Abstract: Fuel efficiency, emission control, operating durability and predictive maintenance requirements continue to grow on today's engine management systems. Traditional real-time optimization approaches are often limited with decision rules being static and one-dimensional objectives, and limited usage of predictive intelligence, resulting in suboptimal performances under dynamic operational conditions. In order to overcome such limitations, a next-generation artificial intelligence (AI)-based real-time optimization framework for intelligent engine management systems is proposed in this paper. The proposed architecture includes multi-sensor data-driven machine learning models like Random Forest, Gradient Boosting, XGBoost, LightGBM and deep neural models with Monte Carlo Dropout for uncertainty quantification. Multi-objective optimization is carried out by NSGA-II, this algorithm represents a trade-off between fuel consumption, emissions, efficiency and component life. A digital twin simulation layer is used for lifecycle-aware predictive insights while an autoencoder-based anomaly detection layer is used to proactively detect if the engine behavior is abnormal. SHAP Explainable AI gives understandable interpretations on the function of features and the reasoning behind the choice. Physics-based feature engineering improves the robustness of models, as well as guarantees the compliance with the constraints of operations. The experimental validation of the synthetic and augmented engine datasets using experimentation evidences the correct remaining life prediction, multi-objective trade-offs, as well as high reliability in anomaly detection. The framework provides practical recommendations on predictive maintenance, performance control and realtime control of operations, which fills the gap between intelligent AI and practical engine control. The holistic framework provides a scalable platform to the next-generation intelligent engines and helps create the sustainable, efficient, and robust industrial and transportation systems.

Abstract: As the world switches to the use of renewable energy sources, the efficient and scalable energy storage solutions that would yield a stable and sustainable grid are in high demand. Traditional energy storage components, e.g., hydrogen storage, lithium-ion battery are experiencing cost, scaling, efficiency disadvantages. This essay suggests a solution of Cryogenic Energy storage (CES), which holds a lot of potential in an attempt to resolve these problems through the use of the cryogenic fluids in very low temperatures to store and release energy effectively. The CES system uses sophisticated thermodynamic model, dynamic phase transition model and optimization computation to achieve high grid implementation performance. Multi-objective optimization and Monte Carlo simulations are used to minimize the cost of storage and maximize energy efficiency, and Model Predictive Control (MPC) and Reinforcement Learning (RL) are used to perform optimal energy dispatch. The results demonstrate that CES is superior to such conventional storage technologies as Li-ion and H2 in the cost, efficiency, and scalability. CES is more NPV and IRR, less sensitive to CAPEX variation, and provides more stable energy dispatch profile, which explains its high suitability in energy storage in large-scale energy storage in the intelligent grids. In addition, MPC control measures/strategies also enhance the system performance by optimum real time dispatch of energy. This paper has noted that CES has a great potential in transforming grid storage solutions, and in favour of a more sustainable and resilient energy infrastructure. The findings lead to more investigation into how to increase the scalability of the systems, the materials used, and the integration of the hybrid storage systems to obtain superior grid performance.