Department of Electrical and Electronics Engineering
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Item Development of Cyber-Physical Systems for Water Quality Monitoring in Smart Water Grid(Springer, 2022-05) Gupta, Raj Kumar; Gupta, Karunesh KumarThere are many challenges while developing a smart city, such as air/water quality monitoring, water resource management, power grid implementation, and transport management. Water quality monitoring is one of them in which many researchers and scientists showed interest. The current water distribution systems always face leakage, failure, and maintenance delays due to the unavailability of real-time monitoring in distribution systems, which results in a high amount of water wastage. This can be solved by implementing a smart water grid. This paper proposes a solution for water quality monitoring for distribution systems in a real-time environment based on low-cost commercial off-the-shelf modules. Various water quality parameters were monitored from the developed setup. The proposed architecture can log, analyze data, make decisions, and remotely represent the data. The data obtained from various sensing nodes were uploaded to the cloud, a service provided by Amazon Web Services (AWSs). Experimental results show that the proposed low-cost sensing network can be an ideal early warning system in smart cities.Item Detection of cadmium ion in aqueous medium by simultaneous measurement of piezoelectric and electrochemical responses(Elsevier, 2018-09) Gupta, Karunesh Kumar; Gupta, Raj Kumar; Manjuladevi, V.Cadmium is one of the important heavy metals which poses health hazards due to its consumption through potable water. Cadmium is known to form complexes with amine group and also it has good affinity towards carbon nanotubes. The octadecylamine functionalized single-walled carbon nanotubes (ODACNTs) can be employed for sensing cadmium ion in aqueous medium. A thin film of ODACNTs offers not only strong adsorption properties towards cadmium ion but also provides an enormous gain in surface to volume ratio, and good mechanical and chemical stability. Therefore, a sensing layer of ODACNTs was formed on the gold deposited quartz wafer and the sensing towards cadmium ion in the aqueous medium was performed. An experimental setup was designed to record the electrochemical and piezo-responses simultaneously. The piezo and electrochemical responses were found to be linear in the given concentration range. Interestingly, the piezoresponse modulates systematically and repeatedly from a maximum to minimum value due to voltage sweep during cyclic voltammetry indicating the interfacial phenomenon of adsorption and desorption.Item Raspberry Pi-based smart sensing platform for drinking-water quality monitoring system: a Python framework approach(DWES, 2019) Gupta, Raj Kumar; Gupta, Karunesh KumarThis paper proposes the development of a Raspberry Pi-based hardware platform for drinking-water quality monitoring. The selection of water quality parameters was made based on guidelines of the Central Pollution and Control Board (CPCB), New Delhi, India. A graphical user interface (GUI) was developed for providing an interactive human machine interface to the end user for ease of operation. The Python programming language was used for GUI development, data acquisition, and data analysis. Fuzzy computing techniques were employed for decision-making to categorize the water quality in different classes like “bad”, “poor”, “satisfactory”, “good”, and “excellent”. The system has been tested for various water samples from eight different locations, and the water quality was observed as being good, satisfactory, and poor for the measured water samples. Finally, the obtained results were compared with the benchmark for authentication.Item Heavy Metal Ion Sensing using Ultrathin Langmuir-Schaefer Film of Tetraphenylporphyrin Molecule(IEEE, 2020) Gupta, Raj Kumar; Gupta, Karunesh Kumar; Manjuladevi, V.; Kumar, DalipThe exciting properties of porphyrin molecules can be employed for several applications like sensing, catalysis, photovoltaics and energy related fields. The assemblies of the molecules at interfaces can give rise to some unique physicochemical properties which can enhance the device performance. In this article, we report our studies on heavy metal ion sensing in aqueous medium using ultrathin film of tetraphenylporphyrin (TPP) molecules. The TPP molecules were synthesized and used for forming Langmuir monolayer at the air-water interface. The monolayer was transferred onto solid substrates by Langmuir-Schaefer (LS) method at different target surface pressure of deposition (πT). The morphological analysis of the films indicated supramolecular assembly of the molecules in the LS film deposited at πT = 30 mN/m. The LS films of TPP molecules were employed for sensing heavy metal cations viz. Pb 2+ , Hg 2+ , Co 2+ and Cd 2+ from the aqueous medium by measuring piezoresponse from a quartz crystal microbalance. The sensing