PMID- 34599437 OWN - NLM STAT- MEDLINE DCOM- 20220204 LR - 20220204 IS - 1614-7499 (Electronic) IS - 0944-1344 (Linking) VI - 29 IP - 10 DP - 2022 Feb TI - Urease production using corn steep liquor as a low-cost nutrient source by Sporosarcina pasteurii: biocementation and process optimization via artificial intelligence approaches. PG - 13767-13781 LID - 10.1007/s11356-021-16568-6 [doi] AB - 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 R(2) 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 muM 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. CI - (c) 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. FAU - Maleki-Kakelar, Mahdi AU - Maleki-Kakelar M AUID- ORCID: 0000-0001-5232-9164 AD - Department of Chemical Engineering, University of Zanjan, Zanjan, Iran. mmaleki@znu.ac.ir. FAU - Azarhoosh, Mohammad Javad AU - Azarhoosh MJ AD - Chemical Engineering Department, Urmia University, Urmia, Iran. FAU - Golmohammadi Senji, Sina AU - Golmohammadi Senji S AD - Department of Civil Engineering, University of Tehran, Tehran, Iran. FAU - Aghaeinejad-Meybodi, Abbas AU - Aghaeinejad-Meybodi A AD - Chemical Engineering Department, Urmia University, Urmia, Iran. LA - eng PT - Journal Article DEP - 20211001 PL - Germany TA - Environ Sci Pollut Res Int JT - Environmental science and pollution research international JID - 9441769 RN - EC 3.5.1.5 (Urease) RN - H0G9379FGK (Calcium Carbonate) RN - Sporosarcina pasteurii SB - IM MH - Artificial Intelligence MH - Calcium Carbonate MH - Nutrients MH - Sporosarcina MH - *Urease MH - *Zea mays OTO - NOTNLM OT - Adaptive neuro-fuzzy inference system OT - Biogrout OT - Genetic programming OT - Industrial waste OT - Microbial induced carbonate precipitation OT - Shear strength OT - Soil improvement EDAT- 2021/10/03 06:00 MHDA- 2022/02/05 06:00 CRDT- 2021/10/02 06:22 PHST- 2021/04/10 00:00 [received] PHST- 2021/09/12 00:00 [accepted] PHST- 2021/10/03 06:00 [pubmed] PHST- 2022/02/05 06:00 [medline] PHST- 2021/10/02 06:22 [entrez] AID - 10.1007/s11356-021-16568-6 [pii] AID - 10.1007/s11356-021-16568-6 [doi] PST - ppublish SO - Environ Sci Pollut Res Int. 2022 Feb;29(10):13767-13781. doi: 10.1007/s11356-021-16568-6. Epub 2021 Oct 1.