Synthesis of magnetic layered double hydroxide (Fe3O4@CuCr-LDH) decorated with ZIF-8 for efficient sonocatalytic degradation of tetracycline.

A series of Fe3O4@CuCr-LDH hybrids decorated with different amount of ZIF-8 (FLZ, 10-40 wt%) was prepared using simple methods and characterized with different techniques. The activity of the synthesized nanocomposites was investigated in the sonocatalytic degradation of tetracycline (TC) antibiotic from wastewater. When the content of ZIF-8 in the nanocomposite structure was 20 wt%, the FLZ-20 sonocatalyst exhibited the high performance in the sonocatalytic removal of TC. At optimum conditions (0.7 g/L catalyst dosage, pH of 7, 50 mg/L initial concentration of antibiotic, and 15 min sonication time) of the sonocatalytic removal of TC approached to 91.4% under ultrasonic irradiation (USI) using FLZ-20. This efficiency was much higher than those of obtained results by Fe3O4@CuCr-LDH and pristine ZIF-8. The formed ●OH and ●O2- exhibited the major roles in the sonocatalytic TC degradation process. The excellent performance of FLZ-20 can be attributed to the heterojunctions created between composite components, which could improve the electron transfer ability and effectively separate e-/h+ pairs. In addition, FLZ-20 showed the superior reusability and stability during three successive recycling. Moreover, the facile magnetically separation of the sonocatalyst from the aqueous solution was another outstanding feature, which prevents the formation of secondary pollutants. It can be concluded that the fabrication of heterojunctions is an efficient procedure to promote the sonocatalytic acting of the catalyst.

Estimation of the flow rate of pyrolysis gasoline, ethylene, and propylene in an industrial olefin plant using machine learning approaches.

Light olefins, as the backbone of the chemical and petrochemical industries, are produced mainly via steam cracking route. Prediction the of effects of operating variables on the product yield distribution through the mechanistic approaches is complex and requires long time. While increasing in the industrial automation and the availability of the high throughput data, the machine learning approaches have gained much attention due to the simplicity and less required computational efforts. In this study, the potential capability of four powerful machine learning models, i.e., Multilayer perceptron (MLP) neural network, adaptive boosting-support vector regression (AdaBoost-SVR), recurrent neural network (RNN), and deep belief network (DBN) was investigated to predict the product distribution of an olefin plant in industrial scale. In this regard, an extensive data set including 1184 actual data points were gathered during four successive years under various practical conditions. 24 varying independent parameters, including flow rates of different feedstock, numbers of active furnaces, and coil outlet temperatures, were chosen as the input variables of the models and the outputs were the flow rates of the main products, i.e., pyrolysis gasoline, ethylene, and propylene. The accuracy of the models was assessed by different statistical techniques. Based on the obtained results, the RNN model accurately predicted the main product flow rates with average absolute percent relative error (AAPRE) and determination coefficient (R2) values of 1.94% and 0.97, 1.29% and 0.99, 0.70% and 0.99 for pyrolysis gasoline, propylene, and ethylene, respectively. The influence of the various parameters on the products flow rate (estimated by the RNN model) was studied by the relevancy factor calculation. Accordingly, the number of furnaces in service and the flow rates of some feedstock had more positive impacts on the outputs. In addition, the effects of different operating conditions on the propylene/ethylene (P/E) ratio as a cracking severity factor were also discussed. This research proved that intelligent approaches, despite being simple and straightforward, can predict complex unit performance. Thus, they can be efficiently utilized to control and optimize different industrial-scale units.

Machine learning approaches for predicting arsenic adsorption from water using porous metal-organic frameworks.

Arsenic in drinking water is a serious threat for human health due to its toxic nature and therefore, its eliminating is highly necessary. In this study, the ability of different novel and robust machine learning (ML) approaches, including Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Decision Tree, and Random Forest was implemented to predict the adsorptive removal of arsenate [As(V)] from wastewater over 13 different metal-organic frameworks (MOFs). A large experimental dataset was collected under various conditions. The adsorbent dosage, contact time, initial arsenic concentration, adsorbent surface area, temperature, solution pH, and the presence of anions were considered as input variables, and adsorptive removal of As(V) was selected as the output of the models. The developed models were evaluated using various statistical criteria. The obtained results indicated that the LightGBM model provided the most accurate and reliable response to predict As(V) adsorption by MOFs and possesses R2, RMSE, STD, and AAPRE (%) of 0.9958, 2.0688, 0.0628, and 2.88, respectively. The expected trends of As(V) removal with increasing initial concentration, solution pH, temperature, and coexistence of anions were predicted reasonably by the LightGBM model. Sensitivity analysis revealed that the adsorption process adversely relates to the initial As(V) concentration and directly depends on the MOFs surface area and dosage. This study proves that ML approaches are capable to manage complicated problems with large datasets and can be affordable alternatives for expensive and time-consuming experimental wastewater treatment processes.

The performance of micro-meso-pore HY zeolite for supporting Mo toward oxidation of dibenzothiophene.

A uniformly distribution of 3 wt.% Mo (with tetrahedral coordination) on a commercial HY zeolite having both micro- and meso-pores, provided a new active catalyst which resulted 100% removal of DBT in this work. Respectively, H2O2 and acetonitrile were used as the oxidant and extraction solvent for oxidative desulfurization (ODS) at a mild condition. The structure of three-dimensional meso-pores, despite major micro-pores, was proved to be intriguing for the use of acidic HY zeolite as a support material in this process. The catalyst samples were characterized by different analyses of XRPD, XRF, FTIR, SEM, EDX, TEM, N2 adsorption desorption, BET, BJH, UV-vis, and NH3-TPD. High amounts of Mo were not in favor of the catalytic performance because of increasing non-framework polymolybdate formation, which led to decreasing meso-pore volume. Acid sites strength also decreased by increasing Mo content. The Mo active sites at a low loading of 3 wt.% reached the best performance for the complete removal of DBT (t = 90 min, T = 60 °C, catalyst/fuel = 8 g/L, O/S = 2, VSolvent/VOil = 1/2, DBT = 1000 ppm), mainly due to the presence of isolated Mo species in the framework of HY. The efficiency still reached to 90% after recycling the catalyst three times. The reusability of catalyst revealed the adsorption of the aqueous phase by this hydrophilic catalyst during the process being as a major deactivation factor. This was significantly diminished via a subsequent washing by acetonitrile.