Nanoparticle Catalyzed Hydrodesulfurization of Diesel Fuel in a Trickle Bed Reactor: Experimental and Optimization Study

This work focuses on the preparation, simulation, and optimization of the hydrodesulfurization (HDS) of dibenzothiophene (DBT) using a nanocatalyst. A homemade nanocatalyst (3 percent Co, 10 percent Mo/γ-Al2O3 nanoparticles) was used in a trickle bed reactor (TBR). The HDS kinetic model was estimated based on experimental observations over ranges of operating conditions to evaluate kinetic parameters of the HDS process and apply the key parameters. Based on these parameters, the performance of the TBR catalyzed by the nanocatalyst was evaluated and scaled up to a commercial scale. Also, the selectivity of HDS reactions was also modeled to achieve the highest yield of the desired hydrogenation product based on the desirable route of HDS. A comprehensive modeling and simulation of the HDS process in a TBR was developed and the output results were compared with experimental results. The comparison showed that the simulated and experimental data of the HDS process match well with a standard error of up to 5%. The best reaction kinetic variables obtained from the HDS pilot-plant (specific reaction rate expression, rate law, and selectivity) TBR have been utilized to develop an industrial scale HDS of DBT. The hydrodynamic key factors (effect of radial and axial dispersion) were employed to obtain the ratio of the optimal working reactor residence time to reactor diameter

Development of Kinetic and Process Models for the Oxidative Desulfurization of Light Fuel, Using Experiments and the Parameter Estimation Technique

YesThe oxidative desulphurization (ODS) of light gas oil (LGO) is investigated with an in-house designed cobalt 11 oxide loaded on alumina (γ-Al2O3) catalyst in the presence of air as oxidizing agent under moderate operating 12 conditions (temperature from 403 to 473 K, LHSV from 1 to 3 hr-1, initial concentration from 500 to 1000 13 ppm). Incipient Wetness Impregnation method (IWI) of cobalt oxide over gamma alumina (2% Co3O4/γ-14 Al2O3) is used for the preparation of the catalyst. The optimal design of experiments is studied to evaluate the 15 effects of a number of process variables namely temperature, liquid hourly space velocity (LHSV) and 16 concentration of dibenzothiophene and their optimal values were found to be 473 K, 1hr-1 and 1000 ppm 17 respectively. For conversion dibenzothiophene to sulphone and sulphoxide, the results indicates that the 18 Incipient Wetness Impregnation (IWI) is suitable to prepare this type of the catalyst. Based on the 19 experiments, mathematical models that represent a three phase reactor for describing the behavior of the ODS 20 process are developed. 21 In order to develop a useful model for simulation, control, design and scale-up of the oxidation process, 22 accurate evaluation of important process parameters such as reaction rate parameters is absolutely essential. 23 For this purpose, the parameter estimation technique available in gPROMS (general Process Modelling 24 System) software is employed in this work. With the estimated process parameters further simulations of the 25 process is carried out and the concentration profiles of dibenzothiophene within the reactor are generated

Sustainable optimizing WMN performance through meta-heuristic TDMA link scheduling and routing

Wireless mesh networks (WMNs) have become a popular solution for expanding internet service and communication in both urban and rural areas. However, the performance of WMNs depends on generating optimized time-division multiple access (TDMA) schedules, which distribute time into a list of slots called superframes. This study proposes novel meta-heuristic algorithms to generate TDMA link schedules in WMNs using two different interference/constraint models: multi-transmit-receive (MTR) and full-duplex (FD). The objectives of this study are to optimize the TDMA frame for packet transmission, satisfy the constraints, and minimize the end-to-end delay. The significant contributions of this study are: (1) proposing effective and efficient heuristic solutions to solve the NP-complete problem of generating optimal TDMA link schedules in WMNs; (2) investigating the new FD interference model to improve the network capacity above the physical layer. To achieve these objectives and contributions, the study uses two popular meta-heuristics, the artificial bee colony (ABC) and/or genetic algorithm (GA), to solve the known NP-complete problems of joint scheduling, power control, and rate control. The results of this study show that the proposed algorithms can generate optimized TDMA link schedules for both MTR and FD models. The joint routing and scheduling approach further minimizes end-to-end delay while maintaining the schedule's minimum length and/or maximum capacity. The proposed solution outperforms the existing solutions in terms of the number of active links, end-to-end delay, and network capacity. The research aims to improve the efficiency and effectiveness of WMNs in most applications that require high throughput and fast response time

