Characterisation of Chinese alcoholic beverages by their congener contents

Using gas-liquid chromatography 33 Chinese alcoholic beverages were analysed for their contents of methanol, ethyl acetate, n-propanol, iso-butanol and iso-amyl alcohol (sum of 3-methyl-1-butanol and 2-methyl-1-butanol). A characteristic pattern of the ratios of some of the major congeners was observed. This was compared with the corresponding results for other alcoholic beverages obtained by the authors and by other workers. It was found that the ratios of iso-butanol: n-propanol, iso-amyl alcohol: n-propanol and methanol: n-propanol are useful criteria for the identification of Chinese alcoholic beverages.     

Intelligent Pool Decision System Using Zero-Order Sugeno Fuzzy System

This paper describes the approach of using the zero-order Sugeno fuzzy system to design an intelligent decision system for a game of pool. The decision system consists of three types of playing strategies for a normal game of pool: direct, combination and bank shots. For each of the playing scenario on the pool table, the decision system comes out with one output shot (or best shot): presenting which ball should be chosen and which path should it follow. The system has been tested step-by-step on a real pool game and results are tabulated in the paper. The decision is designed as the brain for a robotic pool system.     

Convolutional neural networks for classification of music-listening EEG: comparing 1D convolutional kernels with 2D kernels and cerebral laterality of musical influence

Neural Computing and Applications - This paper highlights the ability of convolutional neural networks (CNNs) at classifying EEG data listening to different kinds of music without the requirement...     

Melt index prediction with a mixture of Gaussian process regression with embedded clustering and variable selections

In this study, a penalized mixture of the Gaussian process regression model was proposed for the prediction of melt index (MI) in industrial polymer production. MI plays an important role in detecting the grade of a product. It is difficult to measure directly and is characterized by a large number of variables and multigrades. Because of multigrade products, in the development of soft sensors for MI prediction, it is not valid to assume unimodal Gaussian distribution of the data. To this end, the proposed method is capable of the simultaneous identification of significant variables and determination of important clusters of multigrade products. It is based on the shrinkage methods that have been shown to provide stable models that are more interpretable. Case studies are presented to show the features of the proposed method and its applicability to industrial MI prediction. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45237.     

Investigation of the effect of metal foam characteristics on the PCM melting performance in a latent heat thermal energy storage unit by pore-scale lattice Boltzmann modeling

Latent heat thermal energy storage (LHTES) has many advantages such as high energy density and phase change at a nearly constant temperature compared with sensible thermal energy storage or chemical energy storage techniques. However, one of its major drawbacks is the low thermal conductivity of phase change materials (PCMs) which impedes the heat transfer efficiency. High thermal conductivity metal foams could be added into the LHTES to enhance the heat transfer speed. Under this case, the investigation of the effects of metal foam porosity and pore size on the melting process is essential for improving the heat storage capability of LHTES. In this article, a pore-scale modeling of melting process in a LHTES unit filled with metal foams is carried out by enthalpy-based multiple-relaxation-time lattice Boltzmann method. The quartet structure generation set is used to generate the morphology of metal foams. In addition, a Compute Unified Device Architecture (CUDA) Fortran code is developed in this work for executing highly parallel computation through graphics processing units. The melting process in the PCMs is investigated in terms of porosity, pore size, nonuniform metal foam, hot wall temperature, and initial subcooled temperature to optimize the design of LHTES filled with metal foams.     

Effects of Fish Gelatin and Tea Polyphenol Coating on the Spoilage and Degradation of Myofibril in Fish Fillet During Cold Storage

Fish fillet is easily spoiled during storage. Antimicrobial edible coating of gelatin extracted from fish skins and bones and tea polyphenol (TP) was developed to inhibit the spoilage of fish fillet during cold storage. For coating containing 0.4 % TP and 1.2 % gelatin, the pH only slightly increased from 6.17 at day 0 to 6.32 at day 17 of cold storage, while the pH of control coating increased to 6.87 at day 17. Atomic force spectrometry was used to analyse the nanostructure of myofibril, which is the major component of fish muscle. The results showed that the length of myofibril from 0.4 % TP and 1.2 % gelatin group was greater than 15 μm, while the diameter and height were 3.38 and 0.59 μm, respectively, which exhibited the most intact nanostructure after 17 days of cold storage. Meanwhile, matrix-assisted laser desorption–ionisation–time-of-flight mass spectrometry result showed that TP delayed the degradation of myosin light chain 3 and troponin T in myofibril. Gas chromatography–mass spectrometry of volatile organic compounds (VOCs) also showed that 0.4 % TP and 1.2 % gelatin coating group had minimal production of spoilage markers, such as 1-octen-3-ol, 2-methyl propanoic acid and dimethyl sulfide. The microbial analysis showed that the aerobic mesophilic/psychrotrophic count, yeasts and moulds of 0.4 % TP and 1.2 % gelatin group were significantly lower than the control group. Therefore, 0.4 % TP and 1.2 % gelatin coating showed the best antimicrobial effect and can be used to maintain the nanostructure of fish fillet and prevent the spoilage during cold storage.     

