Synthesis,Characterization, and Evaluation of Mesoporous MCM-41-supported Molybdenum Hydrotreating Catalysts

Abstract

Mesoporous MCM-41 material with high surface area and narrow pore size distribution was synthesized and used as a support for Mo, CoMo, and NiMo catalysts. The molybdenum loading was varied from 2–14 wt% on MCM-41. On 10 wt% Mo/MCM-41, the promoter Co or Ni concentration was varied from 1–5 wt%. All the catalyst samples were characterized by surface area, low temperature oxygen chemisorption, x-ray diffraction (XRD), and temperature programmed reduction methods. Characterization results show that Mo is well dispersed on MCM-41 up to 10 wt%. The catalytic activities were evaluated for thiophene hydrodesulphurization (HDS), cyclohexene hydrogenation (HYD), and furan hydrodeoxygenation (HDO). All three catalytic functionalities vary in a similar manner to that of oxygen chemisorption as a function of Mo loading, indicating that there is a correlation between oxygen uptake and catalytic sites. The activities of these catalysts were compared with γ-Al₂O₃- and amorphous SiO₂-supported catalysts. It was found that MCM-41-supported Mo catalysts displayed superior activities.     

Application of ANN, Fuzzy Logic and Decision Tree Algorithms for the Development of Reservoir Operating Rules

Optimal use of scarce water resources is the prime objective for water resources development projects in the developing country like India. Optimal releases have been generally expressed as a function of reservoir state variables and hydrologic inputs by a relationship which ultimately allows the policy/water managers to determine the water to be released as a function of available information. Optimal releases were obtained by using optimal control theory with inflow series and revised reservoir characteristics such as elevation area capacity table, zero elevation level as input in this study. Operating rules for reservoir were developed as a function of demand, water level and inflow. Artificial Neural Network (ANN) with back propagation algorithm, Fuzzy Logic and decision tree algorithms such as M5 and REPTree were used for deriving the operating rules using the optimal releases for an irrigation and power supply reservoir, located in northern India. It was found that fuzzy logic model performed well compared to other soft computing techniques such as ANN, M5P and REPTree investigated in this study.     

A collaborative policy-based security scheme to enforce resource access controlling mechanism

Advances in both telecommunications and Information technology have improved the way users do business online. Android, an open-source mobile operating system, is becoming an attractive target for cyber criminals to exploit due to its predefined permission model. Without classification, the mobile operating system permits installation of mobile applications of all kinds, including Trojans, thus making its trustworthiness into question. In this paper, we present a security system called collaborative policy-based security scheme (CSS) that permits users to customize the access permissions of Android applications during runtime. The proposed CSS security scheme validates the trustworthiness of each application before being installed. The experimental results show that the proposed CSS successfully detects all malicious applications with a run-time overhead of 2.7%.

     

An experimental investigation of performance and emission characteristics of diesel engine with blends of cottonseed oil methyl ester with cold and hot EGR

An experimental investigation of diesel engine using cottonseed oil biodiesel and its blends with exhaust gas recirculation (EGR) techniques has been carried out. An optimum nozzle opening pressure of 250 bar and lower static injection timing of 20° before top dead centre (bTDC) are considered because it has been observed that these conditions only give minimum emissions. From the test results, it could be noted that there is an increasing trend of emission characteristics of HC, smoke density and NO_x for both cold and hot EGR for all blends of fuel with respect to brake power. As compared with cold EGR, the hot EGR gives lower emissions at all loads. In hot EGR, among the blends, at no-load and full-load conditions, the B100 gives the highest reduction in NO_x of 14.23% and 7.91%, respectively. However, the use of EGR leads to a rise in soot emission because of soot–NO_x trade-off for both the cases.     

Genetically tuned fuzzy PID controller in two area reheat thermal power system

This paper demonstrates the design and analysis of automatic generation control using intelligent genetic algorithm tuned fuzzy based controller. A two area thermal power system simulated for four different scenarios considers a reheat steam turbine in each area with Generator rate constraints. The Integral Time Squared Error (ITSE) employed to get an objective function for the optimization of controller gains. The simulation results compared with the conventional Proportional Integral Derivative (PID) controller, Genetic Algorithm (GA) tuned PID controller and GA tuned Fuzzy PID controller. The proposed GA tuned Fuzzy based PID Controller can generate the best performance for peak overshoot, undershoot and settling time with step load disturbances. Robustness of the performance of the proposed controller provided with system parametric uncertainties.     

Long-term Sector-wise Electrical Energy Forecasting Using Artificial Neural Network and Biogeography-based Optimization

This article presents a hybrid model involving artificial neural networks and biogeography-based optimization for long-term forecasting of India's sector-wise electrical energy demand. It involves socio-economic indicators, such as population and per capita gross domestic product, and uses two artificial neural networks, which are trained through a biogeography-based optimization algorithm with a goal of perfect mapping of the input–output data in the non-linear space through obtaining the global best weight parameters. The biogeography-based optimization based training of the artificial neural network improves the forecasting accuracy and avoids trapping in local optima besides enhancing the convergence to the lowest mean squared error at the minimum number of iterations than existing approaches. The model requires an input and the year of the forecast and predicts the sector-wise energy demand. Forecasts up to the year 2025 are compared with those of the regression model, the artificial neural network model trained by back-propagation, and the artificial neural network model trained by harmony search algorithm to exhibit its effectiveness.