Publications by authors named "Muhammad Bilal Qureshi"

5 Publications

  • Page 1 of 1

Synthesis and evaluation of Ca-doped ferrihydrite as a novel adsorbent for the efficient removal of fluoride.

Environ Sci Pollut Res Int 2021 Aug 27. Epub 2021 Aug 27.

School of Geographic Sciences, East China Normal University, Shanghai, 200241, China.

Ferric hydrate has been extensively applied for the removal of various types of pollutants from wastewater because of its low cost and high efficiency. However, its wide-scale application has been greatly restricted by high-dose and low-adsorption capacity. Therefore, a novel Ca-doped ferrihydrite adsorbent has been synthesized and used for the enhanced removal of fluoride from wastewater in the presence of other co-existing ions. At 5 mg/L initial fluoride concentration and pH 5, the removal efficiency of fluoride approached to 97.5% and remained stable. Similarly, with the increase of dose from 100 to 300 mg/L, the fluoride removal linearly increased to 98% and remained plateau at neutral pH. Also, the presence of co-existing ions such as NO, SO, Cl, and natural organic matter has not significantly influenced the removal performance of the adsorbent. Fluoride removal best fit the pseudo-second-order reaction kinetics and Langmuir isotherm model. The prepared adsorbent exhibited a maximum adsorption capacity of 53.21 mg/g for fluoride uptake from water. The SEM-EDX confirmed the doping of Ca onto the ferrihydrite where the elemental peaks of Ca and Fe emerged at the energy value of about 3.6 Kev and 7.1 Kev respectively in EDX analysis. In addition, SEM results of Ca-doped ferrihydrite adsorbent illustrated that a large microplates type of products was acquired after synthesis. The regeneration results confirmed that adsorbent could retain their original adsorption capacity after five regeneration cycles. The current study suggested that Ca-doped ferrihydrite has the application potential for the enhanced adsorption of fluoride from the water phase.
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http://dx.doi.org/10.1007/s11356-021-16105-5DOI Listing
August 2021

Investigation of 9000 hours multi-stress aging effects on High-Temperature Vulcanized Silicone Rubber with silica (nano/micro) filler hybrid composite insulator.

PLoS One 2021 28;16(7):e0253372. Epub 2021 Jul 28.

Manchester Metropolitan University, Manchester, United Kingdom.

Degradation in the polymeric insulators is caused due to the environmental stresses. The main aim of this paper is to explore the improved aging characteristics of hybrid samples by adding nano/micro silica in High Temperature Vulcanized Silicone Rubber (HTV-SiR) under long term accelerated aging conditions for 9000 hours. As HTV-SiR is unable to sustain environmental stresses for a long time, thus a long term accelerated aging behavior is an important phenomenon to be considered for field application. The aging characteristics of nano/micro filled HTV-SiR are analyzed by using techniques such as Scanning Electron Microscopy (SEM), Leakage Current (LC), Fourier Transform Infrared Microscopy (FTIR), Hydrophobicity Classification (HC), and breakdown strength for the aging time of 9000 hours. FTIR and leakage currents are measured after every cycle. All the co-filled samples revealed escalated aging characteristics as compared to the neat sample except the SN8 sample (8% nano-silica+20% micro-silica) after 9000 hours of aging. The highest loading of 6% and 8% nano-silica with 20% micro-silica do not contribute to the improved performance when compared with the neat and hybrid samples. However, from the critical experimental analysis, it is deduced that SN2 sample (2% nano-silica+20% micro-silica) is highly resistant to the long term accelerated aging conditions. SN2 has no cracks, lower loss percentages in the important FTIR absorption peaks, higher breakdown strength and superior HC after aging as compared to the unfilled and hybrid samples.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0253372PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318273PMC
July 2021

A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique.

Sensors (Basel) 2020 Aug 27;20(17). Epub 2020 Aug 27.

Department of Operations Technology, Events and Technology Management, Manchester Metropolitan University, Manchester M15 6BH, UK.

Deployment of efficient and cost-effective parking lots is a known bottleneck for the electric vehicles (EVs) sector. A comprehensive solution incorporating the requirements of all key stakeholders is required. Taking up the challenge, we propose a real-time EV smart parking lot model to attain the following objectives: (a) maximize the smart parking lot revenue by accommodating maximum number of EVs and (b) minimize the cost of power consumption by participating in a demand response (DR) program offered by the utility since it is a tool to answer and handle the electric power usage requirements for charging the EV in the smart parking lot. With a view to achieving these objectives, a linear programming-based binary/cyclic (0/1) optimization technique is developed for the EV charge scheduling process. It is difficult to solve the problems of binary optimization in real-time given that the complexity of the problem increases with the increase in number of EV. We deploy a simplified convex relaxation technique integrated with the linear programming solution to overcome this problem. The algorithm achieves: minimum power consumption cost of the EV smart parking lot; efficient utilization of available power; maximization of the number of the EV to be charged; and minimum impact on the EV battery lifecycle. DR participation provide benefits by offering time-based and incentive-based hourly intelligent charging schedules for the EV. A thorough comparison is drawn with existing variable charging rate-based techniques in order to demonstrate the comparative validity of our proposed technique. The simulation results show that even under no DR event, the proposed scheme results in 2.9% decrease in overall power consumption cost for a 500 EV scenario when compared to variable charging rate method. Moreover, in similar conditions, such as no DR event and for 500 EV arrived per day, there is a 2.8% increase in number of EV charged per day, 3.2% improvement in the average state-of-charge (SoC) of the EV, 12.47% reduction in the average time intervals required to achieve final SoC.
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http://dx.doi.org/10.3390/s20174842DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506577PMC
August 2020

Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory.

Sensors (Basel) 2019 Dec 5;19(24). Epub 2019 Dec 5.

Manchester Interdisciplinary Biocentre, School of Computer Science, Manchester Metropolitan University, Manchester M13 9PR, UK.

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.
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http://dx.doi.org/10.3390/s19245357DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6961001PMC
December 2019

Multisensor-Based Target-Tracking Algorithm with Out-of-Sequence-Measurements in Cluttered Environments.

Sensors (Basel) 2018 Nov 20;18(11). Epub 2018 Nov 20.

School of Automation, Hangzhou Dianzi University, Xiasha Higher Education Zone, 2rd Street, Hangzhou 310018, China.

A localization and tracking algorithm for an early-warning tracking system based on the information fusion of Infrared (IR) sensor and Laser Detection and Ranging (LADAR) is proposed. The proposed Kalman filter scheme incorporates Out-of-Sequence Measurements (OOSMs) to address long-range, high-speed incoming targets to be tracked by networked Remote Observation Sites (ROS) in cluttered environments. The Rauch⁻Tung⁻Striebel (RTS) fixed lag smoothing algorithm is employed in the proposed technique to further improve tracking accuracy, which, in turn, is used for target profiling and efficient filter initialization at the targeted platform. This efficient initialization increases the probability of target engagement by increasing the distance at which it can be effectively engaged. The increased target engagement range also reduces risk of any damage from debris of the engaged target. Performance of the proposed target localization algorithm with OOSM and RTS smoothing is evaluated in terms of root mean square error (RMSE) for both position and velocity, which accurately depicts the improved performance of the proposed algorithm in comparison with existing retrodiction-based OOSM filtering algorithms. The effects of assisted target state initialization at the targeted platform are also evaluated in terms of Time to Impact (TTI) and true track retention, which also depict the advantage of the proposed strategy.
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http://dx.doi.org/10.3390/s18114043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263986PMC
November 2018
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