Application of Neural Network Algorithm in Propylene Distillation

Artificial neural network modeling does not need to consider the mechanism. It can map the implicit relationship between input and output and predict the performance of the system well. At the same time, it has the advantages of self-learning ability and high fault tolerance. The gas-liquid two phases in the rectification tower conduct interphase heat and mass transfer through countercurrent contact. The functional relationship between the product concentration at the top and bottom of the tower and the process parameters is extremely complex. The functional relationship can be accurately controlled by artificial neural network algorithms. The key components of the propylene distillation tower are the propane concentration at the top of the tower and the propylene concentration at the bottom of the tower. Accurate measurement of them plays a key role in increasing propylene yield in ethylene production enterprises. This article mainly introduces the development process of neural network model and its application progress in propylene distillation tower.

Ni, Mo-containing catalysts for the single-step synthesis of propylene from ethylene: the effect of the support nature

Polyfunctional Ni, Mo-containing catalysts were synthesized by sequential impregnation of the support (SiO2, Al2O3, B2O3-Al2O3, SO42–/Al2O3,SO42–/ZrO2) with solutions of the corresponding salts with intermediate drying at 120 °C and calcination at 500–550 °C. Physicochemical properties of the synthesized catalysts were studied by XRD, H2-TPR, UV-vis spectroscopy and EPR; the catalysts were tested in the singlestep synthesis of propylene from ethylene at atmospheric pressure, temperature 200 °С, and ethylene mass flow rate 0.5 h–1. The maximum values of ethylene conversion and propylene yield were observed for the sample synthesized from borate-containing alumina; this is related to the formation of active sites of ethylene dimerization – the Ni2+ cations bound to acid sites of the support, and active sites of metathesis – the surface monomolybdate compounds.

Propylene Yield from Olefin Plant Utilizing Box-Cox Transformation in Regression Analysis

Propylene yield is one of the key operating parameters that is monitored daily in the running olefin plant. This study was conducted in the actual world-scale olefin plant to measure the impact of identified controlled variables on the propylene yield. The Box-Cox data transformation was adopted in the Regression Analysis using Minitab Software Version 18 due to non-normal data were observed after normality and stability test were conducted using Box Plot, I-MR Chart, Run Chart, Graphical Summary, and Normality Plot tools. The model concluded that propylene yield in the studied plant was contributed by the factors of -0.000243 Hearth Burner Flow, 0.01332 Integral Burner Flow, and 0.08598 Naphtha Feed Flow. The Response Optimizer tool also suggested that the propylene yield from naphtha liquid feed can be maximized at 11.22% with the control setting at 10,993.86 kg/hr of Hearth Burner Flow, 604.61 kg/hr of Integral Burner Flow, and 63.50 t/hr of Naphtha Feed Flow.

Dehydrogenation of Propane to Propylene Using Promoter-Free Hierarchical Pt/Silicalite-1 Nanosheets

Propane dehydrogenation (PDH) is the extensive pathway to produce propylene, which is as a very important chemical building block for the chemical industry. Various catalysts have been developed to increase the propylene yield over recent decades; however, an active site of monometallic Pt nanoparticles prevents them from achieving this, due to the interferences of side-reactions. In this context, we describe the use of promoter-free hierarchical Pt/silicalite-1 nanosheets in the PDH application. The Pt dispersion on weakly acidic supports can be improved due to an increase in the metal-support interaction of ultra-small metal nanoparticles and silanol defect sites of hierarchical structures. This behavior leads to highly selective propylene production, with more than 95% of propylene selectivity, due to the complete suppression of the side catalytic cracking. Moreover, the oligomerization as a side reaction is prevented in the presence of hierarchical structures due to the shortening of the diffusion path length.

Direct conversion of bio-ethanol to propylene in high yield over the composite of In2O3 and zeolite beta

The superior propylene yield of the In2O3-beta composite (ca. 50%) for the conversion of ethanol to propylene compared to In2O3 (ca. 32%) is due to the fact that zeolite beta in the composite enhances the conversion of the intermediate of acetone to propylene via an additional pathway.

CFD Simulation of Propane Thermal Cracking Furnace and Reactor: A Case Study

In the present work, a different new arrangement of side-wall burners of an industrial furnace with varying fuel flow rate was studied by three-dimensional CFD simulation. Tube skin temperature and heat flux profiles were obtained by solving mass, momentum and energy equations of the furnace by Ansys Fluent software. A reasonable fuel flow rate ($$\dot m$$=0.0695 kg/s) was assigned and effect of different ratio of this rate (0.25$$\dot m$$, 0.5$$\dot m$$, 2$$\dot m$$, 4$$\dot m$$) was investigated on reactor tube skin temperature profiles. Heat and temperature non-uniform distribution was observed by proposed arrangement. It was found that proper range for fuel rate was 0.5$$\dot m$$ to 2$$\dot m$$. Temperature profiles were used in one dimensional plug flow reactor model equations to consider fuel rate variations on reactor performance. By the proposed burner arrangement, Propane conversion and Ethylene yield obtained 6.25 % and 7.2 % more than the base case. Furthermore coil outlet temperature (COT) decreased about 7 °C. Also, feed flowrate was taken as an effective parameter on reactor process under no coke formation condition. Results showed that by increasing fuel rate, outlet Propylene yield decreased, while, process gas temperature, pressure drop, process severity (propane conversion) and Ethylene yield increased along the reactor tube i. e. for 0.5$$\dot m$$ to 2$$\dot m$$ at 0.8 kg/s reactor flowrate, Propylene yield decreased 15.95 % and reached to zero, whereas Ethylene yield increased 16.5 %. Also, in any fuel rate, by increasing reactor feed flowrate, even though the reactor coil outlet temperature decreased, the desired product yields increased. At 0.95 kg/s reactor flowrate, maximum Ethylene yield was obtained about 45.5 % at 1$$\dot m$$ kg/s; while, Propylene yield production at 0.5$$\dot m$$ kg/s fuel rate was 22.41 %.

Investigation of Key Factors and Their Interactions in MTO Reaction by Statistical Design of Experiments

The effects of templating agent [i. e., tetraethyl ammonium hydroxide (TEAOH), triethylamine (TEA) and morpholine (MOR)] and molar ratio of SiO2/Al2O3 and H2O/Al2O3 over SAPO-34 catalysts in methanol to olefin (MTO) reaction were studied systematically. The exact effect of main factors and their interaction were studied by response surface methodology (RSM) applying central composite design (CCD). Two empirical models for two systems, based on these preparation variables for the yield of ethylene and propylene were constructed in two CCD studies and ultimately these models showed as counter and three-dimensional (3D) diagrams. Analysis of Variance (ANOVA) applied for investigating the significance of the variables indicated that TEA and SiO2/Al2O3 content were the most significant variables in the case (1) and case (2), respectively. The maximum predicted ethylene and propylene yield was 58.57 wt. % and 30.22 wt. %, for catalyst with TEA = 0.2, TEAOH = 0.38 in case (1). For case (2), catalyst with SiO2/Al2O3 = 0.17, H2O/Al2O3 = 101.18 showed the maximum ethylene and propylene yield of 49.87 wt. % and 20.58 wt. %, respectively.