Abstract
<title>Abstract</title> <p> Sustainable Aviation Fuel (SAF) derived from agricultural residues via anaerobic digestion (AD) offers a promising pathway for aviation decarbonisation. However, current research is characterised by fragmented optimisation approaches focusing on isolated process stages− pretreatment, digestion, upgrading, or synthesis− rather than integrated system performance. This study presents a novel six-layer integrated optimisation framework for AD-based SAF production that connects feedstock characterisation, pretreatment decision-making, AD process optimisation, advanced hybrid modelling, system-level design, and sustainability evaluation. The framework was developed through quantitative analysis of engineering performance data from 33 studies (methane yield:29.7–312 mL CH <sub>4</sub> /g VS; yield improvements: 41.6–209%) combined with qualitative synthesis of optimisation methodologies. Key innovations include (i) integration of energy and cost penalties within the pretreatment decision layer; (ii) hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) optimisation enhine achieving predictive accuracy of R <sup>2</sup> = 0.974–0.981; (iii) Mixed-Integer Linear Programming (MILP) for system-level configuration; and (iv) embedded Techno-Economic Analysis and Life Cycle Assessment within optimisation loop. Application of the framework demonstrated significant performance improvements: methane yield enhancement up to 20% under optimised conditions, emissions reductions of 77%, and carbon-negative operation (-4.58 g CO <sub>2</sub> e/MJ). The framework addresses critical limitation in existing literature multi-objective optimisation that simultaneously considers yield, cost, and environmental performance. This integrated approach provides a decision-support tool for engineers and policymakers, facilitating the transaction from laboratory-scale optimisation to industrial deployment of AD-based SAF systems. </p>