Solar thermal application, Solar power Generation, Solar air Dryer, Solar Stills
11
Scopus Publications
Scopus Publications
A machine learning model for the computation of thermophysical properties of WCO biodiesel mixed with multiwalled carbon nanotubes Syed Sameer Hussain, Syed Abbas Ali, Dilawar Husain, Manish Sharma Science and Technology for Energy Transition Stet, 2025 A Machine Learning (ML) model has been developed to compute the thermophysical properties of Waste Cooking Oil (WCO) biodiesel dispersed with MultiWalled Carbon NanoTubes (MWCNTs). The thermophysical properties when incorporating multiwalled CNTs into biodiesel are critical in improving performance, combustion, and emissions in internal combustion engines because of the high thermal conductivity and mechanical strength. Firstly, MWCNTs are mixed with WCO biodiesel for dosages of 30 ppm, 40 ppm, and 50 ppm. After it, each of the properties, including calorific value, density, viscosity, flash point, and fire point, is evaluated. Further, the MultiLayer Neural Network (MLNN) is a ML model that employs a back-propagation algorithm for mapping the input-output parameters. The parameters that constitute the input are WCO biodiesel blends and MWCNTs ppm. The parameters that are output include the calorific value, density, viscosity, flash point, and fire point. The optimum model’s results indicate that six hidden neurons (2_6H_5) can accurately predict the aforementioned properties under various operating conditions. Then, the Multivariable Regression (MVR) model has been devised to compare with the MLNN model. Subsequently, a comparison between the MLNN and MVR models has been carried out. The properties predicted by MLNN in comparison to the MVR model are seen as close to experimental values with good accuracy, as they depict the good “R” values as 0.98209, 0.97921, 0.99261, 0.9558, and 0.99139 for calorific value, density, viscosity, flash point, and fire point, respectively, and also give the average relative error (RE) for calorific value as 0.803%, density as 0.322%, viscosity as 3.036%, flash point as 5.104%, and fire point as 3.266%. Furthermore, the developed MLNN model is suitable for predicting the calorific value, density, viscosity, flash point, and fire point of WCO biodiesel that has been infused with MWCNTs. This saves time, money, and effort required.
Comparative Analysis of Machine Learning Methods for Electric Vehicle Charging in Smart Grid Shah Faisal, Dilawar Husain, Manish Sharma, Talib Hasan, Syed Abbas Ali, Md Salman Baig Proceedings 2025 IEEE Delcon International Conference on Recent Smart Technologies in Engineering for Sustainable Development, 2025 Electric vehicles (EVs) have grown at a rapid pace, which has resulted in novel opportunities and challenges of building smart cities. With the continued adoption of EVs, the importance of effective charging infrastructure, peak load prediction and grid stability will become paramount. Large scale charging without control may cause fluctuations in voltage, transformer overload, and power losses on the smart grid. In the given paper, a machine learning (ML)-based charging management system, which incorporates traditional, fast, and vehicle-to-grid (V2G) charging technologies, is proposed. A variety of ML models, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM) are considered in the frame of optimization of EV charging schedules, both to save money and reduce the stress on the grid. The LSTM model records better results in enhancing the voltage stability, leveling the load curve, and reducing billing. Experimental results are given through figures and tables that aim to demonstrate the differences in performance and suggested methodologies.
Ecological Footprint Assessment of e-Waste Recycling Shameem Ahmad, Mohd Akram, Dilawar Husain, Akbar Ahmad, Manish Sharma, Ravi Prakash, Mahboob Ahmed Environmental Footprints and Eco Design of Products and Processes, 2023
A Review of Cobalt-Based Metal Hydroxide Electrode for Applications in Supercapacitors Sajid Naeem, Arun V. Patil, Arif V. Shaikh, U. P. Shinde, Dilawar Husain, Md Tanwir Alam, Manish Sharma, Kirti Tewari, Shameem Ahmad, Aqueel Ahmed Shah, Syed Abbas Ali, Akbar Ahmad Advances in Materials Science and Engineering, 2023 Supercapacitors are the cutting-edge, high performing, and emerging energy storage devices in the future of energy storage technology. It delivers high energy and produces higher specific capacitances. This research study provides insights into supercapacitor materials and their potential applications by examining different battery technologies compared with supercapacitors’ advantages and disadvantages. Transition metal hydroxides (cobalt hydroxides) have been studied to develop electrodes for supercapacitors and their use in various fields of energy and conversion devices. Cobalt-based metal oxides and hydroxides provide high-capacitance electrodes for supercapacitors. Metal hydroxides combine high electrical conductivity and excellent stability over time. The metal oxides used to prepare the electrodes for supercapacitors are cobalt-based metal oxides and hydroxides. It is stronger than most of the other oxides and has tremendous electrical conductivity. Cobalt hydroxides are also used in supercapacitors instead of other metal hydroxides, such as aluminum hydroxide, copper hydroxide, and nickel hydroxide. This study gives a complete overview of the preparation, synthesis, analysis, and characterization of cobalt hydroxide thin film electrodes by using the electrochemical deposition technique, parameters measurements, important characteristics, material properties, various applications, and future enhancement in supercapacitors.
