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Can I read Urease production using corn steep liquor as a low-cost nutrient source by Sporosarcina pasteurii: biocementation and process optimization via artificial intelligence approaches on EtoBox?
Urease production using corn steep liquor as a low-cost nutrient source by Sporosarcina pasteurii: biocementation and process optimization via artificial intelligence approaches by Mahdi Maleki-Kakelar; Mohammad Javad Azarhoosh; Sina Golmohammadi Senji; Abbas Aghaeinejad-Meybodi is a Environmental Science article available to read on EtoBox.
What is Urease production using corn steep liquor as a low-cost nutrient source by Sporosarcina pasteurii: biocementation and process optimization via artificial intelligence approaches about?
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 μM nickel supplementation, and 60 h incubation time. A good agree
Who reads Urease production using corn steep liquor as a low-cost nutrient source by Sporosarcina pasteurii: biocementation and process optimization via artificial intelligence approaches?
It is typically read by researchers, students, and practitioners in Environmental Science.
- Author
- Mahdi Maleki-Kakelar; Mohammad Javad Azarhoosh; Sina Golmohammadi Senji; Abbas Aghaeinejad-Meybodi
- Publisher
- Springer Science and Business Media LLC
- Published
- 2021
- Language
- EN
- Field
- Environmental Science (Physical Sciences)