Catalytic Study of the Partial Oxidation Reaction of Methanol to Formaldehyde in the Vapor Phase

In the present work, several parameters affecting on the catalytic behavior were studied in the process of partial oxidation of methanol to formaldehyde, such as: Mo/Fe ratio in unsupported catalysts, weight percent of the metallic phase in the supported catalysts, the effect of different supports, the method of Mo-Fe deposition on the supports, and the stability of the prepared catalysts against coke. These catalysts were characterized by X-ray diffraction (XRD), Fourier Transform Infra Red (FT-IR), Thermogravimetric Analysis (TGA), Scanning Electron Microscopy (SEM), N2 adsorption-desorption, and Atomic Adsorption Spectroscopy (AAS) methods. The best results (the methanol conversion = 97 % and formaldehyde selectivity = 96 %) were obtained for Mo-Fe/g-Al2O3 prepared by co-precipitation method with Mo/Fe = 1.7, 50 wt.% of Fe-Mo phase, 2 mL/h methanol flow rate, and 120 mL/min air flow rate at 350 oC. Copyright © 2018 BCREC Group. All rights reserved Received: 1st January 2018; Revised: 17th July 2018; Accepted: 24th July 2018 How to Cite: Peyrovi, M.H., Parsafard, N., Hasanpour, H. (2018). Catalytic Study of the Partial Oxidation Reaction of Methanol to Formaldehyde in the Vapor Phase. Bulletin of Chemical Reaction Engineering & Catalysis, 13 (3): 520-528 (doi:10.9767/bcrec.13.3.2048.520-528) Permalink/DOI: https://doi.org/10.9767/bcrec.13.3.2048.520-52

Space-Time Transportation System Modelling: from Traveler’s Characteristics to the Network Design Problem

Traditional network design problems only consider the long-term stationary travel patterns (e.g., fixed OD demand) and short-term variations of human mobility are ignored. This study aims to integrate human mobility characteristics and travel patterns into network design problems using a space-time network structure. Emerging technologies such as location-based social network platforms provide a unique opportunity for understanding human mobility patterns that can lead to advanced modeling techniques. To reach our goal, at first multimodal network design problems are investigated by considering safety and flow interactions between different modes of transport. We develop a network reconstruction method to expand a single-modal transportation network to a multi-modal network where flow interactions between different modes can be quantified. Then, in our second task, we investigate the trajectory of moving objects to see how they can reveal detailed information about human travel characteristics and presence probability with high-resolution detail. A time geography-based methodology is proposed to not only estimate an individual’s space-time trajectory based on his/her limited space-time sample points but also to quantify the accuracy of this estimation in a robust manner. A series of measures including activity bandwidth and normalized activity bandwidth are proposed to quantify the accuracy of trajectory estimation, and cutoff points are suggested for screening data records for mobility analysis. Finally, a space-time network-based modeling framework is proposed to integrate human mobility into network design problems. We construct a probabilistic network structure to quantify human’s presence probability at different locations and time. Then, a Mixed Integer Nonlinear Programming (MINLP) model is proposed to maximize the spatial and temporal coverage of individual targets. To achieve near optimal solutions for large-scale problems, greedy heuristic, Lagrangian relaxation and simulated annealing algorithms are implemented to solve the problem. The proposed algorithms are implemented on hypothetical and real world numerical examples to demonstrate the performance and effectiveness of the methodology on different network sizes and promising results have been obtained

Bimetallic CoMo Nanoparticles Supported over Carbon-Zeolite Composites as Dibenzothiophene Hydrodesulfurization Catalyst

Cobalt molybdenum catalysts supported on novel activated carbon-HZSM-5 composites with different mass ratios were prepared by wet-impregnation method and pre-sulfided by CS2. Characterization of these catalysts was done using X-ray powder diffraction, Fourier transform infrared spectroscopy, N2 adsorption-desorption, and scanning electron microscope analytics. Their activity for the hydrodesulfurization reaction of dibenzothiophene was investigated at atmospheric pressure in the temperature range of 250–400 °C using the fixed-bed reactor with 0.5 g of each powder and pre-sulfided with CS2. The highest conversion of dibenzothiophene at the temperature range of 300–400 °C was obtained for the CoMo/activated carbon-HZSM-5(1:1) catalyst. The best selectivity for cyclohexylbenzene, which is the dominant product according to gas chromatography results, was obtained at all temperatures using CoMo/activated carbon-HZSM-5(3:1) catalyst. Copyright © 2021 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0)

Error Measures for Trajectory Estimations with Geo-Tagged Mobility Sample Data

Although geo-tagged mobility data (e.g., cell phone data and social media data) can be potentially used to estimate individual space-time travel trajectories, they often have low sample rates that only tell travelers\u27 whereabouts at the sparse sample times while leaving the remaining activities to be estimated with interpolation. This paper proposes a set of time geography-based measures to quantify the accuracy of the trajectory estimation in a robust manner. A series of measures including activity bandwidth and normalized activity bandwidth are proposed to quantify the possible absolute and relative error ranges between the estimated and the ground truth trajectories that cannot be observed. These measures can be used to evaluate the suitability of the estimated individual trajectories from sparsely sampled geo-tagged mobility data for travel mobility analysis. We suggest cutoff values of these measures to separate useful data with low estimation errors and noisy data with high estimation errors. We conduct theoretical analysis to show that these error measures decrease with sample rates and peoples\u27 activity ranges. We also propose a lookup table-based interpolation method to expedite the computational time. The proposed measures have been applied to 2013 geo-tagged tweet data in New York City, USA, and 2014 cell-phone data in Shenzhen, China. The results illustrate that the proposed measures can provide estimation error ranges for exceptionally large datasets in much shorter times than the benchmark method without using lookup tables. These results also reveal managerial results into the quality of these data for human mobility studies, including their distribution patterns