Abstract
The microbiologically induced calcite precipitation (MICP) can be an emerging approach that could tap onto soil bacterial diversity and use as a bioremediation technique. Based on the concept that bacteria with biomineralization capacity could be effective CaCO3 inductance agents, this study aimed to evaluate the simultaneous influence of 11 operational and environmental factors on the MICP process, for the first time. Therefore, Bacillus muralis, B. lentus, B. simplex, B. firmus, and B. licheniformis, isolated from alkaline soils, were used in the selection of the best performing bacterium compared with a well-known MICP bioagent Sporosarcina pasteurii DSM 33. Plackett-Burman's experimental design was labouring to screen all independent variables for their significances on five outputs (pH value, number of viable cells and spores, amount of urea and CaCO3 precipitate). According to experimentally obtained data, an artificial neural network model based on the Broyden-Fletcher-Goldfarb-Shanno algorithm showed good prediction capabilities, while differences in the relative influences were observed at the bacterial strain level. B. licheniformis turn out to be the most potent bioagent, with a maximum amount of CaCO3 precipitate of 3.14 g/100 mL in the optimal conditions.
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