Thermal energy storage properties of polyethylene glycol grafted styrenic copolymer as novel solid-solid phase change materials

Polymeric solid-solid phase change materials (S-SPCMs) are functional materials with phase transition-heat storing/releasing ability. With this respect, a series of polyethylene glycol (PEG) grafted styrenic copolymer were produced as novel S-SPCMs. PEGs with three different molecular weights were used for synthesis of isocyanate-terminated polymers (ITPs). To achieve cross-linking S-SPCMs, the ITPs were grafted with styrene-co-ally alcohol) (PSAA) at three different PSAA:PEG mole ratios. The produced polymers were characterized using Fourier transform infrared (FT-IR), proton nuclear magnetic resonance (¹H NMR), and X-ray diffraction (XRD) technique. The crystalline-amorphous phase transitions of the polymers were examined using polarized optical microscopy (POM). The FT-IR, NMR, and XRD results confirmed the expected chemical structures and crystallization performances of the polymers. Thermal energy storage (TES) properties of the S-SPCMs were determined by differential scanning calorimetry (DSC). The DSC results revealed that the polymers with grafting ratio of PSAA:PEG(1:1) had phase transition enthalpies between about 74 and 142 J/g and phase transition temperatures between about 26°C and 57°C. Thermogravimetric analysis (TGA) measurements demonstrated that the S-SPCMs were resistant to thermal decomposition until about 300°C. Thermal conductivities of the produced S-SPCMs were measured in a range of about 0.18 to 0.19 W/mK. Furthermore, TES properties of the S-SPCMs were slightly changed as their chemical structures were remained after 5000 thermal cycles. By overall evaluation of the findings, it can be foreseen that particularly PSAA-g-PEG(1:1) polymers can be considered as promising S-SPCMs for some TES practices such as air conditioning of buildings, thermoregulation of food packages, automobile components, electronic devices, and solar photovoltaic panels.     

Dissolution kinetics of an oxidized copper ore in water saturated by chlorine

Dissolution kinetics of an oxidized copper ore (mainly malachite) in water saturated by Cl₂ has been studied. The effect of particle size, flow rate of the gas, temperature and solid-liquid ratio has been determined. The dissolution rate increased with decrease in particle size and solid-liquid ratio and with increase in the gas flow rate and temperature. It has been found that the dissolution proceeds in two stages and is controlled by diffusion through the ash layer in each stage. The activation energies for the first and second stage are 27.15 and 20.21 kJ mol^?1, respectively.     

A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications

Accurate and real-time product demand forecasting is the need of the hour in the world of supply chain management. Predicting future product demand from historical sales data is a highly non-linear problem, subject to various external and environmental factors. In this work, we propose an optimised forecasting model - an extreme learning machine (ELM) model coupled with the Harris Hawks optimisation (HHO) algorithm to forecast product demand in an e-commerce company. ELM is preferred over traditional neural networks mainly due to its fast computational speed, which allows efficient demand forecasting in real-time. Our ELM-HHO model performed significantly better than ARIMA models that are commonly used in industries to forecast product demand. The performance of the proposed ELM-HHO model was also compared with traditional ELM, ELM auto-tuned using Bayesian Optimisation (ELM-BO), Gated Recurrent Unit (GRU) based recurrent neural network and Long Short Term Memory (LSTM) recurrent neural network models. Different performance metrics, i.e., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) were used for the comparison of the selected models. Horizon forecasting at 3 days and 7 days ahead was also performed using the proposed approach. The results revealed that the proposed approach is superior to traditional product demand forecasting models in terms of prediction accuracy and it can be applied in real-time to predict future product demand based on the previous week’s sales data. In particular, considering RMSE of forecasting, the proposed ELM-HHO model performed 62.73% better than the statistical ARIMA(7,1,0) model, 40.73% better than the neural network based GRU model, 34.05% better than the neural network based LSTM model, 27.16% better than the traditional non-optimised ELM model with 100 hidden nodes and 11.63% better than the ELM-BO model in forecasting product demand for future 3 months. The novelty of the proposed approach lies in the way the fast computational speed of ELMs has been combined with the accuracy gained by tuning hyperparameters using HHO. An increased number of hyperparameters has been optimised in our methodology compared to available models. The majority of approaches to improve the accuracy of ELM so far have only focused on tuning the weights and the biases of the hidden layer. In our hybrid model, we tune the number of hidden nodes, the number of input time lags and even the type of activation function used in the hidden layer in addition to tuning the weights and the biases. This has resulted in a significant increase in accuracy over previous methods. Our work presents an original way of performing product demand forecasting in real-time in industry with highly accurate results which are much better than pre-existing demand forecasting models.