Design and fabrication of diffusive solar cell window

A diffusive solar cell window was designed and fabricated with uniformly distributed nanocomposite particles in a light diffusive plate that was sandwiched between two glass layers. The entire composite construction transfers light radiation to solar cells at the edge of the windows. It is based on a new combination of existing technologies because of it uses mature, mass-produced components - solar cells - as well as nanocomposite particles that are embedded inside the light-guide plate. They are integrated using an inexpensive and widely used method for making building windows. The result is an inexpensive, strong, stable, view quality-preserving solar energy-harvesting window that has no close competition. The diffusive solar cell window does not suffer from aging, and products that are made using diffusive solar cell window technology will be new entries to the solar power generation window market.     

A review on conventional technologies and emerging process intensification (PI) methods for biodiesel production

Biodiesel is commonly produced from lipid feedstock, animal fats and waste cooking oil by transesterification reaction. Considering the depletion of fossil fuel, biodiesel is gaining more attention as a renewable and environmental friendly fuel. The rapid growth of biodiesel industry thereafter has raised concerns to many existing commercial biodiesel enterprises. The major issues like feedstock flexibility, yield productivity and environmental impact are always the challenges to the continuous growth of conventional biodiesel processing technology. The processing of biodiesel is greatly hinged on the rich scientific background and technology development for better process advancement. The present paper reviews various concerns raised from the commercial biodiesel processing technology. It also addresses some innovative process intensification (PI) technologies, which likely bring appropriate technological improvement for biodiesel production.     

Extending the validity of lumped capacitance method for large Biot number in thermal storage application

In a typical thermal energy storage system, a heat transfer fluid is usually used to deposit/extract heat when it flows through a packed bed of solid thermal storage material. A one-dimensional model of the heat transfer and energy storage/extraction for a packed-bed thermal storage system has been developed previously by the authors. The model treats the transient heat conduction in the thermal storage material by using the lumped capacitance method, which is not valid when the Biot number is large. The current work presents an effective heat transfer coefficient between the solid and fluid for large Biot numbers. With the corrected heat transfer coefficient, the lumped capacitance method can be applied to model the thermal storage in a wide range of Biot numbers. Four typical structures for the solid thermal storage material are considered. Formulas for the effective heat transfer coefficient (and effective Biot number) are presented. To verify the prediction by the lumped capacitance method using the effective heat transfer coefficient, we compare the results to the corresponding analytical solutions. The results are in very good agreement. The effective heat transfer coefficient extended the validity of the lumped capacitance method to large Biot numbers, which is of significance to the analysis of thermal energy storage systems.     

A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiency

One of the ways to achieve energy efficiency in various residential electrical appliances is with energy usage feedback. Research work done showed that with energy usage feedback, behavioural changes by consumers to reduce electricity consumption contribute significantly to energy efficiency in residential energy usage. In order to improve on the appliance-level energy usage feedback, appliance disaggregation or non-intrusive appliance load monitoring (NIALM) methodology is utilized. NIALM is a methodology used to disaggregate total power consumption into individual electrical appliance power usage. In this paper, the electrical signature features from the publicly available REDD data set are extracted by the combination of identifying the ON or OFF events of appliances and goodness-of-fit (GOF) event detection algorithm. The k-nearest neighbours (k-NN) and naive Bayes classifiers are deployed for appliances’ classification. It is observed that the size of the training sets effects classification accuracy of the classifiers. The novelty of this paper is a systematic approach of NIALM using few training examples with two generic classifiers (k-NN and naive Bayes) and one feature (power) with the combination of ON-OFF based approach and GOF technique for event detection. In this work, we demonstrated that the two trained classifiers are able to classify the individual electrical appliances with satisfactory accuracy level in order to improve on the feedback for energy efficiency.