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.

CFD Modeling of Methanol to Light Olefins in a Sodalite Membrane Reactor using SAPO-34 Catalyst with In Situ Steam Removal.

AIMS AND OBJECTIVE: In this work, the performance of a sodalite membrane reactor (MR) in the conversion of methanol to olefins (MTO process) was evaluated for ethylene and propylene production with in situ steam removal using 3-dimensional CFD (computational fluid dynamic) technique. METHODS: Numerical simulation was performed using the commercial CFD package COMSOL Multiphysics 5.3. The finite element method was used to solve the governing equations in the 3- dimensional CFD model for the present work. In the sodalite MR model, a commercial SAPO-34 catalyst in the reaction zone was considered. The influence of key operation parameters, including pressure and temperature on methanol conversion, water recovery, and yields of ethylene, propylene, and water was studied to evaluate the performance of sodalite MR. RESULTS: The local information of component concentration for methanol, ethylene, propylene, and water was obtained by the proposed CFD model. Literature data were applied to validate model results, and a good agreement was attained between the experimental data and predicted results using CFD model. Permeation flux through the sodalite membrane was increased by an increase of reaction temperature, which led to the enhancement of water stream recovered in the permeate side. CONCLUSION: The CFD modeling results showed that the sodalite MR in the MTO process had higher performance in methanol conversion compared to the fixed-bed reactor (methanol conversion of 97% and 89% at 733 K for sodalite MR and fixed-bed reactor, respectively).

Performance analysis of ultrasound-assisted synthesized nano-hierarchical SAPO-34 catalyst in the methanol-to-lights-olefins process via artificial intelligence methods.

The present study has focused on performance analysis of ultrasound-assisted synthesized nano-hierarchical silico-alumino-phosphate-34 (SAPO-34) catalyst during methanol-to-light-olefins (MTO) process. A classical method, i.e., multiple linear regression (MLR) and two intelligent methods, i.e., genetic programming (GP) and artificial neural networks (ANN) were used for modeling of the performance of nano-hierarchical SAPO-34 catalyst. We studied the influence of basic parameters for the sonochemical synthesis of nano-hierarchical SAPO-34 catalyst such as crystallization time, ultrasonic irradiation time, ultrasonic intensity, amount of organic template (diethylamine (DEA) and carbon nanotube (CNT)) on its performance (methanol conversion and light olefins selectivity) in MTO process. The results revealed that the models achieved using the GP method had the highest accuracy for training and test data. Therefore, GP models were used in the following to predict the effect of main parameters for the sonochemical synthesis of nano-hierarchical SAPO-34 catalyst. Finally, an optimal catalyst with the highest yield into light olefins was predicted using the genetic optimization algorithm. The genetic models were employed as an evaluation function in the genetic algorithm (GA). A good agreement between the outputs of GP models for the optimal catalyst and experimental results were obtained. The optimal ultrasound-assisted synthesized nano-hierarchical SAPO-34 was accompanied by light olefins selectivity of 77% and methanol conversion of 94% from the onset of the process after 9 h.

Degradation of antidepressant drug fluoxetine in aqueous media by ozone/H2O2 system: process optimization using central composite design.

The main objective of this work is the modelling and optimization of antidepressant drug fluoxetine degradation in aqueous solution by ozone/H2O2 process using central composite design. The operational parameters were ozone concentration, initial hydrogen peroxide concentration, reaction time and initial fluoxetine concentration. A good agreement between the predicted values of fluoxetine removal and experimental results were observed (R2=0.976 and Adj-R2=0.955). Pareto analysis indicated that all selected factors and some interactions were effective on the removal efficiency. It was found that the reaction time is the most effective parameter in the ozone/H2O2 process. The maximum removal efficiency (86.14%) was achieved at ozone concentration of 30 mg L(-1), initial H2O2 concentration of 0.02 mM, reaction time of 20 min and initial fluoxetine concentration of 50 mg L(-1) as the optimum conditions.