     

Experimental application of nonlinear minimum variance estimation for fault detection systems

The purpose of this paper is to present an experimental design and application of a novel model-based fault detection technique by using a nonlinear minimum variance (NMV) estimator. The NMV estimation technique is used to generate a residual signal which is then used to detect faults in the system. The main advantage of the approach is the simplicity of the nonlinear estimator theory and the straightforward structure of the resulting solution. The proposed method is implemented and validated experimentally on DC servo system. Experimental results demonstrate that the technique can produce acceptable performance in terms of fault detection and false alarm.     

Adaptive and intelligent wireless sensor networks through neural networks: an illustration for infrastructure adaptation through Hopfield network

This paper proposes embedding an artificial neural network into a wireless sensor network in fully parallel and distributed computation mode. The goal is to equip the wireless sensor network with computational intelligence and adaptation capability for enhanced autonomous operation. The applicability and utility of the proposed concept is demonstrated through a case study whereby a Hopfield neural network configured as a static optimizer for the weakly-connected dominating set problem is embedded into a wireless sensor network to enable it to adapt its network infrastructure to potential changes on-the-fly and following deployment in the field. Minimum weakly-connected dominating set defined for the graph model of the wireless sensor network topology is employed to represent the network infrastructure and can be recomputed each time the sensor network topology changes. A simulation study using the TOSSIM emulator for TinyOS-Mica sensor network platform was performed for mote counts of up to 1000. Time complexity, message complexity and solution quality measures were assessed and evaluated for the case study. Simulation results indicated that the wireless sensor network embedded with Hopfield neural network as a static optimizer performed competitively with other local or distributed algorithms for the weakly connected dominating set problem to establish its feasibility.     

Flow rate-insensitive microparticle separation and filtration using a microchannel with arc-shaped groove arrays

Inertial microfluidics can separate microparticles in a continuous and high-throughput manner, and is very promising for a wide range of industrial, biomedical and clinical applications. However, most of the proposed inertial microfluidic devices only work effectively at a limited and narrow flow rate range because the performance of inertial particle focusing and separation is normally very sensitive to the flow rate (Reynolds number). In this work, an innovative particle separation method is proposed and developed by taking advantage of the secondary flow and particle inertial lift force in a straight channel (AR = 0.2) with arc-shaped groove arrays patterned on the channel top surface. Through the simulation results achieved, it can be found that a secondary flow is induced within the cross section of the microchannel and guides different-size particles to the corresponding equilibrium positions. On the other hand, the effects of the particle size, flow rate and particle concentration on particle focusing and separation quality were experimentally investigated. In the experiments, the performance of particle focusing, however, was found relatively insensitive to the variation of flow rate. According to this, a separation of 4.8 and 13 µm particle suspensions was designed and successfully achieved in the proposed microchannel, and the results show that a qualified particle separation can be achieved at a wide range of flow rate. This flow rate-insensitive microfluidic separation (filtration) method is able to potentially serve as a reliable biosample preparation processing step for downstream bioassays.     

