A review of autonomous experimentation in lignocellulosic bioethanol production: Self-driving laboratory strategies for process optimization
Year: 2026
Type: article
Abstract: Lignocellulosic bioethanol production remains commercially constrained by the high-dimensional, nonlinear complexity of biomass conversion processes and the inadequacy of traditional Design of Experiments (DoE) approaches in navigating them. Autonomous experimentation platforms or self-driving laboratories (SDLs) address this gap by integrating robotic execution, real-time analytics and machine learning (ML) into closed-loop systems that adaptively select and optimize experiments with minimal human intervention. Unlike static DoE methods, these platforms efficiently explore enzyme combinations, process parameters, and fermentation conditions simultaneously, generating high-quality, reproducible data at a fraction of the experimental cost. This review critically examines the latest developments in autonomous experimentation for bioethanol process optimization, evaluating the performance, trade-offs, and bioprocess-specific applicability of key decision engines including Bayesian optimization (BO), reinforcement learning, and active learning. It further assesses current implementation challenges, from hardware constraints and data noise to scale-up barriers and identifies future directions centred on digital twins, hybrid modelling, and sustainability-integrated optimization. By linking ML - based decision-making directly to the challenges of lignocellulosic conversion, this review repositions autonomous experimentation as a foundational shift rather than merely a technical advance in how next-generation biorefineries will be designed and operated for sustainable intelligent bioethanol production.
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7203545850