Microalgae growth optimization in open ponds with uncertain weather data

• Published in: Computers & chemical engineering Vol. 117; pp. 410 - 419
• Main Authors: De-Luca, Riccardo, Trabuio, Martino, Barolo, Massimiliano, Bezzo, Fabrizio
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 02.09.2018
• Subjects: Biofuels; Microalgae; Open ponds; Optimization; Weather uncertainty; Open ponds; Microalgae; Biofuels; Weather uncertainty; Optimization
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2018.07.005

•Uncertain weather forecasts may jeopardise control effectiveness in microalgae-based processes.•Two approaches are proposed to avoid operation unfeasibility due to uncertain forecasts.•Productivity losses are limited to at most −10% with respect to the optimal process operation.•Water utilization in the process is significantly increased. Although microalgae-based processes are currently one of the most promising new technologies for the substitution of fossil fuels and chemicals, the theoretical potential of these technologies is currently limited by their low profitability, hence hindering the development of large scale plants in an economically feasible way. One of the process bottlenecks is the cultivation phase, whose operation is complicated by both the involved biological mechanisms complexity and the highly fluctuating weather conditions affecting the system. Available mathematical models describing microalgae growth and pond temperature dynamics through weather data implementation assume perfect knowledge of weather conditions, hence neglecting the inaccuracy of meteorological predictions that is expected even considering short-term forecasts. In this study a sensitivity study is first carried out to evaluate the weather variables that most impact on productivity. Then, two optimization approaches are proposed to prevent potential critical conditions (such as cell death due to too high temperatures) that may arise by using inaccurate weather forecast. The study demonstrates the reliability of the proposed methodologies and compares them in terms of productivity loss and water demand.