Application of LSTM and GRU neural networks to improve peristaltic pump dosing accuracy

Abstract

Peristaltic pumps (PP), widely acknowledged for their benefits in pharmaceutical contexts, face challenges in achieving optimal dosing accuracy. This investigation contributes novel insights for the improvement of dosing precision, identifying how to apply AI models, specifically focusing on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks over a realistic span of target volumes. To provide a more accurate representation of real-world performance, we consider a modified root mean square error metric ($RMS{E}_{PP}$) that directly compares dispensed volumes to target volumes. Based on this the study delves into two main methodologies: an iterative retraining method, called Online Training, and Pre-trained approach. Online Training shows best results, especially for volumes below 1.0 ml, achieving 38.4% improvement in $RMS{E}_{PP}$ and 31.6% in standard deviation ($STD$). Pre-trained models are faster and exhibit promising outcomes especially for volumes above 1.0 ml, with a three-features approach delivering the best performance (13.8% and 4.6% improvements in $RMS{E}_{PP}$ and $STD$, respectively). Overall, the findings highlight the effectiveness of iterative learning techniques, particularly for smaller dosage amounts, which complements the good performance of non-AI approaches for larger ones.