Designing heterogeneous catalysts for biodiesel synthesis

Concerns over dwindling oil reserves, carbon dioxide emissions from fossil fuel sources and associated climate change is driving the urgent need for clean, renewable energy supplies. The conversion of triglycerides to biodiesel via catalytic transesterification remains an energetically efficient and attractive means to generate transportation fuel1. However, current biodiesel manufacturing routes employing soluble alkali based catalysts are very energy inefficient producing copious amounts of contaminated water waste during fuel purification. Technical advances in catalyst and reactor design and introduction of non-food based feedstocks are thus required to ensure that biodiesel remains a key player in the renewable energy sector for the 21st century. This presentation will give an overview of some recent developments in the design of solid acid and base catalysts for biodiesel synthesis. A particular focus will be on the benefits of designing materials with interconnected hierarchical macro-mesoporous networks to enhance mass-transport of viscous plant oils during reaction.

Heterogeneous catalysts for biodiesel production

The combination of dwindling oil reserves and growing concerns over carbon dioxide emissions and associated climate change is driving the urgent development of clean, sustainable energy supplies. Biodiesel is non-toxic and biodegradable, with the potential for closed CO2 cycles and thus vastly reduced carbon footprints compared with petroleum fuels. However, current manufacturing routes employing soluble catalysts are very energy inefficient and produce copious amounts of contaminated water waste. This review highlights the significant progress made in recent years towards developing solid acid and base catalysts for biodiesel synthesis. Issues to be addressed in the future are also discussed including the introduction of non-edible oil feedstocks, as well as technical advances in catalyst and reactor design to ensure that biodiesel remains a key player in the renewable energy sector for the 21st century.

Simulation process of biodiesel production over heterogeneous catalysts

Biodiesel production is a very promising area due to the relevance that it is an environmental-friendly diesel fuel alternative to fossil fuel derived diesel fuels. Nowadays, most industrial applications of biodiesel production are performed by the transesterification of renewable biological sources based on homogeneous acid catalysts, which requires downstream neutralization and separation leading to a series of technical and environmental problems. However, heterogeneous catalyst can solve these issues, and be used as a better alternative for biodiesel production. Thus, a heuristic diffusion-reaction kinetic model has been established to simulate the transesterification of alkyl ester with methanol over a series of heterogeneous Cs-doped heteropolyacid catalysts. The novelty of this framework lies in detailed modeling of surface reacting kinetic phenomena and integrating that with particle-level transport phenomena all the way through to process design and optimisation, which has been done for biodiesel production process for the first time. This multi-disciplinary research combining chemistry, chemical engineering and process integration offers better insights into catalyst design and process intensification for the industrial application of Cs-doped heteropolyacid catalysts for biodiesel production. A case study of the transesterification of tributyrin with methanol has been demonstrated to establish the effectiveness of this methodology.

Simulation process of biodiesel production over heterogeneous catalysts

Biodiesel production is a very promising area due to the relevance that it is an environmental-friendly diesel fuel alternative to fossil fuel derived diesel fuels. Nowadays, most industrial applications of biodiesel production are performed by the transesterification of renewable biological sources based on homogeneous acid catalysts, which requires downstream neutralization and separation leading to a series of technical and environmental problems. However, heterogeneous catalyst can solve these issues, and be used as a better alternative for biodiesel production. Thus, a heuristic diffusion-reaction kinetic model has been established to simulate the transesterification of alkyl ester with methanol over a series of heterogeneous Cs-doped heteropolyacid catalysts. The novelty of this framework lies in detailed modeling of surface reacting kinetic phenomena and integrating that with particle-level transport phenomena all the way through to process design and optimisation, which has been done for biodiesel production process for the first time. This multi-disciplinary research combining chemistry, chemical engineering and process integration offers better insights into catalyst design and process intensification for the industrial application of Cs-doped heteropolyacid catalysts for biodiesel production. A case study of the transesterification of tributyrin with methanol has been demonstrated to establish the effectiveness of this methodology.

On the active site in heterogeneous palladium selox catalysts

The nature of the active site in the Pd-catalysed aerobic selective oxidation of cinnamyl and crotyl alcohols has been directly probed by bulk and surface X-ray techniques. The importance of high metal dispersions and the crucial role of surface palladium oxide have been identified. © The Royal Society of Chemistry 2006.

Visualisation of heterogeneous data with the generalised generative topographic mapping

Heterogeneous and incomplete datasets are common in many real-world visualisation applications. The probabilistic nature of the Generative Topographic Mapping (GTM), which was originally developed for complete continuous data, can be extended to model heterogeneous (i.e. containing both continuous and discrete values) and missing data. This paper describes and assesses the resulting model on both synthetic and real-world heterogeneous data with missing values.

Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations

Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and load-balancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations.

Evolutionary market agents and heterogeneous service providers:achieving desired resource allocations

In future massively distributed service-based computational systems, resources will span many locations, organisations and platforms. In such systems, the ability to allocate resources in a desired configuration, in a scalable and robust manner, will be essential.We build upon a previous evolutionary market-based approach to achieving resource allocation in decentralised systems, by considering heterogeneous providers. In such scenarios, providers may be said to value their resources differently. We demonstrate how, given such valuations, the outcome allocation may be predicted. Furthermore, we describe how the approach may be used to achieve a stable, uneven load-balance of our choosing. We analyse the system's expected behaviour, and validate our predictions in simulation. Our approach is fully decentralised; no part of the system is weaker than any other. No cooperation between nodes is assumed; only self-interest is relied upon. A particular desired allocation is achieved transparently to users, as no modification to the buyers is required.

