A correct assessment of microalgae growth on porous building materials (i.e.: fired bricks, sandstones and limestones) can provide a useful tool for researchers and practitioners. In fact, it may help predicting the biofouling damage extension and it can assist the experts in a correct planning of maintenance interventions to limit costs. The literature regarding such issue outlined the Avrami's model as the most recurrent one, even considering the influence of biocidal treatments on the substrate. However, it seems to have some limitations when the growth is very fast or, conversely, when the latency time is extended over the time. Therefore, a different modelling approach is here proposed, by using the logistic function (extensively used i.e. in population growth). Results reveal that the logistic function seems to succeed in better modelling the available experimental data. Moreover, it seems to overcome the limits of the Avrami's model, as well as to be less influenced by the main drivers of microalgae growth, such as porosity and roughness of the substrate, biocides treatments and environmental conditions (temperature).
Modelling microalgae biofouling on porous buildings materials: a novel approach / Quagliarini, E; Gregorini, B; D'Orazio, M. - In: MATERIALS AND STRUCTURES. - ISSN 1359-5997. - ELETTRONICO. - 55:6(2022). [10.1617/s11527-022-01993-x]
Modelling microalgae biofouling on porous buildings materials: a novel approach
Quagliarini, E
Primo
Conceptualization
;Gregorini, BSecondo
Writing – Original Draft Preparation
;D'Orazio, MUltimo
Supervision
2022-01-01
Abstract
A correct assessment of microalgae growth on porous building materials (i.e.: fired bricks, sandstones and limestones) can provide a useful tool for researchers and practitioners. In fact, it may help predicting the biofouling damage extension and it can assist the experts in a correct planning of maintenance interventions to limit costs. The literature regarding such issue outlined the Avrami's model as the most recurrent one, even considering the influence of biocidal treatments on the substrate. However, it seems to have some limitations when the growth is very fast or, conversely, when the latency time is extended over the time. Therefore, a different modelling approach is here proposed, by using the logistic function (extensively used i.e. in population growth). Results reveal that the logistic function seems to succeed in better modelling the available experimental data. Moreover, it seems to overcome the limits of the Avrami's model, as well as to be less influenced by the main drivers of microalgae growth, such as porosity and roughness of the substrate, biocides treatments and environmental conditions (temperature).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.