AI-Enabled Waste Biorefinery: Valorization of Seafood & Agricultural Residues into High-Value Bioproducts

Aim

Workshop Objectives

Workshop Structure

Day 1: Seafood Waste Biorefinery — Protein, Chitin & Chitosan

• Seafood waste composition, characterization and valorization opportunities.
• Processing and pretreatment of fish, shrimp and shellfish residues.
• Protein recovery and production of protein hydrolysates.
• Shrimp-shell demineralization and deproteinization.
• Chitin extraction, purification and conversion to chitosan.
• Chemical, enzymatic and fermentation-based extraction approaches.
• Yield, mass balance and recovery of valuable co-products.

Hands-on: Develop and analyse a shrimp-shell waste → protein → chitin → chitosan process workflow using experimental/literature data, including yield and process-parameter calculations.

Day 2: Agricultural Residues — Biomass Fractionation & Bioproducts

• Composition of agricultural residues: cellulose, hemicellulose and lignin.
• Feedstock selection, preprocessing and pretreatment.
• Biomass fractionation and recovery of valuable components.
• Cellulose and nanocellulose as value-added products.
• Lignin valorization and biomass-derived products.
• Biochar and activated carbon from agricultural residues.
• Product properties and characterization using FTIR, XRD, SEM, BET and thermal analysis.

Hands-on: Analyse an agricultural-residue dataset and develop a biomass → cellulose/nanocellulose or biochar conversion workflow, relating process conditions to product yield and properties.

Day 3: AI-Driven Biorefinery & Process Optimization

• Role of AI and machine learning in waste valorization and biorefinery.
• Preparing experimental and process datasets for machine learning.
• Feature engineering and identification of important process variables.
• Predicting product yield and quality using regression models.
• Random Forest and Gradient Boosting for process-property prediction.
• Design of Experiments and Response Surface Methodology.
• AI-assisted process optimization and multi-objective decision-making.
• Basic techno-economic and sustainability considerations.
• Designing an integrated waste-to-value biorefinery.

Hands-on: Build an AI-assisted waste-to-value optimization workflow:

Feedstock → Process Conditions → ML Prediction → Optimization → Product Selection

Who Should Enrol?

Important Dates

Workshop Outcomes