Insight into biochar properties and its cost analysis

Biochars (BCs) are widely produced and used for the remediation of environmental contaminants as bio-sorbents. In this review, statistical analysis of different BC physico–chemical properties was conducted. It was observed that woody materials are the most suitable for preparing BCs, among many other potential raw materials such as food wastes and agricultural materials. Currently BCs are produced through a variety of thermal treatment processes between 300 and 900 °C, among which slow pyrolysis is widely used due to its moderate operating conditions and optimization of BC yields. Hydrothermal carbonisation (HTC) is also an effective approach for BC production under certain conditions. As pyrolysis temperature is increased, the carbon content, ash content, surface area, and pore volume tend to be increased while the yield, hydrogen, oxygen, nitrogen content, and H/C and O/C molar ratios tend to decrease. The economic feasibility of BCs depends on a range of factors from raw material price to efficient production technologies. Thus, the overall cost equation of a pilot BC production plant together with the cost equation for BC regeneration has been proposed. The future research directions of BCs are also elaborated.     

The significant effect of turbulence characteristics on heat transfer enhancement using nanofluids: A comprehensive review

In this article, turbulent flow characteristic of nanofluids is thoroughly reviewed. Turbulent flows have unique characteristics and are preferred in many industrial applications. Therefore, this paper reviews different techniques used to enhance heat transfer using nanofluids within turbulent regime. This paper also presents the effects of some important parameters such as nanoparticle type, nanoparticles concentration, and Reynolds number on heat transfer rate. Studies on numerical techniques are also discussed. Finally, the conclusions and important summaries are presented according to the data collected.     

Study of synthesis,stability and thermo-physical properties of graphene nanoplatelet/platinum hybrid nanofluid

In the present study a new synthesis method has been introduced for the decoration of platinum (Pt) on the functionalized graphene nanoplatelet (GNP) and also highlighted the preparation method of nanofluids. GNP–Pt uniform nanocomposite was produced from a simple chemical reaction procedure, which included acid treatment for functionalization of GNP. The surface characterization was performed by various techniques such as XRD, FESEM and TEM. The effective thermal conductivity, density, viscosity, specific heat capacity and stability of functionalized GNP–Pt water based nanofluids were investigated in different instruments. The GNP–Pt hybrid nanofluids were prepared by dispersing the nanocomposite in base fluid without adding any surfactant. The examined nanofluids were stable and no significant sedimentation was observed for a long time (22 days). Thermal conductivity of GNP–Pt nanocomposite dispersed in distilled water nanofluids shows an enhancement of 17.77% at 40 °C and 0.1% weight concentration.     

Text analytics in industry: Challenges,desiderata and trends

The recent decades have witnessed an unprecedented expansion in the volume of unstructured data in digital textual formats. Companies are now starting to recognize the potential economic value lying untapped in their text data repositories and sources, including external ones, such as social media platforms, and internal ones, such as safety reports and other company-specific document collections. Information extracted from these textual data sources is valuable for a range of enterprise application and for informed decision making. In this article we provide a systematic review of the current state of the art in the application of text analytics in industry. Our review is structured along three dimensions: the application context, the methods and techniques utilized, and the evaluation procedure. Based on the review, we identify the different challenges and constraints that an real-world, industrial environment imposes on text analytics techniques, as opposed to their deployment in more controlled, research environments. In addition, we formulate a set of desiderata that text analytics techniques should satisfy in order to alleviate these challenges and to ensure their successful deployment in industry. Furthermore, we discuss future trends in text analytics and their potential application in industry.     

Artistic information extraction from Chinese calligraphy works via Shear-Guided filter

Chinese calligraphy is a unique visual art, and is still emitting fascinating artistic brilliance and charm. The artistic beauty of Chinese calligraphy is constituted by two elements, i.e., form and spirit, which attract continual attentions by calligraphy artists. In this paper, we present Shear-Guided filter to extract the artistic information. Shear-Guided filter is designed by combining the shear transformation and the guided filter. It has both the edge-preserving smoothing property from the guided filter and the linear multidirectional property from the shear transformation. Shear-Guided filter can extract more spirit information from the calligraphy images, especially in the half-dry strokes. Furthermore, the previous evaluation parameters consider all pixels equally, which is against the truth. Here we put forward three new parameters to solve this issue. Finally, experiments show that Shear-Guided filter can extract the information of form and spirit more accurately, and the new evaluation parameters have better performances in evaluation.     

Image selective restoration using multi-scale variational decomposition

In this paper we propose a multi-scale variational decomposition model for image selective restoration. Firstly, we introduce a single-parameter (BV, G, L²) variational decomposition functional and theoretically analyze the relationship between the parameter and the scale of image features. And then, by replacing the fixed scale parameter with a varying sequence in the single-parameter decomposition functional, we obtain the multi-scale variational decomposition which can decompose the input image into a series of image slices of different scales. Furthermore, we show some properties and prove the convergence of the multi-scale decomposition. Finally, we introduce an alternating and iterative method based on Chambolle’s projection algorithm to numerically solve the multi-scale variational decomposition model. Experiments are conducted on both synthetic and real images to demonstrate the effectiveness of the proposed multi-scale variational decomposition. In addition, we use the multi-scale variational decomposition to achieve image selective restoration, and compare it with several state-of-the-art models in denoising application. The numerical results show that our model has better performance in terms of PSNR and SSIM indexes.     