An adaptive IEEE 802.15.4 MAC layer transmission scheme for applications with heterogeneous traffic

IEEE 802.15.4 networks has the features of low data rate and low power consumption. It is a strong candidate technique for wireless sensor networks and can find many applications to smart grid. However, due to the low network and energy capacities it is critical to maximize the bandwidth and energy efficiencies of 802.15.4 networks. In this paper we propose an adaptive data transmission scheme with CSMA/CA access control, for applications which may have heavy traffic loads such as smart grids. The adaptive access control is simple to implement. Its compatibility with legacy 802.15.4 devices can be maintained. Simulation results demonstrate the effectiveness of the proposed scheme with largely improved bandwidth and power efficiency. © 2013 International Information Institute.

Temperature management for heterogeneous multi-core FPGAs using adaptive evolutionary multi-objective approaches

Heterogeneous multi-core FPGAs contain different types of cores, which can improve efficiency when used with an effective online task scheduler. However, it is not easy to find the right cores for tasks when there are multiple objectives or dozens of cores. Inappropriate scheduling may cause hot spots which decrease the reliability of the chip. Given that, our research builds a simulating platform to evaluate all kinds of scheduling algorithms on a variety of architectures. On this platform, we provide an online scheduler which uses multi-objective evolutionary algorithm (EA). Comparing the EA and current algorithms such as Predictive Dynamic Thermal Management (PDTM) and Adaptive Temperature Threshold Dynamic Thermal Management (ATDTM), we find some drawbacks in previous work. First, current algorithms are overly dependent on manually set constant parameters. Second, those algorithms neglect optimization for heterogeneous architectures. Third, they use single-objective methods, or use linear weighting method to convert a multi-objective optimization into a single-objective optimization. Unlike other algorithms, the EA is adaptive and does not require resetting parameters when workloads switch from one to another. EAs also improve performance when used on heterogeneous architecture. A efficient Pareto front can be obtained with EAs for the purpose of multiple objectives.

Visualisation of heterogeneous data with simultaneous feature saliency using Generalised Generative Topographic Mapping

Most machine-learning algorithms are designed for datasets with features of a single type whereas very little attention has been given to datasets with mixed-type features. We recently proposed a model to handle mixed types with a probabilistic latent variable formalism. This proposed model describes the data by type-specific distributions that are conditionally independent given the latent space and is called generalised generative topographic mapping (GGTM). It has often been observed that visualisations of high-dimensional datasets can be poor in the presence of noisy features. In this paper we therefore propose to extend the GGTM to estimate feature saliency values (GGTMFS) as an integrated part of the parameter learning process with an expectation-maximisation (EM) algorithm. The efficacy of the proposed GGTMFS model is demonstrated both for synthetic and real datasets.

Quality assessment of video transmission in heterogeneous networks as applied to real-time telecare

This work looks into video quality assessment applied to the field of telecare and proposes an alternative metric to the more traditionally used PSNR based on the requirements of such an application. We show that the Pause Intensity metric introduced in [1] is also relevant and applicable to heterogeneous networks with a wireless last hop connected to a wired TCP backbone. We demonstrate through our emulation testbed that the impairments experienced in such a network architecture are dominated by continuity based impairments rather than artifacts, such as motion drift or blockiness. We also look into the implication of using Pause Intensity as a metric in terms of the overall video latency, which is potentially problematic should the video be sent and acted upon in real-time. We conclude that Pause Intensity may be used alongside the video characteristics which have been suggested as a measure of the overall video quality. © 2012 IEEE.

Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.

Nanoengineering heterogeneous catalysts for the selective hydrogenation of key agrochemical derivatives

Rapidly rising world populations have sparked growing concerns over global food production to meet this increasing demand. Figures released by The World Bank suggest that a 50 % increase in worldwide cereal production is required by 2030. Primary amines are important intermediates in the synthesis of a wide variety of fine chemicals utilised within the agrochemical industry, and hence new 'greener' routes to their low cost manufacture from sustainable resources would permit significantly enhanced crop yields. Early synthetic pathways to primary amines employed stoichiometric (and often toxic) reagents via multi-step protocols, resulting in a large number of by-products and correspondingly high Environmental factors of 50-100 (compared with 1-5 for typical bulk chemicals syntheses). Alternative catalytic routes to primary amines have proven fruitful, however new issues relating to selectivity and deactivation have slowed commercialisation. The potential of heterogeneous catalysts for nitrile hydrogenation to amines has been demonstrated in a simplified reaction framework under benign conditions, but further work is required to improve the atom economy and energy efficiency through developing fundamental insight into nature of the active species and origin of on-stream deactivation. Supported palladium nanoparticles have been investigated for the hydrogenation of crotononitrile to butylamine (Figure 1) under favourable conditions, and the impact of reaction temperature, hydrogen pressure, support and loading upon activity and selectivity to C=C versus CºN activation assessed.