Microbial bioprocessing offers a promising route to more sustainable production of proteins and functional ingredients for animal feed, including alternatives to protein sources such as soy that face environmental, nutritional, and supply-chain challenges. However, microbial production and scale-up remain largely empirical because organism performance depends on complex interactions between microbial characteristics, metabolism, substrate composition, and bioreactor conditions. Valuable information is generated during development, but historical datasets are rarely integrated into a framework that can support prediction, optimization, and knowledge transfer. This project will develop a historical digital twin of microbial bioprocesses using already collected experimental data from bacterial isolates cultivated in bioreactors. The dataset combines complementary biological and process information, including metabolomics, phenomics, microbial characteristics, and process parameters such as pH, dissolved oxygen, temperature, feeding conditions, and other bioreactor measurements. By integrating these heterogeneous data layers, the project will establish a data-driven representation of the relationships between microbial state, process conditions, metabolic responses, and production outcomes. The digital twin will use artificial intelligence and machine learning to reconstruct historical process behaviour and identify patterns associated with yield, product quality, growth, metabolic performance, and process stability. The objective is not only to predict outcomes, but to create a reusable knowledge framework in which new experimental data can be integrated and compared with previous processes. This will enable prediction of process performance under alternative cultivation conditions and support more informed decisions during bioprocess development. A central application will be microbial organism and process design. The digital twin will be used to identify combinations of microbial characteristics, substrates, media compositions, and process parameters associated with desirable production profiles. This can guide substrate and medium optimization, reduce experimental trial-and-error, and prioritize the most promising conditions for laboratory validation. Transfer learning will further allow knowledge learned from existing bacterial isolates and bioreactor experiments to be transferred to new isolates for which only limited experimental data are available. The project will also address bioprocess scale-up. Historical data from different reactor configurations and production scales will be used to learn how microbial performance changes with process conditions and reactor size. This will support more informed transitions from small-scale experiments to larger bioreactors and help identify conditions that maintain yield, quality, and process stability during scale-up. Ultimately, the digital twin will provide an AI-enabled foundation for accelerating microbial production of alternative proteins and healthy, sustainable animal-feed ingredients. By transforming historical experimental data into a reusable knowledge resource, the project will support yield and quality prediction, organism design, substrate and medium optimization, process optimization, transfer learning to new isolates, and scale-up. The approach will reduce dependence on purely empirical development and enable data-driven, more efficient and sustainable design of future microbial production processes.