A review of two-step sintering for ceramics

The development of high-density ceramic materials with fine-grained microstructures has been studied to considerably improve their properties for high-performance applications. Many alternatives have been searched to refine their microstructure by changing their composition and/or processing. Among such alternatives, the densification of ceramic materials by sintering curve control is an effective, simple and economical microstructure refinement method. Thus, different thermal treatment techniques such as spark plasma sintering and microstructural forms of control such as the control of sintering conditions have been used to obtain nanostructured materials. One of the techniques widely used in recent years is two-step sintering. Two-step sintering (TSS) is a promising method used to obtain high-density bodies and smaller grain sizes. Two TSS methodologies are known: sintering with thermal pretreatment at a low sintering temperature, followed by a second stage at elevated temperature, and the more recent approach presented by Chen and Wang, which has been the most widely used. In addition to the sintering conditions (temperature, heating rate and sintering holding times) that must be suitable for each composition type, the starting materials, particle size and processing method may influence the obtained microstructure, especially the reduced grain size and increased densification. The current review of two-step sintering presents the effect of this technique on the grain density and sizes of different ceramic materials. The influence of the addition of doping agents and its effect on the mechanical properties in different systems is also presented in the current study.     

An energy flow analysis in a paper-based industry

Industrial sector is the largest user of energy in India and many parts of the world. More than half of the total energy is used in industries to operate various energy-using machineries. Energy conservation is a cost-effective approach in any energy optimization program that can be implemented in industrial activities. Energy-intensive industries in developing economies offer significant energy-saving potential owing to the technology gap as well as lower operating efficiency. A detailed energy flow analysis was conducted in a paper carton manufacturing unit in India to quantify the energy-saving potential. The study revealed that the compressed air leakage is responsible for about 50% of the total energy loss. The specific electrical and thermal energy consumption was estimated and found to be 91.85?kWh/ton and 1,619?MJ/ton of biomass, respectively. Annual energy saving potential was found to be 5.9% of the total annual energy consumption. The cost of DG power is found to be 389% higher than the grid power. The share of self-generated DG power is estimated to be 41% of the total power consumed. Electrical energy intensity of the plant could be reduced by 6.36% implementing energy conservation measures proposed in this study.     

An experimental investigation on the effects of exponential window and impact force level on harmonic reduction in impact-synchronous modal analysis

A novel method called Impact-synchronous modal analysis (ISMA) was proposed previously which allows modal testing to be performed during operation. This technique focuses on signal processing of the upstream data to provide cleaner Frequency response function (FRF) estimation prior to modal extraction. Two important parameters, i.e., windowing function and impact force level were identified and their effect on the effectiveness of this technique were experimentally investigated. When performing modal testing during running condition, the cyclic loads signals are dominant in the measured response for the entire time history. Exponential window is effectively in minimizing leakage and attenuating signals of non-synchronous running speed, its harmonics and noises to zero at the end of each time record window block. Besides, with the information of the calculated cyclic force, suitable amount of impact force to be applied on the system could be decided prior to performing ISMA. Maximum allowable impact force could be determined from nonlinearity test using coherence function. By applying higher impact forces than the cyclic loads along with an ideal decay rate in ISMA, harmonic reduction is significantly achieved in FRF estimation. Subsequently, the dynamic characteristics of the system are successfully extracted from a cleaner FRF and the results obtained are comparable with Experimental modal analysis (EMA).     

An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism

Alcoholism affects the structure and functioning of brain. Electroencephalogram (EEG) signals can depict the state of brain. The EEG signals are ensemble of various neuronal activity recorded from different scalp regions having different characteristics and very low magnitude in microvolts. These factors make human interpretation difficult and time consuming to analyze these signals. Moreover, these highly varying EEG signals are susceptible to inter/intra variability errors. So, a Computer-Aided Diagnosis (CAD) can be used to identify the alcoholic and normal subjects accurately. However, these EEG signals exhibit nonlinear and non-stationary properties. Therefore, it needs much effort in deciphering the diagnostic evidence from them using linear time and frequency-domain methods. The nonlinear parameters together with time-frequency/scale domain methods can help to detect tiny changes in these signals. The correntropy is nonlinear indicator which characterizes the dynamic behavior of EEG signals in time-scale domain. In this paper, we present a new way for diagnosis of alcoholism using Tunable-Q Wavelet Transform (TQWT) based features derived from EEG signals. The feature extraction is performed using TQWT based decomposition and extracted Centered Correntropy (CC) from the forth decomposed detail sub-band. The Principal Component Analysis (PCA) is used for feature reduction followed by Least Squares-Support Vector Machine (LS-SVM) for classifying normal and alcoholic EEG signals. In order to make sure reliable classification performance, 10-fold cross-validation scheme is adopted. Our proposed system is able to diagnose the alcoholic and normal EEG signals, with an average accuracy of 97.02%, sensitivity of 96.53%, specificity of 97.50% and Matthews correlation coefficient of 0.9494 for Q-factor (Q) varying between 3 and 8 using Radial Basis Function (RBF) kernel function. Also, we have established a novel Alcoholism Risk Index (ARI) using three clinically significant features to discriminate the given classes by means of a single number. This system can be used for automated diagnosis and monitoring of alcoholic subjects to evaluate the effect of treatment.