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Updated: Apr 13, 2026
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Pretreatment of Lignocellulosic Biomass with Low-cost Ionic Liquids
Published on: August 10, 2016
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A comprehensive approach to optimize lignocellulosic biomass processing for cost-effective biofuel production
Ankit Agrawal1, Ranjana Sharma2, Anita Kaul Gupta3
1Department of Mathematics, DBS Global University, Dehradun, Uttarakhand, India.
Environmental Science and Pollution Research International
|April 11, 2026
Summary
Optimizing bioethanol production using corn stover, sugarcane bagasse, and miscanthus is crucial for renewable energy. The Social Group Optimization (SGO) algorithm effectively minimizes supply chain costs for sustainable bioethanol.
Area of Science:
- Renewable Energy Systems
- Biomass Conversion Technologies
- Supply Chain Optimization
Background:
- Growing global population necessitates advanced renewable energy solutions.
- Bioethanol from lignocellulosic biomass offers a sustainable alternative to fossil fuels.
- Efficient supply chain management is key to cost-effective bioethanol production.
Purpose of the Study:
- To optimize bioethanol production costs using a supply chain model.
- To evaluate the performance of the Social Group Optimization (SGO) algorithm against traditional methods.
- To identify key cost drivers in bioethanol supply chains.
Main Methods:
- Development of a mixed-integer linear programming (MILP) model for supply chain cost minimization.
- Application of the Social Group Optimization (SGO) algorithm for enhanced computational efficiency.
- Inclusion of feedstocks: corn stover, sugarcane bagasse, and miscanthus.
- Analysis of cost factors: feedstock procurement, production, transportation, and facility installation.
Main Results:
- The SGO algorithm achieved lower total supply chain costs compared to traditional methods.
- SGO demonstrated faster convergence, indicating improved computational efficiency.
- Feedstock procurement significantly impacts total supply chain costs, followed by production and installation costs.
Conclusions:
- The SGO-based framework provides a practical and cost-effective solution for bioethanol supply chain optimization.
- Strategic feedstock sourcing and production planning are vital for economic viability.
- Further research should incorporate uncertainty and environmental assessments for enhanced sustainability.
Keywords:
Agricultural residuesBioethanol supply chainCost minimizationRenewable energy systemsSensitivity analysisSocial group optimizationMore Related Videos
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