Optimization of Time and Saving Water, Energy through Using Regulator with Hydrogen Peroxide in Exhaust Bleaching Process

On this investigation so as to optimize time, water and energy of exhaust bleaching procedure one test turned into accomplished by using modern bleaching regulator Imerol® Blue liquid (mixture of carboxylic acid salts and ethoxylated fatty alcohols) with caustic soda, H2O2 (50%), Bactosol AP (peroxide killer), Acetic acid whilst others become conducted the use of classical wetting agent Imerol (PCLF), sequestering agent (EDTA), stabilizer (NaSi03), caustic soda, H2O2 (35%). In this take a look at demonstrated that (a) Applied the bleaching regulator at 110°C decreased the bleaching time 15 min that accelerated productiveness in comparison to classical bleaching agent. (b) While Bleaching achieved with Imerol® Blue liquid absorbency of cotton knit material changed into stepped forward rather than classical bleaching process. (c) Modern bleaching method decreased weight loss percentage of cotton knit material as compared with classical method. (d) Whilst bleaching regulator Imerol® Blue liquid implemented on cotton knit cloth no rinsing became wished that’s leads the minimization of bleaching time and water with in comparison to classical bleaching. For the outcome effluent volumes decreased that gives benefit on the surroundings and ecology. (e) Bleach regulator Imerol® Blue liquid allows to consume caustic soda at neutral pH in knit cloth as evaluation with classical bleaching. For the result neutralization with acid turned into prevented in modern bleaching technique. (f) For the bleach regulator, wetting, sequestering trait of imerol® Blue liquid no longer simplest leads the minimization of energy, alkaline quantity, degradation of cellulose in method but also advanced degree of whiteness, uniformity and improved dye-potential

State estimation within ied based smart grid using kalman estimates

State Estimation is a traditional and reliable technique within power distribution and control systems. It is used for building a topology of the power grid network based on state measurements and current operational state of different nodes & buses. The protection of sensors and measurement units such as Intelligent Electronic Devices (IED) in Central Energy Management System (CEMS) against False Data Injection Attacks (FDIAs) is a big concern to grid operators. These are special kind of cyber-attacks that are directed towards the state & measurement data in such a way that mislead the CEMS into making incorrect decisions and create generation load imbalance. These are known to bypass the traditional bad data detection systems within central estimators. This paper presents the use of an additional novel state estimator based on Kalman filter along with traditional Distributed State Estimation (DSE) which is based on Weighted Least Square (WLS). Kalman filter is a feedback control mechanism that constantly updates itself based on state prediction and state correction technique and shows improvement in the estimates. The additional estimator output is compared with the results of DSE in order to identify anomalies and injection of false data. We evaluated our methodology by simulating proposed technique using MATPOWER over IEEE-14, IEEE-30, IEEE-118, IEEE-300 bus. The results clearly demonstrate the superiority of the proposed method over traditional state estimation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Should disaster management strategies in Bangladesh be just about constructing new shelters?

With a population of over 143 million people and a population density of more than 1,200 persons per km2 Bangladesh is a very densely populated country. The country’s geographic location in the waters of Bay of Bengal, often the source of tropical cyclones and storm surges, makes Bangladesh one of the most natural disasters prone nations in the world. A severe tropical cyclone hits the country, every 3 years on average. As 16 major cyclones have hit the country since 1960 with the loss of nearly 500,000 lives, multi-purpose cyclone shelters – that can provide refuge to susceptible population in the events of natural hazards and to a certain extent with the utility of community functionalities during normal times – have become a vital component of disaster management strategies. Country has already constructed more than 2,500 such shelters across 16 of the most disaster prone coastal districts. This paper uses content analysis of disaster management policies, and programs in order to comprehend and assess the distributions of shelters with a lens of integrated strategic asset management framework. Analysis of secondary data indicates that existing cyclone shelters are not equitably distributed to cater the needs of the highly vulnerable population. In the backdrop of the recommendation of The World Bank [TWB] that the country needs 5,500 new shelters (TWB, 2010), this paper contends that future construction of cyclone shelters must be need as well as evidence-based in order to ensure that highly vulnerable population benefits from cyclone shelters the most

Inchoate fault detection framework: adaptive selection of wavelet nodes and cumulant orders

Inchoate fault detection for machine health monitoring (MHM) demands high level of fault classification accuracy under poor signal-to-noise ratio (SNR) which persists in most industrial environment. Vibration signals are extensively used in signature matching for abnormality detection and diagnosis. In order to guarantee improved performance under poor SNR, feature extraction based on statistical parameters which are immune to Gaussian noise becomes inevitable. This paper proposes a novel framework for adaptive feature extraction based on higher order cumulants (HOCs) and wavelet transform (WT) (AFHCW) for MHM. Features extracted based on HOCs have the tendency to mitigate the impact of Gaussian noise. WT provides better time and frequency domain analysis for the nonstationary signals such as vibration in which spectral contents vary with respect to time. In AFHCW, stationary WT is used to ensure linear processing on the vibration data prior to feature extraction, and it helps in mitigating the impact of poor SNR. K-nearest neighbor classifier is used to categorize the type of the fault. Simulation studies show that the proposed scheme outperforms the existing techniques in terms of classification accuracy under poor SNR

Envelope-Wavelet Packet Transform for Machine Condition Monitoring

Wavelet transform has been extensively used in machine fault diagnosis and prognosis owing to its strength to deal with non-stationary signals. The existing Wavelet transform based schemes for fault diagnosis employ wavelet decomposition of the entire vibration frequency which not only involve huge computational overhead in extracting the features but also increases the dimensionality of the feature vector. This increase in the dimensionality has the tendency to 'over-fit' the training data and could mislead the fault diagnostic model. In this paper a novel technique, envelope wavelet packet transform (EWPT) is proposed in which features are extracted based on wavelet packet transform of the filtered envelope signal rather than the overall vibration signal. It not only reduces the computational overhead in terms of reduced number of wavelet decomposition levels and features but also improves the fault detection accuracy. Analytical expressions are provided for the optimal frequency resolution and decomposition level selection in EWPT. Experimental results with both actual and simulated machine fault data demonstrate significant gain in fault detection ability by EWPT at reduced complexity compared to existing techniques

Multiple-points fault signature's dynamics modeling for bearing defect frequencies

Occurrence of a multiple-points fault in machine operations could result in exhibiting complex fault signatures, which could result in lowering fault diagnosis accuracy. In this study, a multiple-points defect model (MPDM) is proposed which can simulate fault signature-s dynamics for n-points bearing faults. Furthermore, this study identifies that in case of multiple-points fault in the rotary machine, the location of the dominant component of defect frequency shifts depending upon the relative location of the fault points which could mislead the fault diagnostic model to inaccurate detections. Analytical and experimental results are presented to characterize and validate the variation in the dominant component of defect frequency. Based on envelop detection analysis, a modification is recommended in the existing fault diagnostic models to consider the multiples of defect frequency rather than only considering the frequency spectrum at the defect frequency in order to incorporate the impact of multiple points fault

Optimally parameterized wavelet packet transform for machine residual life prediction

One of the prevalent issues in condition based maintenance (CBM) is to predict the residual life of the equipment. This paper propos-es a novel framework to predict the remnant life of the equipment, called Residual life prediction based on optimally parameterized Wavelet transform and Mute-step Support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications and decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature-signal energy contents. The prediction model is based on multi-step support vector regression (MSVR) and prediction accuracy is improved in comparison with the techniques based on support vector regression (SVR). Performance of RWMS is evaluated in terms of Root Means Square Error (RMSE), studies show that proposed algorithm predicts the residual life of the equipment accurately