Hydrogen prediction in poultry litter gasification process based on hybrid data-driven deep learning with multilevel factorial design and process simulation: A surrogate model

Keyphrases

• Design Simulation 100%
• Process-based 100%
• Hybrid Data 100%
• Process Simulation 100%
• Gasification Process 100%
• Surrogate Model 100%
• Poultry Litter 100%
• Multilevel Factorial Design 100%
• Data-driven Deep Learning 100%
• Syngas 75%
• Convolutional Neural Network 75%
• Gasification 50%
• Aspen Plus Simulation 50%
• Response Surface Model 25%
• Statistical Analysis 25%
• Performance Prediction 25%
• Hydrogen Content 25%
• Process Optimization 25%
• Coefficient of Determination 25%
• Optimal Process Parameters 25%
• Response Surface Methodology 25%
• Mean Square Error 25%
• Particle Swarm Optimization 25%
• Mean Absolute Error 25%
• Energy Waste 25%
• Particle Swarm Optimization Algorithm 25%
• Higher Heating Value 25%
• Air Ratio 25%
• Syngas Production 25%
• Waste Management Practices 25%
• Lower Heating Value 25%
• High-quality Syngas 25%
• Hydrogen Level 25%
• Energy Production Management 25%
• Process Inputs 25%
• Research Integration 25%

Engineering

• Process Simulation 100%
• Gasification 100%
• Surrogate Model 100%
• Deep Learning Method 100%
• Syngas 80%
• Convolutional Neural Network 60%
• Particle Swarm Optimization 40%
• Potential Application 20%
• Input Parameter 20%
• Energy Production 20%
• Process Parameter 20%
• Response Surface 20%
• Square Mean 20%
• Response Surface Method 20%
• Surface Model 20%
• Air Ratio 20%
• Hydrogen Level 20%
• High Heating Value 20%
• Syngas Generation 20%
• Low Heating Value 20%
• Optimization Technique 20%
• Mean Square Error 20%
• Mean Absolute Error 20%

Chemical Engineering

• Deep Learning Method 100%
• Gasification 100%
• Neural Network 60%
• Aspen Plus Simulation 40%
• Syngas Generation 20%