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"Iqbal Muhammad"
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A state-of-the-art review on wastewater treatment techniques: the effectiveness of adsorption method
The world’s water supplies have been contaminated due to large effluents containing toxic pollutants such as dyes, heavy metals, surfactants, personal care products, pesticides, and pharmaceuticals from agricultural, industrial, and municipal resources into water streams. Water contamination and its treatment have emerged out as an escalating challenge globally. Extraordinary efforts have been made to overcome the challenges of wastewater treatment in recent years. Various techniques such as chemical methods like Fenton oxidation and electrochemical oxidation, physical procedures like adsorption and membrane filtration, and several biological techniques have been recognized for the treatment of wastewater. This review communicates insights into recent research developments in different treatment techniques and their applications to eradicate various water contaminants. Research gaps have also been identified regarding multiple strategies for understanding key aspects that are important to pilot-scale or large-scale systems. Based on this review, it can be determined that adsorption is a simple, sustainable, cost-effective, and environmental-friendly technique for wastewater treatment, among all other existing technologies. However, there is a need for further research and development, optimization, and practical implementation of the integrated process for a wide range of applications.
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Journal Article
A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals
In condition based maintenance, different signal processing techniques are used to sense the faults through the vibration and acoustic emission signals, received from the machinery. These signal processing approaches mostly utilise time, frequency, and time-frequency domain analysis. The features obtained are later integrated with the different machine learning techniques to classify the faults into different categories. In this work, different statistical features of vibration signals in time and frequency domains are studied for the detection and localisation of faults in the roller bearings. These are later classified into healthy, outer race fault, inner race fault, and ball fault classes. The statistical features including skewness, kurtosis, average and root mean square values of time domain vibration signals are considered. These features are extracted from the second derivative of the time domain vibration signals and power spectral density of vibration signals. The vibration signal is also converted to the frequency domain and the same features are extracted. All three feature sets are concatenated, creating the time, frequency and spectral power domain feature vectors. These feature vectors are finally fed into the K- nearest neighbour, support vector machine and kernel linear discriminant analysis for the detection and classification of bearing faults. With the proposed method, the reduction percentage of more than 95% percent is achieved, which not only reduces the computational burden but also the classification time. Simulation results show that the signals are classified to achieve an average accuracy of 99.13% using KLDA and 96.64% using KNN classifiers. The results are also compared with the empirical mode decomposition (EMD) features and Fourier transform features without extracting any statistical information, which are two of the most widely used approaches in the literature. To gain a certain level of confidence in the classification results, a detailed statistical analysis is also provided.
Journal Article
Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter
The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.
Journal Article
A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction
In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.
Journal Article
Toxicity of biogenic zinc oxide nanoparticles to soil organic matter cycling and their interaction with rice-straw derived biochar
Given the rapidly increasing use of metal oxide nanoparticles in agriculture as well as their inadvertent addition through sewage sludge application to soils, it is imperative to assess their possible toxic effects on soil functions that are vital for healthy crop production. In this regard, we designed a lab study to investigate the potential toxicity of one of the most produced nanoparticles, i.e. zinc oxide nanoparticles (nZnO), in a calcareous soil. Microcosms of 80 g of dry-equivalent fresh soils were incubated in mason jars for 64 days, after adding 100 or 1000 mg of biogenically produced nZnO kg
−1
soil. Moreover, we also added rice-straw derived biochar at 1 or 5% (w: w basis) hypothesizing that the biochar would alleviate nZnO-induced toxicity given that it has been shown to adsorb and detoxify heavy metals in soils. We found that the nZnO decreased microbial biomass carbon by 27.0 to 33.5% in 100 mg nZnO kg
−1
soil and by 39.0 to 43.3% in 1000 mg nZnO kg
−1
soil treatments across biochar treatments in the short term i.e. 24 days after incubation. However, this decrease disappeared after 64 days of incubation and the microbial biomass in nZnO amended soils were similar to that in control soils. This shows that the toxicity of nZnO in the studied soil was ephemeral and transient which was overcome by the soil itself in a couple of months. This is also supported by the fact that the nZnO induced higher cumulative C mineralization (i.e. soil respiration) at both rates of addition. The treatment 100 mg nZnO kg
−1
soil induced 166 to 207%, while 1000 mg nZnO kg
−1
soil induced 136 to 171% higher cumulative C mineralization across biochar treatments by the end of the experiment. However, contrary to our hypothesis increasing the nZnO addition from 100 to 1000 mg nZnO kg
−1
soil did not cause additional decrease in microbial biomass nor induced higher C mineralization. Moreover, the biochar did not alleviate even the ephemeral toxicity that was observed after 24d of incubation. Based on overall results, we conclude that the studied soil can function without impairment even at 1000 mg kg
−1
concentration of nZnO in it.
Journal Article