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<p>Lignocellulose-based ethanol production remains commercially challenging due to biomass recalcitrance and high cellulase costs. This review first establishes the challenges within current techno-economic limits, then traces three analytical layers that must be solved in concert. A modelling layer couples genome-scale reconstructions with multi-omics data that builds the foundation. The engineering layer converts model-derived insights into robust genetic interventions. A decision layer applies data-efficient learning algorithms that generate sequence functions and process landscapes that guide experimentation based on information-rich variants. All layers feed an automated Design–Build–Test–Learn loop that stores genotypes, phenotypes, and processes data in a unified schema, so each iteration directly refines the next. Case studies spanning life-cycle assessment, multi-objective metabolic optimization, adaptive laboratory evolution, and AI-guided enzyme design illustrate progressive gains in titer, rate, and yield (TRY) approaching commercial thresholds. This review offers a clear route from laboratory insight to cost-competitive second-generation bioethanol.</p>

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Environmental Systems Engineering for Lignocellulosic Biorefineries Using AI and Genome-Scale Models

  • 期刊论文

  • 发表日期：

    2026年1月27日

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  • Yashika Raheja,
  • Vivek Kumar Gaur,
  • Satish Kumar Ainala,
  • Ajay Kumar,
  • Janmejai Kumar Srivastava,
  • Bhupinder Singh Chadha

摘要

Lignocellulose-based ethanol production remains commercially challenging due to biomass recalcitrance and high cellulase costs. This review first establishes the challenges within current techno-economic limits, then traces three analytical layers that must be solved in concert. A modelling layer couples genome-scale reconstructions with multi-omics data that builds the foundation. The engineering layer converts model-derived insights into robust genetic interventions. A decision layer applies data-efficient learning algorithms that generate sequence functions and process landscapes that guide experimentation based on information-rich variants. All layers feed an automated Design–Build–Test–Learn loop that stores genotypes, phenotypes, and processes data in a unified schema, so each iteration directly refines the next. Case studies spanning life-cycle assessment, multi-objective metabolic optimization, adaptive laboratory evolution, and AI-guided enzyme design illustrate progressive gains in titer, rate, and yield (TRY) approaching commercial thresholds. This review offers a clear route from laboratory insight to cost-competitive second-generation bioethanol.