Target-oriented robust optimization for scheduling microalgal cultivation and harvesting for biofuel production

Microalgae is regarded as one of the most viable bioenergy feedstocks due to its ability to produce substantial amounts of essential components for generating various biofuels, while requiring only a limited amount of water and land area because of its high photosynthetic efficiency. However, technical challenges hinder large-scale biofuel production, making it economically unviable for commercial use. One significant challenge lies in its cultivation process, as the cultivation method and system are crucial in determining both the quantity and quality of the biofuel produced. Furthermore, peak biomass concentration and productivity for various compounds and nutrients in microalgae do not align at the same time, are unpredictable, and prone to variability. This study proposes a planning algorithm for microalgae cultivation systems that integrates species selection, cultivation, harvesting strategies, and scheduling under growth rate uncertainty, aiming to maximize profit. The functionality of this decision support system is illustrated through a hypothetical case study, with scenario analysis performed to understand system performance under varying conditions. Moreover, Monte Carlo simulations confirm the stability of the target-oriented robust optimization solutions, outperforming their deterministic counterparts when faced with uncertainty, specifically for risk averse decision makers the suggested solution is when robustness index θ = 1.0, where the profit target must be set at 1.3030 million US$, with this profit is expected to be 1.3010 million US$ with a standard deviation of 4,750 US$ and mean carbon sequestered at 3.1165 million CO₂-kg, which is notably improved compared to lower robustness indices.