Ensemble Methods for Peristaltic Pump Accuracy Enhancement

This study investigates how ensemble learning techniques can be employed for enhancing peristaltic pump accuracy in pharmaceutical manufacturing, and demonstrates significant accuracy improvements through the novel E-AR implementation, with gains of up to 53.93% at 0.3 ml volume compared to 47% achievable with single models. To establish the foundation for ensemble methods evaluation, we first conduct a comprehensive validation of traditional Adaptive Dosing Control System (ADCS) across an extended volume range (0.1-2.0 ml), demonstrating base performance improvements. In this investigation, we develop a novel offline performance indicator enabling rapid assessment of compensation strategies without extensive physical testing, showing strong correlation with actual measurements. These premises enable a thorough investigation of various ensemble configurations, revealing volume-dependent performance patterns where different models excel under specific conditions, suggesting that practical applications may benefit from volume-specific model selection. The comparison with a very accurate reference mechanical pump, demonstrates that our ADCS solutions achieve comparable or superior performance across most volumes while maintaining the cost-effectiveness. Statistical validation via a multi-dimensional framework confirms the significance of these improvements through multiple complementary tests: paired t-tests showing significant mean differences with p $\leq 0.001$ , Mann-Whitney U tests confirming distributional shifts, Levene tests demonstrating variance modifications with statistics up to 801.65, and mixed linear model analysis with F-statistics ranging from 0.004 to 1497.75 confirming global effects.