While carbon fibers (CFs) are still the most attractive reinforcement material for lightweight structures, they are mostly manufactured using crude oil-based process chains. To achieve a higher eco-efficiency, the partial substitution of polyacrylonitrile (PAN) by renewable materials, such as lignin, is investigated. So far, this investigation has only been carried out for batch manufacturing studies, neglecting the transfer and validation to continuous CF manufacturing. Therefore, this work is the first to investigate the possibility of partial substituting lignin for PAN in a continuous process. Lignin/PAN-blended CFs with up to 15 wt.-% lignin were able to attain mechanical properties comparable to unmodified PAN-based carbon fibers, achieving tensile strengths of up to 2466 MPa and a Young’s Modulus of 200 Pa. In summary, this study provides the basis for continuous Lignin/PAN-blended CF manufacturing.

In carbon fiber (CF) production, the stabilization process step is the most energy- and time-consuming step in comparison with carbonization and graphitization. To develop optimization routes for energy and productivity, the stabilization needs to be monitored continuously via inline analysis methods. To prognose the evolution of high-performance CF, the density of stabilized fibers has been identified as a robust pre-indicator. As the offline analysis of density is not feasible for inline analysis, a density-soft sensor based on the stabilization indices of Fourier Transform Infrared spectrum (FTIR)-analysis and Electron Paramagnetic Resonance (EPR) Spectroscopy could potentially be used for inline monitoring. In this study, a Polyacrylonitrile-based precursor fiber (PF) stabilized in a continuous thermomechanical stabilization line with varying stretching profiles was incrementally analyzed using density, FTIR-based relative cyclization index (RCI), and EPR-based free radical concentration (FRC). Our findings show RCI and EPR dependencies for density, correlated for RCI with sensitivity by stretching to cubic model parameters, while FRC exhibits linear relationships. Therefore, this study identifies two possible soft sensors for inline density measurement, enabling autonomous energy optimization within industry 4.0-based process systems.