Thermodynamic, economic and environmental assessment of solid oxide fuel cell-based hybrid cogeneration system for power generation and water heating Yunis Khan, P. M. G. Bashir Asdaque, Manisha, Pawan Kumar Singh, K. K. Sivakumar, Rohit Kumar Singh Gautam Journal of Thermal Analysis and Calorimetry, 2025 Efficient recovery of high-grade waste heat from solid oxide fuel cells (SOFCs) is crucial for enhancing energy utilization and environmental performance. This study addresses this challenge by proposing an advanced SOFC-based cogeneration system that integrates a gas turbine (GT), a recuperated regenerative organic Rankine cycle (RRORC), and a water heater for simultaneous power and hot water production. A comprehensive thermodynamic, economic, and environmental assessment was conducted using a detailed computational model to evaluate system performance and feasibility. The results indicate that incorporating the RRORC with the SOFC-GT system enhances exergy efficiency by 9.56%, while the inclusion of a water heater further raises the improvement to 11.14%. The overall energy efficiency increased by 30.76% with only an 11.16% rise in total cost, and CO ₂ emissions were reduced by 23.49% compared to the conventional SOFC-GT system. These findings demonstrate that the proposed configuration effectively harnesses SOFC waste heat for improved energy recovery and sustainability. The novelty of this work lies in the integration of a RRORC and a water heating subsystem with the SOFC-GT cycle, extending the efficiency and environmental advantages beyond previously reported hybrid configurations.
Implementing Lightweight Post-Quantum Signatures for Enhanced Security in Green IoE H. Manoj T. Gadiyar, Sivakumar KK, Nainee Patel, Shadaksharaiah C, Amit Lathigara, Vanguri Smitha 4th IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics Icdcece 2025, 2025 Joining people, processes, data, and gadgets in a sustainable way is called the environmentally friendly Internet of Everything (Green IoE). Traditional encryption techniques, however, face serious security threats from the advent of quantum computing. Discrete logarithmic and factorisation issues make current cryptography systems like RSA and ECC susceptible to quantum attacks. In order to improve Green IoE security without sacrificing energy efficiency, this article suggests using lightweight post-quantum signature methods. The promising members are the ones derived from the lattice, hash, or multivariate quadratic theory when it comes to the choice between security and performance. This research identifies how it is possible to integrate quantum-safe security for Green IoE applications without eradicating the low power requirements inherent in these systems. It is by combining these little post-quantum signatures into Green IoE that it becomes possible to come up with a safe system that meets the set environmental standards. Realizing the need to construct a dependable safety system with Green IoE in the post-quantum period, the research evaluates the strength and speed of the proposed cryptography algorithms.
Exergy-energy, economic and environmental evaluations of a solid oxide fuel cell based trigeneration system Yunis Khan, Pawan Kumar Singh, Aftab Anjum, K.K. Sivakumar, Shikha Gupta, Subhash Mishra International Journal of Exergy, 2025 This work proposes a trigeneration system based on a solid oxide fuel cell (SOFC) for power generation, steam production, and cooling effects. The results show that energy, exergy efficiency, and total cost of the trigeneration plant were enhanced by 36.51%, 4.14%, and 1.76%, respectively, as compared to the conventional SOFC-based gas turbine plant. However, the CO2 emission per MWh of output energy was reduced by 26.73%. Additionally, cooling effects of 100 kW were obtained at 5°C for general-purpose applications, and heating effects of 101.4 kW were obtained by generating saturated steam through a heat recovery steam generator.
Creating an Energy-Aware Cloud Platform to Optimize Carbon Footprint of Large-Scale Computational Environmental Models Srigouri Kosuri, Ammar Younas, Dipanwita Chattopadhyay, K.K. Sivakumar, S. Suma Christal Mary, I Infant Raj 2025 IEEE 4th International Conference for Advancement in Technology Iconat 2025, 2025 With the growing use of computational environmental models, energy utilization and carbon footprints of cloud data centers are a serious sustainability issue. Current study, such as carbon-conscious frameworks CFWS and GEECO, has tried to optimize energy consumption, yet they usually do not have a proper temporal prediction and adaptive scheduling strategy under real-world energy-carbon fluctuations. To overcome this limitation, this study suggests a new energy-aware cloud platform that combines Long ShortTerm Memory (LSTM) networks for accurate prediction of energy demand and carbon intensity, and Deep Reinforcement Learning (DRL) for smart, carbon-aware task scheduling. The suggested model was implemented using Python and TensorFlow and trained and tested on the Low-Carbon Industrial Park Energy Dataset from Kaggle. The findings show a 12.6 % reduction in carbon emissions and a 9.3 % improvement in scheduling efficiency compared to current approaches. The LSTM model obtained an MAE of 2.74 and RMSE of 3.28 for carbon prediction, while DRL minimized task placement overhead. The integrated deep learning-based technique presents a new and scalable solution for green cloud computing. The framework offers new opportunities for real-time, lowcarbon resource management in cloud platforms and is extremely desirable to reviewers interested in sustainable AI and energy optimization.
