Urease production using corn steep liquor as a low-cost nutrient source by Sporosarcina pasteurii: biocementation and process optimization via artificial intelligence approaches.

To commercialize the biocementation through microbial induced carbonate precipitation (MICP), the current study aimed at replacing the costly standard nutrient medium with corn steep liquor (CSL), an inexpensive bio-industrial by-product, on the production of urease enzyme by Sporosarcina pasteurii (PTC 1845). Multiple linear regression (MLR) in linear and quadratic forms, adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) were used for modeling of process based on the experimental data for improving the urease activity (UA). In these models, CSL concentration, urea concentration, nickel supplementation, and incubation time as independent variables and UA as target function were considered. The results of modeling showed that the GP model had the best performance to predict the extent of urease, compared to other ones. The GP model had higher R2 as well as lower RSME in comparison with the models derived from ANFIS and MLR. Under the optimum conditions optimized by GP method, the maximum UA value of 3.6 Mm min-1 was also obtained for 5%v/v CSL concentration, 4.5 g L-1 urea concentration, 0 μM nickel supplementation, and 60 h incubation time. A good agreement between the outputs of GP model for the optimal UA and experimental result was obtained. Finally, a series of laboratory experiments were undertaken to evaluate the influence of biological cementation on the strengthening behavior of treated soil. The maximum shear stress improvement between bio-treated and untreated samples was 292% under normal stress of 55.5 kN as a result of an increase in interparticle cohesion parameters.