Objective

  • Improve fermentation process visibility by using HPLC to monitor critical parameters related to ethanol performance, bacterial contamination, yeast stress and residual sugar conversion
  • Enable faster and more targeted corrective actions by establishing a data-driven detect → act → verify approach for identifying and addressing fermentation deviations
  • Quantify and sustain process improvements by evaluating the impact of interventions across 42 fermentation batches, covering multiple feedstocks and yeast strains over a six-week study period

Plant Profile

Parameter Detailsn
Plant capacity 300 KLPD
Location North India
Duration 6 weeks
Fermentation batches analysed 42
Feedstocks evaluated FCI Rice, DFG Rice and Maize
Yeast strains 2 strains
Monitoring technique HPLC

The Challenge

  • Limited process visibility: Conventional endpoint titration provided only the final fermentation result, making it difficult to identify the underlying causes of yield variation
  • Root causes were difficult to distinguish: Bacterial contamination, yeast stress and incomplete sugar utilisation could contribute to fermentation losses, but their individual impact was not clearly identifiable
  • Corrective action lacked specificity: Without clear biochemical insights, interventions could be generic and slow, creating a need for data-driven diagnosis and a detect → act → verify approach to fermentation control

HPLC Monitoring Solution

  • Ethanol: Monitored overall fermentation performance; target endpoint 13.5–14.5%
  • Lactic Acid: Indicated bacterial contamination; target <0.50%, with an alarm above 0.80%
  • Glycerol: Indicated yeast stress and associated fermentation losses; target <1.00%
  • DP1–DP4 Sugars: Tracked residual unconverted sugars; lower levels indicated better sugar utilisation and conversion efficiency

Intervention & Process Optimisation

  • Contamination Detection & Correction: HPLC identified elevated lactic acid in four consecutive FCI batches (0.91–0.99%), exceeding the0.80% alarm threshold. A targeted antibacterial protocol was introduced, and a subsequent contamination event linked to stored maize feedstock was detected and addressed—establishing a Detect → Correct → Verify loop
  • Yeast Stress Identification & Optimisation: Glycerol monitoring provided an early indicator of yeast stress. Optimisation of fermentation temperature resulted in a reported 9% reduction in glycerol, demonstrating how HPLC enabled corrective action before stress translated into significant yield loss
  • Residual Sugar Reduction: DP1–DP4 monitoring showed improved sugar utilisation as fermentation conditions were optimised, with residual DP sugars decreasing from 0.30% to 0.22%, a 27% reduction, indicating better conversion efficiency and reduced potential yield loss

Performance Highlights

Parameter Initial / First 7 Batches Remaining Batches Change
Lactic acid 0.92% 0.62% 35% reduction
Glycerol 1.09% 0.99% 9% reduction
Residual DP1–DP4 sugars 0.30% 0.22% 27% reduction

Quantified Yield Impact

Loss Component Additional Ethanol Recovery
Lactic acid reduction ≈ 4,825 L/day
Glycerol reduction ≈ 1,230 L/day
DP sugar reduction ≈ 742 L/day
Total Recovery ≈ 6,798 L/day
Overall Improvement ≈ 0.31% v/v additional ethanol production volume.

Key Outcomes

  • Improved Contamination Control: 35% reduction in lactic acid through targeted intervention, indicating better control of bacterial contamination
  • Reduced Yeast Stress & Improved Sugar Utilisation: 9% reduction in glycerol and 27% reduction in residual DP1–DP4 sugars, demonstrating improved fermentation efficiency
  • Ethanol Performance Maintained: Endpoint ethanol remained within or around the 13.5–14.5% target range during FCI and DFG, with 23 of 28 batches meeting or slightly exceeding the target
  • Quantified Ethanol Recovery: The combined improvements delivered a reported +0.31% v/v ethanol uplift, equivalent to approximately 6,798 L/day of additional ethanol

Conclusion

  • HPLC provided clear root-cause visibility by differentiating fermentation losses associated with bacterial contamination, yeast stress and incomplete sugar utilisation, enabling the plant to take targeted corrective actions
  • The monitoring programme translated analytical insights into measurable fermentation and yield improvements by supporting better contamination control, reduced yeast stress and improved utilisation of fermentable sugars
  • Most importantly, HPLC evolved from a periodic analytical technique into an operational control system, establishing a closed-loop Detect → Correct → Verify approach across subsequent fermentation batches