Bespoke Models for Real-Time On-Site (Demo/Flag-Scale) Compositional Analysis of Feedstocks and Process Outputs - with Customised Software Development

We have extensive expertise in the development of algorithms and models for rapidly predicting the composition of samples using their near infrared spectra. This allows the time for analysis (for detailed lignocellulosic compositional paramaters) to be reduced from weeks to seconds. To date we have over 10,000 biomass samples in our proprietary models for lignocellulosic composition and we have also developed custom models for feedstocks and pretreatment/bioprocess outputs in the CBE-JU/BBI-JU RIA projects BIOrescue and UNRAVEL. In BIOrescue we developed custom software, employing a browser interface for the user, that allowed us to tailor the model-generation and composition-prediction experience to the requirements of our lab personnel and in-house chemometricians. This software also employed advanced chemometric techniques that we proved, in project deliverables, to deliver improved accuracies in prediction over the conventional PLS approaches.

This software is constantly being improved and, in the ongoing CBE-JU/BBI-JU Innovation Action (TRL7) project VAMOS we are deploying an upgraded version of it at the demo-scale biorefinery being built and operated by project partner Fiberight. This has allowed us to extend the reach of our predictive models beyond our own laboratories and into the global biorefinery landscape. We see many opportunities, within the current CBE-JU topics, to apply and refine this at-line analysis system to other IA (demo) projects as well as for Flagship projects. In addition, we also plan for the deployment of in-line analyses, using Celignis-developed models, using the latest state-of-the-art and cost-effective hardware on the market.

While there are numerous options currently on the market for NIR hardware, there is no robust solution currently available for the at-line/in-line analysis of lignocellulosic feedstocks and process outputs/residues. This is due to the complexity of such analysis, the difficulties often faced in getting precise and representative data, abd the widely varying outputs of different biomass processing technologies. At Celignis, however, we have years of expertise in getting precise lignocellulosic data and in using these data as inputs to our chemometric tools. As a result Celignis provides full vertical integration regarding advancing the art in rapid biomass analysis and the utilisation of biorefinery data.

Our abilities to develop on-site rapid analysis solutions are applicable to many of the Innovation Action and Flagship topics of the 2022 CBE-JU Work Programme, including: JU-CBE-2022-IA-04 (Co-processing of mixed bio-based waste streams), JU-CBE-2022-IA-02 (Cooperative business models for sustainable mobilisation And valorisation of agricultural residues, by-products, and waste in rural areas), JU-CBE-2022-IA-03 (Cost-effective production routes towards bio-based alternatives to fossil-based chemical building blocks), JU-CBE-2022-IAFlag-01 (Maximum valorisation of sustainably sourced bio-based feedstock in multi-product, zero-waste, zero-pollution biorefinery), and JU-CBE-2022-IAFlag-02 (Alternative sources for high added value food and/or feed ingredients).