Digital Twins and Artificial Intelligence (AI) Modeling for Industrial Fermentation and Bioprocess Control - Okpabi, Thankyou, Saturday; Chukwudi, Eze Franklin; Uchendu, Anaesthesia chekwube; Ugochi, Udensi Justina; Njoku-Obi, Treasure Njideka (American Journal of Biology and Natural Sciences, 2026)
Industrial fermentation processes are often limited by low efficiency, poor real-time control, and high resource wastage due to inadequate monitoring of key process variables. This study aimed to develop and evaluate a digital twin–assisted AI framework integrated with multi-omics data for optimizing microbial fermentation processes. Laboratory-scale fermentation experiments were conducted using a 5 L stirred-tank bioreactor equipped with IoT-enabled sensors to continuously monitor temperature, pH, dissolved oxygen, agitation speed, substrate concentration, and biomass growth. Data were collected from three microbial systems (Saccharomyces cerevisiae, Lactobacillus plantarum, and Aspergillus niger), preprocessed through cleaning, normalization, interpolation, and feature selection, and used to develop ANN, LSTM, Random Forest, and SVM models. Whole genome sequencing, transcriptomics (RNA-seq), proteomics (LC-MS), and metabolomics (GC-MS/HPLC) were integrated with process data using data fusion and dimensionality reduction techniques. The digital twin system was trained using 70:15:15 data splitting and validated using k-fold cross-validation. Results showed high predictive accuracy (R² = 0.91–0.97) with low error margins (RMSE ≤ 0.18–0.32) and real-time prediction latency below 10 seconds. The system enabled continuous monitoring and automatic adjustment of fermentation parameters, resulting in improved biomass and metabolite yields, reduced waste, and a 10–25% increase in productivity. Multi-omics integration further enhanced model accuracy, robustness, and biological interpretability. In conclusion, the integration of digital twin technology, AI models, and multi-omics data provides a highly efficient framework for optimizing industrial fermentation processes. It is recommended that bioprocess industries adopt digital twin systems, advanced AI models, and multi-omics integration alongside IoT-enabled infrastructure to improve productivity, reduce waste, and enhance process control.