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.

Synthesis of CoFe2O4 magnetic nanoparticles for application in photocatalytic removal of azithromycin from wastewater.

Azithromycin is one of the most widely used antibiotics in medicine prescribed for various infectious diseases such as COVID-19. A significant amount of this drug is always disposed of in hospital effluents. In this study, the removal of azithromycin using Cobalt-Ferrite magnetic nanoparticles (MNP) is investigated in the presence of UV light. For this purpose, magnetic nanoparticles are synthesized and added to the test samples as a catalyst in specific proportions. To determine the structural and morphological properties of nanoparticles, characterization tests including scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), vibrating-sample magnetometer (VSM), and Energy-dispersive X-ray spectroscopy (EDX) are performed. 27 runs have been implemented based on the design of experiments using the Box-Behnken Design (BBD) method. Parameters are the initial concentration of azithromycin (20-60 mg/L), contact time (30-90 min), pH (6-10), and the dose of magnetic nanoparticles (20-60 mg/L). The obtained model interprets test results with high accuracy (R2 = 0.9531). Also, optimization results by the software show that the contact time of 90 min, MNP dosage of 60 mg/L, pH value of 6.67, and azithromycin initial concentration of 20 mg/L leads to the highest removal efficiency of 89.71%. These numbers are in the range of other studies in this regard.

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids.

Absorption has always been an attractive process for removing hydrogen sulfide (H2S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H2S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H2S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H2S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared. A vast experimental databank comprising 792 datasets was utilized. Temperature, pressure, acentric factor, critical pressure, and critical temperature of investigated ILs are the affecting parameters of our models. Sensitivity and statistical error analysis were utilized to assess the performance and accuracy of the proposed models. The calculated solubility data and the derived models were validated using seven statistical criteria. The obtained results showed that the LS-SVM accurately predicts H2S solubility in ILs and possesses R2, RMSE, MSE, RRSE, RAE, MAE, and AARD of 0.99798, 0.01079, 0.00012, 6.35%, 4.35%, 0.0060, and 4.03, respectively. It was found that the H2S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM+ and Tf2N-, i.e., [OMIM][Tf2N] ionic liquid, is the best choice for H2S capture among the investigated absorbents. The H2S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.

Influence of SiO2 and graphene oxide nanoparticles on efficiency of biological removal process.

The effects of the presence of synthesized silica (SS) and exfoliated graphene oxide (EGO) on the removal of sulfide ion with activated sludge (AS) are experimentally investigated. The maximum removal efficiency of sulfide ion for AS without nanoparticles, and the samples with SS and EGO nanoparticles were 81%, 88% and 79%, respectively. Moreover, the maximum elimination capacity (ECmax) for the bioreactor with SS-nanoparticles is 7542 mg/L s, while the ECmax of AS and EGO samples were 7075 and 6625 mg/L s, respectively. Two filamentous microbial strains as Gram-negative and Gram-positive bacteria are discerned that removed sulfide ion in the presence of nanoparticles. The measurement of mixture liquor volatile suspended solid that indicates the biomass growth rate during the test shows that the bioreactor containing SS-nanoparticles has more biomass content than the other samples. Our findings indicate that SS-nanoparticles with 0.1% wt. concentration in the bioreactor have no negative effects on the efficiency of the biological removal of sulfide and the presence of SS-nanoparticles even enhances the performance of the bioreactor. On the other side, a bioreactor with EGO nanosheets, as highly antibacterial nanoparticles, with 0.02% wt. concentration significantly influences the microbial growth and reduces sulfide removal efficiency.