Energy Sustainability: A Crucial Path to Sustainable Development and Sustainability

Abstract:

Sustainable development is an important goal for human and societal activity. Energy sustainability is of great importance to any activities and plans for overall sustainable development, and the achievement of sustainability. This is particularly important given (1) the pervasiveness of energy use throughout almost all aspects of societies and the lives of people, (2) the importance of energy in economic development of societies and the living standards attained by people, and (3) the significant impacts that energy processes and systems have on the environment and the ecosystems within them.

Many factors that need to be considered and appropriately addressed in moving or shifting towards energy sustainability are examined in this talk. These include appropriate selection of energy resources bearing in mind sustainability criteria, facilitation of the use of sustainable energy resources particularly through the use of appropriate energy carriers, enhancement of the efficiency of energy-related processes, and a holistic adoption of environmental stewardship in energy activities. In addition, other key sustainability measures are addressed, such as economics, equity, land use, lifestyle, sociopolitical factors and population, and examples of energy sustainability measures are described. Conclusions are provided related both on options for energy sustainability and on means to achieve sustainable development and overall sustainability.

AI-Driven Machine Learning Framework for Optimizing Emissions & Energy Efficiency in Power Plants

Abstract:

This research presents an artificial-intelligence-driven framework for efficiency improvement and emission optimization in combined-cycle power plants (CCPPs). The proposed methodology integrates thermodynamic modeling, data analytics, and neural-network optimization to address the challenge of reducing nitrogen-oxides (NOx) emissions while maintaining plant efficiency. Four years of high-resolution operational data from a 150 MW gas-turbine station in north-western Türkiye were analyzed using Neural Designer and validated through a Python-based simulation pipeline.

The hybrid framework transitions from deterministic parameter-based formulations to data-driven predictive models through a systematic sequence of more than forty design and validation steps. An online surrogate-simulation engine, trained on historical operating conditions, predicts NOx variations without executing iterative optimization for each scenario. The optimized configuration achieved a gas-turbine-exhaust pressure P_GTE=17.844 mbar and an outlet NOx concentration C_NOx=78.66 mg·m⁻³.

The model employs a quasi-Newton (BFGS) learning algorithm with L₂-regularization to minimize a composite loss index, enabling high predictive stability across unseen operating regimes. Sensitivity analysis identifies turbine inlet temperature and ambient pressure as dominant variables influencing emission intensity. The framework is transferable to hydrogen-enriched fuels, biomass co-firing, and carbon-capture configurations.