AI and Machine Learning for Energy Optimization Birudala Venkatesh Reddy, K. Anju Aravind, Mohammad Shabbir Alam, Shantanu Datta, B. Karunamoorthy, et al. Energy Efficient Algorithms and Green Data Centers for Sustainable Computing, 2025 ML and AI can transform energy optimisation in numerous industries. This chapter discusses how AI and ML have revolutionized price, energy efficiency, and environmental sustainability. AI-powered systems can optimise the grid's renewable energy integration, manage energy resources in real time, and forecast consumption trends using optimization, and predictive analytics. Smart grids, renewable energy forecasting, industrial energy management, smart buildings, and EV charging infrastructure are major applications. This chapter also discusses these fields ML methodologies. Supervised learning estimates energy consumption, RL regulates energy adaptively, and deep learning analyzes complicated data. This chapter presents effective AI-driven energy solution case studies. Edge AI, decentralized energy management, and intelligent storage technologies are also covered. It address data security, ethical concerns, and regulatory compliance caused by AI's growing use in energy optimisation to achieve a sustainable and egalitarian future.
DEVELOP AND TEST MAGNETIC NANO CATALYSTS REUSE FOR OZONATION IN WASTEWATER TREATMENT Oxidation Communications, 2024
Pipe network blockage detection by frequency response and genetic algorithm technique Shantanu Datta, Nitish Kumar Gautam, Shibayan Sarkar Journal of Water Supply Research and Technology Aqua, 2018 This paper deals with detection of pipeline blockage location. For this, four branched pipe network models, viz. Model 1, Model 2, Model 3 and Model 4, are considered. The first two models are considered for analytical analysis and the second two models are considered for experimental analysis. For Model 1 and Model 2, the transfer matrix method is used to develop pressure frequency diagrams. Number of peaks exceeding the threshold value is considered as a variable to find the blockage location. In Model 3 and Model 4, blockage is created by partial valve closure and periodic oscillation is created by the end valve, manually. Time domain transient pressure data are analysed by the discrete Fourier transformation technique. Afterwards, an attempt is made to establish a relationship towards detection of blockage location using a genetic algorithm. This method is applied for 10%, 20%, 30% and 40% blockage of mean pipe diameter. It is found that location of blockage is independent of number of oscillations. Pressure and velocity of fluid inside the pipeline has negligible influence towards the calculation of blockage detection. New relationships and sensitivity analysis show that blockage location is directly proportional to length of maximum straight pipeline, and square root of pressure peaks. doi: 10.2166/aqua.2018.046 s://iwaponline.com/aqua/article-pdf/67/6/543/493681/jws0670543.pdf Shantanu Datta Nitish Kumar Gautam Shibayan Sarkar (corresponding author) Department of Mechanical Engineering, Indian Institute of Technology (ISM), Dhanbad, 826004, India E-mail: shibayan.sarkar@gmail.com
