Direct estimation of microalgal flocs fractal dimension through laser reflectance and machine learning

Abstract

The pre-concentration of microalgal cultures through flocculation can be applied to reduce the harvesting costs of biomass. The microalgal flocs induced through flocculation must have the optimal size and geometry to enhance the performance of subsequent concentration operations. In this work, we propose a new method to estimate the average fractal dimension of Chlorella sorokiniana flocs based on correlating the suspension chord length distribution with the flocs average geometry through a machine learning random forest regression model. To obtain the data required for training the machine learning model, a set of virtual flocs of prescribed fractal dimension was generated through a computer software. The virtual flocs were subject to chord length data acquisition by means of another piece of software simulating the operation of a focused beam reflectance probe. With the chord length data generated the random forest regression model was trained and optimized and then satisfactorily validated with data of real suspensions of known average geometry. The method developed may be used to implement flocculation control systems capable of adjusting the geometry of flocs to the requirements of subsequent concentration operations by actuating on the process stirring intensity.