An AI-based methodology was developed for estimating the state-of-health (SOH) of lithium-ion batteries based on secondary operational data and benchmarked with ANN, SVM, RF, and BiLSTM models. The proposed framework was evaluated by using tolerance-based accuracy, Bland–Altman agreement analysis, residual autocorrelation diagnostics, and Cartesian Taylor diagram comparison. The BiLSTM model was the best among the tested models for SOH prediction, with the least prediction error, best agreement with the reference SOH values, and near-white-noise residual behavior. The framework was further extended to Remaining Useful Life (RUL) prediction, where the BiLSTM model showed the most consistent overall performance. We also propose a residual-based anomaly detection as a potential extension of the battery monitoring framework. However, a quantitative evaluation of anomaly detection is out of scope in this study due to the lack of labeled anomaly data in the CALCE dataset. The proposed framework is validated by complementary statistical diagnostics, providing a robust and practical framework for non-intrusive battery health monitoring.

The energy efficiency, consistency of product quality, and optimization of operations for sugar manufacturing processes are some of the significant challenges faced by the sugar industry today due to the nonlinear dynamics of these processes. This paper describes how an innovative Digital Twin framework built using Reinforcement Learning (RL) for stepwise learning and adaptation to process control can be developed. First-principles Mathematical Models (FMMs) for four (4) critical Operational Units (OU) in the sugar production process: Cane Maceration, Juice Clarification, Vacuum Evaporation and Crystallization of Sugar are developed. The individual FMMs were combined to form a Digital Twin that continually synchronizes and updates with the respective physical systems using state estimation and Sensor Fusion (SF). The Proximal Policy Optimization (PPO) RL agent developed for this paper learns to control these operations through Multi-Objective Control Policies (MOCP) that optimize Sugar Yield, Energy Consumption, Product Purity, and Operational Safety. Results from simulations demonstrate a 12.0% decrease (4.2→3.7 MJ/kg) in Specific Energy Consumption, 3.1% increase (87.5→90.2%) in Yield, 3.6% increase (88.3→91.5%) in Pure Water, and 57.1% decrease in Process Instability over that of traditional PID Controllers. The authors built a three-tiered safety enforcement architecture to ensure that all policies learned by the PPO RL agent are implemented correctly. Through an economic analysis of a typical 25,000 T/season Sugar Factory (₹89.0 Million / year), it was found that the payback period for this investment is 1.3 months after installation of the RL Agent. By integrating the Physical Principles of a Sugar Manufacturing Process with Data-Driven Optimizations and learning, this research provides a scalable Digital Twin Framework for the development of next-generation sugar factories.