Monitoring of simultaneous saccharification and fermentation of ethanol by multi-source data deep fusion strategy based on near-infrared spectra and electronic nose signals

Fuel ethanol represents a future energy trajectory, and the simultaneous saccharification and fermentation (SSF) technique emerges as the principal approach for ethanol production. This scholarly inquiry offers an innovative means to monitor the SSF process for ethanol meticulously. Employing a profound fusion strategy that effectively amalgamates diverse data sources. The convolutional neural network and recurrent neural network (RNN) architectures are thoughtfully crafted and designed to enable autonomous feature self-learning from near-infrared spectra and electronic nose data. These intricately devised networks further implement data fusion strategies at the granular level of features. Ultimately, a deep fusion correction model was devised and rigorously validated using two distinct data sources, namely near-infrared spectroscopy and electronic nose data. The obtained results demonstrate a discernible improvement in the overall predictive accuracy of the model when employing the fusion feature strategy, surpassing the model constructed solely on a single technical data source. Regarding the monitoring of ethanol content, the optimal RNN fusion model exhibited remarkable performance metrics, with a root mean square error of prediction (RMSEP) value of 3.2265, a coefficient of determination (R2) value of 0.9880, and a relative percent deviation (RPD) value of 9.2662. In terms of monitoring glucose content, the optimal RNN fusion model also demonstrated commendable performance, with the following respective parameters: RMSEP was 3.2770, R2 was 0.9840, and RPD was 8.0085. The overall results indicate that the multi-sensor data fusion strategy not only improves the performance of the model but also provides valuable insights into the fermentation process.