Special Issue: AI-Driven Innovations in Industrial Bioresource Engineering
Theme:
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the industrial bioresource engineering landscape, offering unprecedented opportunities to enhance efficiency, sustainability, and innovation across the entire biomass value chain. This special issue aims to capture cutting-edge research and applications at the intersection of AI and bioresource engineering, spanning smart feedstock management, advanced conversion processes, supply chain optimization, and sustainability assessment.
We invite contributions that demonstrate how AI can transform biomass cultivation, harvesting, characterization, and logistics to ensure consistent and sustainable industrial inputs. Research leveraging AI for thermochemical and biochemical conversion optimization—such as pyrolysis, gasification, fermentation, and enzymatic hydrolysis—will be emphasized, along with predictive modeling, real-time process control, and anomaly detection in bioenergy plants. The issue also welcomes works on digital twins and simulation platforms that integrate AI for real-time monitoring, predictive maintenance, and process optimization.
In addition, the special issue seeks papers on AI-driven life cycle assessment (LCA), carbon footprint modeling, waste valorization, and techno-economic evaluations that inform policy and market adoption of AI in biomass industries. Interdisciplinary studies proposing novel AI algorithms, hybrid models, or reinforcement learning strategies tailored for high-dimensional and uncertain biomass systems are highly encouraged.
By bringing together diverse contributions, this issue will provide a holistic view of how AI can accelerate the transition toward sustainable bioresource utilization, enhance operational reliability, and integrate bioenergy systems with smart grids for a low-carbon future.
Keywords: Artificial Intelligence, Machine Learning, Industrial Bioresource Engineering, Biomass Conversion, Bioenergy, Digital Twins, Supply Chain Optimization, Predictive Maintenance, Sustainability, Techno-Economic Analysis
Topics of Interest Include (but are not limited to):
- AI for biomass yield prediction
- ML in feedstock quality assessment
- Smart sensors for biomass monitoring
- AI in feedstock variability management
- Deep learning for pyrolysis optimization
- AIin gasification process control
- Predictive modeling of bio-oil yields
- Anomaly detection in combustion systems
- AI for fermentation kinetics
- Microbial community optimization with ML
- AI in enzymatic hydrolysis pathways
- Intelligent biorefinery integration
- AI-driven biomass logistics optimization
- Predictive inventory management for biomass
- AI in biomass transport routing
- Risk assessment in biomass supply chains
- Condition monitoring with AI in bioenergy plants
- Predictive maintenance scheduling
- Anomaly detection in bioenergy facilities
- AI-enabled decision support in operations
- Digital twins for biomass processes
- AI-enhanced process simulations
- Surrogate modeling for bioresource systems
- Agent-based AI simulation of biomass flows
- AI for life cycle assessment (LCA)
- Carbon footprint prediction with ML
- AI in waste valorization
- Techno-economic modeling of AI-enabled systems
- AI for policy and regulatory analysis
- AI-driven bioenergy smart grid integration
Guest Editor:
Sameer Neve, PhD, EIT
Affiliation: WSP USA & HydraEarth Network, USA
Email: ssameer.neve@gmail.com
Shikha Soneji, PhD(C)
Affiliation: Penn State University, USA
Email: sxs7000@psu.edu
Submission Information
Submit it online: https://ojs.wiserpub.com/index.php/IBE/user/register
Or send it to the email address: editorial-ibe@wiserpub.com
Submission Deadline
June 30, 2026.
Submission Guideline
https://ojs.wiserpub.com/index.php/ibe/about/submissions
For any inquiries about this Special Issue, please contact the Editors via editorial-ibe@wiserpub.com