies 425
10.1 Modeling Simulation and Practical Application of Robotics in Food
Manufacturing Different Simulation Scenario 426
10.1.1 Case Study: Automation in Aerosol Can Packaging 426
10.2 Robotic Cooking 430
10.2.1 Case Study: Gustoso the Intelligent Project 430
10.3 Robotic Arm Control and Task Training Through Deep Reinforcement
Learning 431
10.3.1 Case Study: Robotic Arm Control and Task Training Through Deep
Reinforcement Learning Overview 431
10.3.2 Pick and Place 431
10.4 Sensors for Automated Food Process Control 433
10.4.1 Case Study: Human-following Control in Agricultural Robots 433
10.4.2 Velocity Command Based on Mixed MF 434
10.5 Indirect Teaching of the Robot Hand 435
10.5.1 Control System Principle and Design 435
10.6 Tuning Humanoid Walking Parameters for Food Service Robots for Better
Performance 437
10.6.1 Case Study: Adaptive PID Control Algorithm for the Two-legged Robot
Walking on a Slope 437
10.6.2 Modeling and Design of Controller of the Biped Robot 438
10.7 Sensorized Compliant Robot Gripper for Estimating the Cooking Time of
Boil-cooked Vegetables 439
10.7.1 The Mechanical Vegetable Model 439
10.7.2 Kinematic Model of Vegetable 440
10.8 Multiple Object Detection and Segmentation for Automated Removal in
Additive Manufacturing with Service Robots 441
10.8.1 Case Study: Service Robot Used to Automate Object Removal in 3D
Printing 441
10.8.2 Methodology 442
10.8.3 Results and Test 442
10.9 Miscellaneous Case Studies 442
10.9.1 Case Study: An Integrated Soft Robotic System for Handling
Heterogeneous Objects 443
10.10 Review Questions 446
Bibliography 446
Appendix: List of Suppliers for Sensors and Automation Equipment 449
Index 455