Optimal Design of a Trickle Bed Reactor for Light Fuel Oxidative Desulfurization based on Experiments and Modelling

YesIn this work, the performance of oxidative desulfurization (ODS) of dibenzothiophene (DBT) in light gas oil (LGO) is evaluated with a homemade manganese oxide (MnO2/γ-Al2O3) catalyst. The catalyst is prepared by Incipient Wetness Impregnation (IWI) method with air under moderate operating conditions. The effect of different reaction parameters such as reaction temperature, liquid hour space velocity and initial concentration of DBT are also investigated experimentally. Developing a detailed and a validated trickle bed reactor (TBR) process model that can be employed for design and optimization of the ODS process, it is important to develop kinetic models for the relevant reactions with high accuracy. Best kinetic model for the ODS process taking into account hydrodynamic factors (mainly, catalyst effectiveness factor, catalyst wetting efficiency and internal diffusion) and the physical properties affecting the oxidation process is developed utilizing data from pilot plant experiments. An optimization technique based upon the minimization of the sum of the squared error between the experimental and predicted composition of oxidation process is used to determine the best parameters of the kinetic models. The predicted product conversion showed very good agreement with the experimental data for a wide range of the operating condition with absolute average errors less than 5%

PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA

Under field conditions in the Amazon forest, soil bulk density is difficult to measure. Rigorous methodological criteria must be applied to obtain reliable inventories of C stocks and soil nutrients, making this process expensive and sometimes unfeasible. This study aimed to generate models to estimate soil bulk density based on parameters that can be easily and reliably measured in the field and that are available in many soil-related inventories. Stepwise regression models to predict bulk density were developed using data on soil C content, clay content and pH in water from 140 permanent plots in terra firme (upland) forests near Manaus, Amazonas State, Brazil. The model results were interpreted according to the coefficient of determination (R2) and Akaike information criterion (AIC) and were validated with a dataset consisting of 125 plots different from those used to generate the models. The model with best performance in estimating soil bulk density under the conditions of this study included clay content and pH in water as independent variables and had R2 = 0.73 and AIC = -250.29. The performance of this model for predicting soil density was compared with that of models from the literature. The results showed that the locally calibrated equation was the most accurate for estimating soil bulk density for upland forests in the Manaus region

Glutamate acts as a key signal linking glucose metabolism to incretin/cAMP action to amplify insulin secretion.

Incretins, hormones released by the gut after meal ingestion, are essential for maintaining systemic glucose homeostasis by stimulating insulin secretion. The effect of incretins on insulin secretion occurs only at elevated glucose concentrations and is mediated by cAMP signaling, but the mechanism linking glucose metabolism and cAMP action in insulin secretion is unknown. We show here, using a metabolomics-based approach, that cytosolic glutamate derived from the malate-aspartate shuttle upon glucose stimulation underlies the stimulatory effect of incretins and that glutamate uptake into insulin granules mediated by cAMP/PKA signaling amplifies insulin release. Glutamate production is diminished in an incretin-unresponsive, insulin-secreting β cell line and pancreatic islets of animal models of human diabetes and obesity. Conversely, a membrane-permeable glutamate precursor restores amplification of insulin secretion in these models. Thus, cytosolic glutamate represents the elusive link between glucose metabolism and cAMP action in incretin-induced insulin secretion