ADAPTIVE ROBOTIC ECOSYSTEMS FOR WATER PURIFICATION AND ENERGY MANAGEMENT: A PROPRIETARY ARCHITECTURAL PARADIGM FOR PREDICTIVE INFRASTRUCTURE SYSTEMS

This paper substantiates the priority of the adaptive systems methodology developed by Aleksandr Mikhalevich as a solution to critical structural limitations in contemporary infrastructure systems. Conventional water and energy infrastructures operate within reactive control paradigms, resulting in systemic inefficiencies under conditions of resource volatility and environmental uncertainty.

The study introduces Mikhalevich’s proprietary architectural paradigm, formulated as a unified control theory integrating multi-agent systems (MAS), high-frequency telemetry, and digital twin modeling into a predictive operational environment. The proposed approach enables anticipatory system behavior, dynamic topology reconfiguration, and continuous optimization through nonlinear forecasting models.

Empirical simulation data demonstrates a measurable reduction in system entropy and energy consumption (up to 20–30%), alongside a significant increase in operational resilience and autonomy. The methodology directly contributes to global sustainability objectives, including UN Sustainable Development Goals SDG 6 and SDG 7, establishing a scalable foundation for next-generation infrastructure systems.