Eudragit S (methyl methacrylate methacrylic acid copolymer)/fatty acid blends as form‐stable phase change material for latent heat thermal energy storage

By composing (Eudragit S) with fatty acids (stearic acid (SA), palmitic acid (PA), and myristic acid (MA)), form‐stable phase change materials (PCMs), which can retain the same shape in a solid state even when the temperature of the PCMs is over the melting points of the fatty acids, are prepared. The compatibility of fatty acids with the Eudragit S is proved by microscopic investigation and infrared (FTIR) spectroscopy. The melting and crystallization temperatures and the latent heats of melting and crystallization of the form‐stable PCMs are measured by Differential Scanning Calorimetry (DSC) method. The maximum mass percentage of all fatty acids in the form‐stable PCMs is found as 70%, and no leakage of fatty acid is observed at the temperature range of 50–70°C for several heating cycles. Thermal properties obtained from the DSC analysis indicate that the Eudragit S/fatty acid blends as form‐stable PCM have great potential for passive solar latent heat thermal energy storage (LHTES) applications in terms of their satisfactory thermal properties and utility advantage. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 1402–1406, 2006     

Particle inertial focusing and its mechanism in a serpentine microchannel

Particle inertial focusing in a curved channel promises a big potential for lab-on-a-chip applications. This focusing concept is usually based on the balance of inertial lift force and the drag of secondary flow. This paper proposes a new focusing concept independent of inertial lift force, relying solely on secondary flow drag and particle centrifugal force. Firstly, a focusing mechanism in a serpentine channel is introduced, and some design considerations are described in order to make the proposed focusing concept valid. Then, numerical modelling based on the proposed focusing mechanism is conducted, and the numerical results agree well with the experimental ones, which verify the rationality of proposed mechanism. Thirdly, the effects of flow condition and particle size on the focusing performance are studied. The effect of particle centrifugal force on particle focusing in a serpentine microchannel is carefully evaluated. Finally, the speed of focussed particles at the outlet is measured by a micro-PIV, which further certifies the focusing positions of particles within the cross section. Our study provides insights into the role of centrifugal force on inertial focusing. This paper demonstrates for the first time that a single focusing streak can be achieved in a symmetric serpentine channel. The simple serpentine microchannel can easily be implemented in a single-layer microfluidic device. No sheath flow or external force field is needed allowing a simple operation in a more complex lab-on-a-chip system.     

Modeling and visualization of lifecycle building performance assessment

Lifecycle building performance assessment (LBPA) ensures that buildings perform and operate as intended during building lifecycle. Such assessment activities are typically multi-phase and multi-disciplinary, and generate large amounts of information that need efficient management. During the lifecycle of one building, multiple assessment methods are employed semi-concurrently and with a great deal of information overlap between successive phases. This information is semantically rich and context-dependent, reflecting the local perspective that it exists within. Usage of separate and disconnected tools for each method results in information fragmentation and redundancy, posing problems for well-informed decision making. The objective of this research is to develop an integral reference model, CLIP (Computational support for Lifecycle Integral Performance assessment), that aims to improve the efficiency and quality of existing performance assessment practices. The model provides flexible, modular, and extensible data structures and algorithms for the representation, transformation, integration, and visualization of performance information. The initial results support the CLIP model’s ability to deal with the local volatility and variation in the processes and information content that is being managed, while requiring further refinement and extension work to achieve a customized solution to fit in different contextual settings.     

Thermal issues in machine tools

This paper presents a review of the latest research activities and gives an overview of the state of the art in understanding changes in machine tool performance due to changes in thermal conditions (thermal errors of machine tools). The topics are focused on metal cutting machine tools, especially on turning and milling machines as well as machining centres. The topics of the paper thermal issues in machine tools include measurement of temperatures and displacements, especially displacements at the tool centre point, computations of thermal errors of machine tools, and reduction of thermal errors. Computing the thermal errors of machine tools include both, temperature distribution and displacements. Shortly addressed is also to avoid thermal errors with temperature control, the influence of fluids and a short link to energy efficiency of machine tools. The paper presents the summary of research work in the past and current. Research challenges in order to achieve a thermal stable machine tool are discussed. The paper apprehend itself as an update and not a substitution of two published keynote papers of Bryan et al. [28] in 1990 and Weck et al. [199] in 1995.