Applying Deep Belief Network for Predicting the Risk of Diabetes Complications S. Prabu, Sivakumar K K, Anurag Kumar Tiwari, S. Selvakanmani, Rajesh Tiwari, Amit Lathigara 4th IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics Icdcece 2025, 2025 The acquisition of knowledge and significant outcomes from complex, multi-dimensional, and diversified medical information continues to be a significant obstacle to providing better healthcare. Every day, new types of clinical information emerge as a result of the overwhelming number of people who must undergo medical tests. Among these are text, images, and data from sensors and electronic health records (EHRs). There is a lack of organization, diversity, good commentary, and predictability in these. In order to sift through and draw conclusions regarding these medical datasets, effective data analytics methods are required. A Deep Belief Network (DBN) model for Type 2 Diabetes Mellitus (T2D) issue prediction is suggested in the subsequent phase of the study. The model sheds light on the prevalence of diabetes and its potential consequences. We compare the accuracy, precision, and recall of DBN-based analytical data models to those of Support Vector Machines (SVMs), Random Forests (RFs), K Nearest Neighbours (KNNs), Artificial Neural Network (ANNs), and Convolutional Neural Networks (CNNs). Throughout training, the suggested approach accurately establishes the widespread nature of risk with a 97.39% accuracy rate, but during examination.
Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyper Parameter-Optimized Neural Networks International Journal of Intelligent Systems and Applications in Engineering, 2024
Photovoltaic characteristics of chemical bath deposition grown EU-Cds/EU-Pbs fabricated solar cells – enhancement studies International Journal of Scientific and Technology Research, 2020
Enhancement of photovoltaic properties of CBD grown Pr: Cds/Pbs solar cells International Journal of Scientific and Technology Research, 2020
Low cost fabrication of EU: Cds/Pbs heterostructures International Journal of Scientific and Technology Research, 2020
Distribution of heavy metals profile in water and soil system at amaravathi river basin of Karur, Tamil Nadu Indian Journal of Environmental Protection, 2012
Studies on the photodegradation of commercial dye colour by nanoparticles Indian Journal of Environmental Protection, 2012
Physico-chemical profile studies of soil along the banks of amaravathi river bank in karur areas of Tamil Nadu Indian Journal of Environmental Protection, 2012
Heavy metal removal from aqueous solution by tamarind and neem leaves Asian Journal of Chemistry, 2012
Prosopis Juliflora Carbon and Commercial Activated Carbon in the removal of COD from aqueous solution Journal of Chemical and Pharmaceutical Research, 2011
Glycine sodium nitrate R. V. Krishnakumar, M. Subha Nandhini, S. Natarajan, K. Sivakumar, Babu Varghese Acta Crystallographica Section C Crystal Structure Communications, 2001
Chloramine-B sesquihydrate Elizabeth George, Subramania Vivekanandan, Kandasamy Sivakumar Acta Crystallographica Section C Crystal Structure Communications, 2000
Two substituted [1,2,4]triazole derivatives R. Velavan, K. Sivakumar, H.-K. Fun, U. S. Pathak, K. S. Jain, S. Singh Acta Crystallographica Section C Crystal Structure Communications, 1997
(Pentafluorophenyl)diphenylphosphine O. bin Shawkataly, M. L. Chong, H. K. Fun, K. Sivakumar Acta Crystallographica Section C Crystal Structure Communications, 1996
8,9,10,11-tetrahydrobenz[c]acridine J. K. Ray, M. K. Haldar, G. D. Nigam, K. Sivakumar, H. K. Fun Acta Crystallographica Section C Crystal Structure Communications, 1996
1-(2,4-dinitrophenyl)-3,5-dimethylpyrazole S. Mani Naidu, M. Krishnaiah, K. Sivakumar, R. P. Sharma Acta Crystallographica Section C Crystal Structure Communications, 1996
Ferrocenium tetrachloroantimonate B. M. Yamin, H. K. Fun, K. Sivakumar, B. C. Yip, O. B. Shawkataly Acta Crystallographica Section C Crystal Structure Communications, 1996
1-cyclohexyl-3-phenylthiourea A. Ramnathan, K. Sivakumar, K. Subramanian, N. Srinivasan, K. Ramadas, H. K. Fun Acta Crystallographica Section C Crystal Structure Communications, 1996
1,3-bis(2-chlorophenyl)thiourea A. Ramnathan, K. Sivakumar, K. Subramanian, N. Janarthanan, K. Ramadas, H. K. Fun Acta Crystallographica Section C Crystal Structure Communications, 1996
1,3-dibenzylthiourea and 1,3-dicyclohexylthiourea A. Ramnathan, K. Sivakumar, K. Subramanian, D. Meerarani, K. Ramadas, H. K. Fun Acta Crystallographica Section C Crystal Structure Communications, 1996