performance was found to be the best with LS film deposited at πT = 30 mN/m. The sensitivity-towards Pb 2+ ion is found to be the highest. The sensing of the heavy metal cations using randomly oriented TPP molecules in spin coated thin film was found to be much inferior as compared to that of LS films of the molecule. The enhanced sensing performance by the LS film of TPP molecules may be attributed to the supramolecular assembly of the molecules. We report that the interference of the sensing measurement for the recognition of Cd 2+ and Pb 2+ ions is least and thus these species can be detected selectively with less errors. The cation species recognition features obtained from scanning electron microscope images and Raman spectroscopy were found to be remarkably different and therefore these cationic heavy metal species can be selectively detected using the LS film of TPPItem Drift compensation of commercial water quality sensors using machine learning to extend the calibration lifetime(Springer, 2020) Gupta, Karunesh Kumar; Gupta, Raj KumarThere are specific issues in the multi-sensor systems used for water quality monitoring, which prevents these systems for routine measurement of water samples. An important issue is drift; related to sensor readings, which may refute the calibration of sensors leads to the necessity of frequent recalibration of the sensors that required effort as well as shut down the system. An alternative approach for drift correction is based on the mathematical correction method. The paper proposed a regression calibration method and implemented by the machine learning approach. In this paper, we have used a feed-forward artificial neural network based regression model to extend the calibration lifetime of sensors. The evaluation of the model was performed based on the root mean square error and the root mean square error for cross-validation. The proposed model is also compared with the traditional statistical method and proved to be superior to the traditional one. The experimental results demonstrate the best performance with a negligible error rate. Based on the results of the current study, ANN appears to be more adaptive for data analysis in environmental monitoring applications.Item Towards the Green Analytics: Design and Development of Sustainable Drinking Water Quality Monitoring System for Shekhawati Region in Rajasthan(Springer, 2021-05) Gupta, Raj Kumar; Gupta, Karunesh KumarIn rural areas, there is limited monitoring of drinking water quality. Reliable water quality monitoring stations are expensive and require high costs for maintenance and calibration process. In this paper, the development of a sustainable water quality monitoring system is proposed. The green analytics principles were considered for developing the proposed system to reduce the measurement’s time consumption and labor cost. Five water quality parameters [pH, oxidation reduction potential (ORP), dissolved oxygen (DO), electrical conductivity (EC), and temperature] have been measured using the developed system. The overall drinking water quality is measured by the proposed partial least squares regression (PLSR) model. The developed system’s performance is determined by mean average percentage error (MAPE), root-mean-square error (RMSE), and R2. The traceability of water quality sensors is defined with required uncertainty in water quality parameters. The measured uncertainty is 0.002, 0.892, 0.015, 0.029, and 0.017 for pH, EC, DO, ORP, and temperature, respectively. The relation between estimated and predicted water quality parameters (R2 > 0.93) shows that the developed system can be a suitable replacement for traditional water quality monitoring techniques.Item Real-time water quality monitoring for distribution networks in IoT environment(Inder Science, 2022-04) Gupta, Karunesh Kumar; Gupta, Raj KumarWater quality has always been a significant concern worldwide as a large portion of accessible water is either contaminated or polluted, which can spread serious diseases like dysentery, diarrhoea and cholera. Before consumption, the water quality should be tested to reduce the risk of infection. In real-time applications, the traditional approach for water quality monitoring is not appropriate, as on-site water sample collection is often a cost-intensive and time-consuming process. This paper introduces a real-time assessment of water quality parameters in distribution systems employing Raspberry Pi and Arduino development boards. The parameters were chosen based on the different categories identified by the Central Pollution and Control Board, Government of India. An Arduino development board was used at the sensing node for water quality sensor interfacing, data acquisition, and transmission to the wireless sensor network via Zigbee. Raspberry Pi was used at the server to collect data and upload data on the cloud platform. The 'Thingspeak' cloud platform was used for IoT implementation. The results were validated with the reference instrument.