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.     

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     

Supporting intelligent and trustworthy maritime path planning decisions

The risk of maritime collisions and groundings has dramatically increased in the past five years despite technological advancements such as GPS-based navigation tools and electronic charts, which may add to, instead of reduce, workload. We propose that an automated path planning tool for littoral navigation can reduce workload and improve the overall system efficiency, particularly under time pressure. To this end, a maritime automated path planner (MAPP) was developed, incorporating information requirements developed from a cognitive task analysis, with special emphasis on designing for trust. Human-in-the-loop experimental results showed that MAPP was successful in reducing the time required to generate an optimized path, as well as reducing path lengths. The results also showed that while users gave the tool high acceptance ratings, they rated the MAPP as average for trust, which we propose is the appropriate level of trust for such a system.     

Self-limiting low-temperature growth of crystalline AlN thin films by plasma-enhanced atomic layer deposition

We report on the self-limiting growth and characterization of aluminum nitride (AlN) thin films. AlN films were deposited by plasma-enhanced atomic layer deposition on various substrates using trimethylaluminum (TMA) and ammonia (NH₃). At 185 °C, deposition rate saturated for TMA and NH₃ doses starting from 0.05 and 40 s, respectively. Saturative surface reactions between TMA and NH₃ resulted in a constant growth rate of ~ 0.86 Å/cycle from 100 to 200 °C. Within this temperature range, film thickness increased linearly with the number of deposition cycles. At higher temperatures (≥ 225 °C) deposition rate increased with temperature. Chemical composition and bonding states of the films deposited at 185 °C were investigated by X-ray photoelectron spectroscopy. High resolution Al 2p and N 1s spectra confirmed the presence of AlN with peaks located at 73.02 and 396.07 eV, respectively. Films deposited at 185 °C were polycrystalline with a hexagonal wurtzite structure regardless of the substrate selection as determined by grazing incidence X-ray diffraction. High-resolution transmission electron microscopy images of the AlN thin films deposited on Si (100) and glass substrates revealed a microstructure consisting of nanometer sized crystallites. Films exhibited an optical band edge at ~ 5.8 eV and an optical transmittance of > 95% in the visible region of the spectrum.     

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.     

The Fate of Enteric Bacteria in Relation to Suspended Particles in Sea Water

The investigation was carried out to determine the effect of suspended particles on the survival of selected enteric bacteria (i.e. Escherichia coli and enterococci) which are important indicators of faecal pollution in marine waters. Rates of bacterial removal under varying experimental conditions (i.e. particle concentration, sewage content, and degree of mixing) were determined. The combined effect of the factors concerned with the removal rate of bacteria was evaluated by statistical analyses carried out on the experimental data utilizing the 'complete factorial' experimental design, and a good correlation between bacteria and particles was obtained.     

Poly(vinyl alcohol) nanocomposites with sepiolite and heat‐treated sepiolites

Poly(vinyl alcohol) (PVA) nanocomposites with pristine sepiolite and heat‐treated (HT) sepiolites were prepared by the method of solution dispersion. The measurements of XRD, FTIR, TEM, and AFM were used for the characterization of the nanocomposites. Furthermore, thermal and optical properties were investigated by TG/DTG/DTA and UV‐visible transmission spectra, respectively. Both the effects of sepiolite/polymer ratio and the structural changes in sepiolite on heating were examined in terms of changes in the properties of the nanocomposites. The addition of sepiolite/HT sepiolites into the PVA matrix resulted in a decrease in the thermal decomposition temperatures of the nanocomposites because of the fact that sepiolite and HT sepiolites facilitated the elimination of the water and acetate groups from the PVA in the second step based on the TG/DTG studies. The HT sepiolites‐PVA nanocomposites had lower thermal stability and more influenced optical clarity than those of the sepiolite PVA, at the same filler levels. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

Moisture Distribution Changes and Wetwood Behavior in Subalpine Fir Wood during Drying Using High X-Ray Energy Industrial CT Scanner

In some species, such as subalpine fir (Abies lasiocarpa [Hook] Nutt), the water content of the confined zones in heartwood is as high as or greater than that of sapwood. Such wet zones of heartwood are referred to as “wetpocket” or “wetwood.” Wood products from subalpine fir forests are adversely affected by the wetwood-associated problems, particularly during the drying process. The objectives of the study were as follows: (1) to investigate feasibility of a high X-ray energy industrial computed tomography (ICT) scanner for imaging wetwood; and (2) to determine changes of the 2-D and 3-D moisture profiles (from core to shell) at different drying times.

Although medical CT scanning has been used for attaining signal intensity profiles of typical wood at different drying times, the technology has not, to date, been used for the study of wetwood phenomenon. This study presents, for the first time, results from the ICT imaging of the wetwood phenomenon. The results indicate that the ICT imaging system provides a powerful technique for imaging wetwood at different drying times. In addition, the results show that during the initial phase of drying, almost flat moisture profiles were observed in all wood types except for the wetwood, which showed a relatively higher moisture profile. A much slower (sluggish) drying development pattern at each increment from core to shell was found within the wetwood zone than normal wood regions along the width, thickness, and length of the board.