Partial Oxidation of Propane Over Mo1V0.3Te0.23Nb0.12OxCatalyst in a Fluidized Bed Reactor

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.

Facile sub-/supercritical water synthesis of nanoflake MoVTeNbOx-mixed metal oxides without post-heat treatment and their catalytic performance

A fast and simple sub-/supercritical water synthesis method is presented in this work in which MoVTeNbO_x-mixed metal oxides with various phase compositions and morphologies could be synthesized without post-heat treatment. It was demonstrated that the system temperature for synthesis had a significant influence on the physico-chemical properties of MoVTeNbO_x. Higher temperatures were beneficial for the formation of a mixed crystalline phase containing TeVO₄, Te₃Mo₂V₂O₁₇, Mo₄O₁₁ and TeO₂, which are very different from the crystalline phases of conventional Mo–V–Te–Nb-mixed metal oxides. While at lower temperatures, Mo₄O₁₁ was replaced by Te. At high temperature, the as-prepared samples presented distinct nanoflake morphologies with an average size of 10–60 nm in width and exhibited excellent catalytic performances in the selective oxidation of propylene to acrylic acid. It is illustrated that the large specific surface area, presence of Mo₄O₁₁ and superficial Mo⁶⁺ and Te⁴⁺ ions are responsible for the high propylene conversion, while suitable acidic sites and superficial Nb⁵⁺ ions improved the selectivity to acrylic acid.

Nanoflake MoVTeNbO_x prepared by sub-/supercritical water exhibit excellent catalytic performance in the selective oxidation of propylene to acrylic acid.