Influence of pH and dissolved oxygen control strategies on the performance of pilot-scale microalgae raceways using fertilizer or wastewater as the nutrient source

Dissolved oxygen concentration and pH are controllable and cost-effective variables that determine the success of microalgae-related processes. The present study compares different control strategies for pH and dissolved oxygen in pilot-scale microalgae production systems. Two 80 m2 raceway reactors were used, one operated with freshwater plus fertilizer and the other with wastewater as the nutrient source. Both were in semi-continuous mode at a fixed dilution rate of 0.2 day-1. A comparison between the classical On-Off and more advanced pH control strategies, such as PI and Event-based control, was performed, focusing on biomass productivity and the influence of all the process parameters on microalgae growth; "No control" of pH was also assayed. The results show that Event-based control was the best algorithm when using freshwater plus fertilizer. In contrast, no significant differences were observed using the different control strategies when wastewater was the nutrient source. These experiments were performed through selective control strategy, prioritizing pH over dissolved oxygen; however, it was demonstrated that they did not allow to achieve satisfactory dissolved oxygen removal results, especially for the fertilizer system. After modifying the gas diffuser configuration and improving the mass transfer, independent on-off strategies have been developed, permitting effective control of both variables and increasing productivity by up to 20% in both systems. Concluding, a detailed analysis of the energy demand for each strategy implemented in terms of gas consumption and gas flow to biomass ratio is provided.

Recycling industrial wastewater for improved carbohydrate-rich biomass production in a semi-continuous photobioreactor: Effect of hydraulic retention time

This study aimed to investigate a mixed microalgae culture's capacity to simultaneously remove nutrients and organic matter from industrial effluents while producing carbohydrate-rich biomass. A culture initially dominated by filamentous cyanobacteria Geitlerinema sp. was inoculated in a lab-scale stirred tank photobioreactor, operating at 10, 8, and 6 days hydraulic retention time (HRT). The results show that different HRT led to different inorganic carbon profiles and N:P ratios in the culture, influencing microbial changes, and carbohydrate content. Hence, higher N-NH₄⁺ removal efficiencies were obtained at HRT of 10 d and decreased with decreasing HRT. Whereas, complete depletion of P-PO₄^3- was achieved only at HRT of 8 d and 6 d. Also, the highest COD removal efficiency (60%) was achieved at 6 d of HRT. The maximum accumulation of carbohydrates was achieved at HRT of 8 d, which presented an N:P ratio of 22:1 and carbon availability, recording a constant carbohydrate content of 57% without any additional carbon source. Furthermore, this operational condition reached the best biomass production of 0.033 g L^-1d^-1 of easy-settling cyanobacteria dominated culture. According to the results, this process presents an alternative to recycling industrial effluents and, at the same time, grow valuable biomass, closing a loop for sustainable economy.

Modelling of Microalgae Culture Systems with Applications to Control and Optimization

Mathematical modeling is becoming ever more important to assess the potential, guide the design, and enable the efficient operation and control of industrial-scale microalgae culture systems (MCS). The development of overall, inherently multiphysics, models involves coupling separate submodels of (i) the intrinsic biological properties, including growth, decay, and biosynthesis as well as the effect of light and temperature on these processes, and (ii) the physical properties, such as the hydrodynamics, light attenuation, and temperature in the culture medium. When considering high-density microalgae culture, in particular, the coupling between biology and physics becomes critical. This chapter reviews existing models, with a particular focus on the Droop model, which is a precursor model, and it highlights the structure common to many microalgae growth models. It summarizes the main developments and difficulties towards multiphysics models of MCS as well as applications of these models for monitoring, control, and optimization purposes.