Multiobjective Harvest-Time Optimisation of Microalgal Biodiesel Production: Balancing Methyl-Ester Output, Conversion Yield and Cultivation Time

Harvest timing directly influences reactor occupancy, cultivation efficiency and downstream product performance, yet microalgal biodiesel studies frequently identify harvest points from individual production maxima rather than explicit multiobjective decision criteria. This study developed time-resolved harvest-decision framework for Chlamydomonas sp. OW5 and Euglena sp. OW7 by integrating cultivation duration, experimentally measured methyl-ester output and conversion yield. Fifteen candidate cultivation days were evaluated using Pareto-dominance screening, normalized distance-to-ideal compromise analysis, harvest-displacement penalties and exhaustive objective-weight sensitivity. Days 1–12 were Pareto-efficient for both strains, reflecting trade-offs between shorter cultivation and improved product performance, whereas Days 13–15 were dominated because prolonged cultivation increased process duration while reducing methyl-ester output and conversion yield. Equal-weight ideal-point analysis identified Day 12 as preferred compromise for both strains, with minimum distance of 0.4536. Harvesting one day earlier reduced cultivation duration by 8.33% but decreased methyl-ester output by 48.99% in Chlamydomonas and Euglena (50.97%), demonstrating substantial production penalty for limited time saving. Across 5,151 objective-weight combinations, Day 12 was selected in 71.7% and 70.3% of cases for Chlamydomonas and Euglena ; these frequencies increased to 81.8% and 80.8% within the balanced interior weight space. Strong prioritisation of cultivation time nevertheless shifted the preferred solution toward earlier harvest states. Despite quantitative differences in absolute methyl-ester output and conversion performance, both strains therefore converged on the same decision structure. Day 12 represents robust, preference-dependent and system-specific compromise rather than universal microalgal harvest optimum. The framework provides transferable process-engineering approach for distinguishing production maxima, Pareto-efficient alternatives and robust operational harvest targets.