Visual process monitoring of biomass conversion reactors using transfer learning and generative AI

Thermochemical reaction system, particularly in the form of small-scale, low-cost bioreactor, offers a promising solution for efficient biomass-to-bioproduct conversion. The bioreactor potentially revolutionizes biomass conversion and keep rural communities into the loop of biomass-based circular economy. However, its continuous operation remains a significant concern. A primary concern arises from the unexpected reaction condition, which is typically indicated by smoke emanating from the bioreactor. This expected issue can be resolved by minor adjustments to ensure proper reaction environment. In this regard, an automated visual process monitoring for smoke detection is crucial. This can be achieved by developing a convolutional neural network (CNN)-based smoke classifier. However, shuffled video-based smoke classification, where a model trained on video recordings from field experiments is applied to monitor new biomass reaction processes with unseen scenarios, poses great challenges due to limited number of field experiments, particularly given the diversity of field backgrounds. Considering the limited diversity issue, this study explore the potential of generative artificial intelligence (GenAI) to generate virtual training images with smoke and without smoke. With augmented training data, a tailored transfer learning strategy is applied to fine tune the CNN model for smoke detection. To assist field operators in understanding classifier decisions and correcting erroneous predictions, an explainable visual representation is provided by smoke localization heatmaps. The results show that the proposed method significantly improve smoke detection accuracy and prediction reliability, which is essential for the continuous operation of biomass conversion reactors and the success of decarbonization within biomass-based circular economy.