ENG. ANDREW OMWENGA
Chemical & Process Simulation Engineer • Thermodynamic Specialist
Optimizing Lactic Acid Fermentation: From Experimental Bioreactor Data to an Operating Strategy
Section 1.0 — Simulation & Facility Metadata
Section 2.0 — Executive Summary
A process-engineering study using experimental bioreactor data for Lactobacillus delbrueckii subsp. bulgaricus CFL1 to evaluate how temperature, pH, and harvest stage govern lactic acid titer, batch kinetics, and apparent volumetric productivity.
Section 3.0 — Problem Statement & Operating Bottlenecks
Industrial lactic acid biomanufacturing demands high volumetric productivity and final titer while minimizing batch duration and unreacted substrate. Traditional batch operations often operate under sub-optimal static conditions that prolong fermentation lag or early stationary phases. Process optimization requires rigorous kinetic analysis of experimental bioreactor data across temperature, controlled pH, and harvest points to establish an evidence-grounded operating strategy.
Section 4.0 — Objectives & Rigorous Simulation Methodology
Process engineering and kinetic modeling of experimental bioreactor data (L. delbrueckii CFL1). Systematic comparison of 4 experimentally tested conditions evaluating temperature (37°C vs 42°C) and controlled pH (4.8 vs 5.8). Apparent volumetric productivity calculated as P_v = Δ[Lactic Acid] / Δt_harvest across biological replicates (n=3) with standard deviation uncertainty bounds.
Section 5.0 — Simulation Results & Thermodynamic Findings
Shifting operating temperature from 37°C to 42°C accelerates microbial metabolic activity and enzyme turnover rates in L. delbrueckii CFL1, dramatically steepening the logarithmic growth curve. Maintaining pH at 5.8 prevents premature undissociated lactic acid self-inhibition, allowing the culture to achieve 22.79 g/L before entering the decline phase. Harvesting precisely at stationary phase (th3, 17.11 h) captures maximum accumulated product before cell lysis and byproduct formation occur.
Section 6.0 — Core Engineering Takeaways
Section 7.0 — Model Assumptions & Future Recommendations
Experimental data evaluated specific discrete test points (37°C, 42°C; pH 4.8, 5.8); continuous optimization across the full 38–44°C range requires fine-grid multi-bioreactor screening.
Dynamic fed-batch feeding strategy modeling in Aspen Plus / Python to sustain glucose concentrations above substrate limitation thresholds and continuous membrane cell-recycle fermentation.
Section 8.0 — Consultant Conclusion & Verification Sign-off
Data-driven kinetic analysis of experimental bioreactor data demonstrates that operating at 42°C and pH 5.8 with stationary-phase harvest represents the best-performing strategy among tested conditions, cutting cycle time by 38.9% and surging volumetric productivity 2.47-fold.
