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.     

The impact of distraction mitigation strategies on driving performance

OBJECTIVES: An experiment was conducted to assess the effects of distraction mitigation strategies on drivers' performance and productivity while engaged in an in-vehicle information system task. BACKGROUND: Previous studies show that in-vehicle tasks undermine driver safety and there is a need to mitigate driver distraction. METHOD: An advising strategy that alerts drivers to potential dangers and a locking strategy that prevents the driver from continuing the distracting task were presented to 16 middle-aged and 12 older drivers in a driving simulator in two modes (auditory, visual) and two road conditions (curves, braking events). RESULTS: Distraction was a problem for both age groups. Visual distractions were more detrimental than auditory ones for curve negotiation, as depicted by more erratic steering, F (6, 155) = 26.76, p < .05. Drivers did brake more abruptly under auditory distractions, but this effect was mitigated by both the advising, t (155) = 8.37, p < .05, and locking strategies, t (155) = 8.49, p < .05. The locking strategy also resulted in longer minimum time to collision for middle-aged drivers engaged in visual distractions, F (6, 138) = 2.43, p < .05. CONCLUSIONS: Adaptive interfaces can reduce abrupt braking on curve entries resulting from auditory distractions and can also improve the braking response for distracted drivers. APPLICATION: These strategies can be incorporated into existing in-vehicle systems, thus mitigating the effects of distraction and improving driver performance.     

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.     

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     

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.     

Effect of dispersed phase feed time on the droplet size of Pickering emulsions produced in a stirred tank

The aim of this study was to investigate the effect of feed time of the oil phase on the average droplet size of Pickering emulsions produced in stirred tanks. Three types of impellers were tested: RT, up-pumping PBT (PBTU), and down-pumping PBT (PBTD). All the impellers were tested at two sizes, T/3 and T/2. All configurations were compared at constant tip speed, power per mass, and impeller Reynolds number. The droplet diameters were measured in Mastersizer® 3,000 (Malvern). The results showed that an increase in feed time causes a reduction in the average droplet size. At lower impeller speeds and higher feed times, the effect is more pronounced. It was found that some other geometric parameters also have an impact on the average droplet size.     

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.