New solid base catalysts for biodiesel synthesis

The application of heterogeneous catalysts for the manufacture of renewable biodiesel fuels offers an exciting, alternative clean chemical technology to current energy intensive processes employing soluble base catalysts. We recently synthesised tuneable MgO nanocrystals as efficent solid base catalysts for biodiesel synthesis, and have developed a simple X-ray spectroscopic method to quantitatively determine surface basicity, thereby providing a rapid screening tool for predicting the reactivity of new solid base catalysts. Promotion of these MgO nanocrystals through Cs doping dramatically enhances biodiesel production rates due to the formaion of a mixed Cs Mg(CO ) phase. These MgO derived nanocatalysts permit energy efficent, continuous processing of diverse, sustainable oil feedstocks in flow reactors.

Progress in the development of mesoporous solid acid and base catalysts for converting carbohydrates into platform chemicals

This chapter provides a general overview of recent studies on catalytic conversion of fructose, glucose, and cellulose to platform chemicals over porous solid acid and base catalysts, including zeolites, ion-exchange resins, heteropoly acids, as well as structured carbon, silica, and metal oxide materials. Attention is focused on the dehydration of glucose and fructose to HMF, isomerization of glucose to fructose, hydrolysis of cellulose to sugar, and glycosidation of cellulose to alkyl glucosides. The correlation of porous structure, surface properties, and the strength or types of acid or base with the catalyst activity in these reactions is discussed in detail in this chapter.

Investigation and modelling of tremor reducer with the technology of targeted energy transfer

This thesis presents the study of a two-degree-of-freedom (2 DOF) nonlinear system consisting of two grounded linear oscillators coupled to two separate light weight nonlinear energy sinks of an essentially nonlinear stiffness. In this thesis, Targeted Energy Transfer (TET) and NES concept are introduced. Previous studies and research of Energy pumping and NES are presented. The characters in nonlinear energy pumping have been introduced at the start of the thesis. For the aim to design the application of a tremor reduction assessment device, the knowledge of tremor reduction has also been mentioned. Two main parties have been presented in the research: dynamical theoretic method of nonlinear energy pumping study and experiments of nonlinear vibration reduction model. In this thesis, nonlinear energy sink (NES) has been studied and used as a core attachment for the research. A new theoretic method of nonlinear vibration reduction which with two NESs has been attached to a primary system has been designed and tested with the technology of targeted energy transfer. Series connection and parallel connection structure systems have been designed to run the tests. Genetic algorithm has been used and presented in the thesis for searching the fit components. One more experiment has been tested with the final components. The results have been compared to find out most efficiency structure and components for the theoretic model. A tremor reduction experiment has been designed and presented in the thesis. The experiment is for designing an application for reducing human body tremor. By using the theoretic method earlier, the experiment has been designed and tested with a tremor reduction model. The experiment includes several tests, one single NES attached system and two NESs attached systems with different structures. The results of theoretic models and experiment models have been compared. The discussion has been made in the end. At the end of the thesis, some further work has been considered to designing the device of the tremor reduction.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.