A novel data-driven model for prediction and adaptive control of pH in raceway reactor for microalgae cultivation

This work proposes a new data-driven model to estimate and predict pH dynamics in freshwater raceway photobioreactors. The resulting model is based purely on data measured from the reactor and divides the pH dynamics into two different behaviors. One behavior is described by the variation of pH due to the photosynthesis phenomena made by microalgae; and the other comes from the effect of CO2 injections into the medium for control purposes. Moreover, it was observed that the model parameters vary throughout the day depending on the weather conditions and reactor status. Thus, a decision tree algorithm is also developed to capture the parameter variation based on measured variables of the system, such as solar radiation, medium temperature, and medium level. The proposed model has been validated for a data set of more than 100 days during 10 months in a semi-industrial raceway reactor, covering a wide range of weather and system scenarios. Additionally, the proposed model was used to design an adaptive control algorithm which was also experimentally tested and compared with a classical fixed parameter control approach.

A Simulation Analysis of a Microalgal-Production Plant for the Transformation of Inland-Fisheries Wastewater in Sustainable Feed

The present research evaluates the simulation of a system for transforming inland-fisheries wastewater into sustainable fish feed using Designer® software. The data required were obtained from the experimental cultivation of Chlorella sp. in wastewater supplemented with N and P. According to the results, it is possible to produce up to 11,875 kg/year (31.3 kg/d) with a production cost of up to 18 (USD/kg) for dry biomass and 0.19 (USD/bottle) for concentrated biomass. Similarly, it was possible to establish the kinetics of growth of substrate-dependent biomass with a maximum production of 1.25 g/L after 15 days and 98% removal of available N coupled with 20% of P. It is essential to note the final production efficiency may vary depending on uncontrollable variables such as climate and quality of wastewater, among others.

Indirect regulation of temperature in raceway reactors by optimal management of culture depth

Temperature and irradiance are the two most relevant factors determining the performance of microalgae cultures in open raceway reactors. Moreover, inadequate temperature strongly reduces the biomass productivity in these systems even if enough sunlight is available. Controlling the temperature in large open raceway reactors is considered unaffordable because of the large amount of energy required. This study presents an indirect method for temperature regulation in microalgae raceway reactors by optimizing the culture depth. First, the effect of the culture depth on the raceway temperature is analyzed for different seasons of the year. Afterward, a simulation study is presented where the proposed control approach is compared to the normal operation mode with constant volume in the reactor. This study is also extended to industrial scale. Relevant improvements on the temperature factor and biomass production are presented. The developed knowledge allows the improvement of the performance in open raceway reactors up to 12% without involving additional energy and costs, being a suitable solution for large industrial reactors that until now have no options for controlling the temperature.

A new model to analyze the temperature effect on the microalgae performance at large scale raceway reactors

In this study a simplified temperature model for raceway reactors is developed, allowing to determine the temperature of the microalgae culture as a function of reactor design and environmental conditions. The model considers the major phenomena taking place in raceway reactors, especially heat absorption by radiation and heat losses by evaporation among others. The characteristic parameters of the model have been calibrated using genetic algorithms, next being validated with a long set of more than 50 days covering different weather conditions. It is worth to highlight the use of the developed model as a tool to analyze the influence of the temperature on the performance of microalgae cultures at large scale. As example, the annual variation of the performance of up to five different microalgae strains has been determined by computing the temperature index, thus the normalized value of performance of whatever microalgae at the real temperature with respect to that achievable at optimal temperature can be established. Results confirm that only strains tolerant to wide ranges of temperature can be efficiently produced all the year around in large scale outdoor raceway reactors without additional temperature control systems.