Luca Cirillo

In capital-intensive industries, operational knowledge is often trapped in unstructured technical manuals, creating a barrier to efficient and reliable maintenance planning. This work addresses the need for an integrated system that can automate knowledge extraction and generate optimized, resilient, operational plans. A synergistic multi-agent framework is introduced that transforms unstructured documents into a structured knowledge base using a self-validating pipeline. This validated knowledge feeds a scheduling engine that combines multi-objective optimization with discrete-event simulation to generate robust, capacity-aware plans. The framework was validated on a complex maritime case study. The system successfully constructed a high-fidelity knowledge base from unstructured manuals and the scheduling engine produced a viable, capacity-aware operational plan for 118 interventions. The optimized plan respected all daily (6) and weekly (28) task limits, executing 64 tasks on their nominal date, bringing 8 forward, and deferring 46 by an average of only 2.0 days (95th percentile 4.8 days) to smooth the workload and avoid bottlenecks. An interactive user interface with a chatbot and planning calendar provides verifiable “plan-to-page” traceability, demonstrating a novel, end-to-end synthesis of document intelligence, agentic AI, and simulation to unlock strategic value from legacy documentation in high-stakes environments.

Wastewater treatment plants (WWTPs) are critical infrastructures that account for a significant share of global electricity, with aeration alone often responsible for over half of the total demand. Reducing the energy intensity of blower operation is, therefore, essential for sustainable and resilient WWTP management. This study presents a modeling and simulation framework for optimizing parallel blower operation in grit chamber aeration system. The framework integrates a modular structure with a blower model, a distribution network model, and an optimization layer that work together to capture equipment performance, simulate hydraulic interactions, and determine energy-optimal operating strategies under process and safety constraints. Two optimization strategies are compared: a heuristic grid search and a Safe Bayesian Optimization (SBO) method. Both algorithms enforce vendor surge and overheat limits, network pressure constraints, and process requirements. Simulation campaigns under representative demand scenarios show that both approaches achieve feasible operating points, while SBO consistently demonstrates higher energy savings and substantially faster runtime. Overall, the findings highlight the potential of data-driven optimization for achieving efficient and safe blower control, with reduced computation time making progress for real-time supervisory optimization in WWTPs.

This study investigates the integration of advanced optimization algorithms within energy-intensive infrastructures and industrial plants. In fact, the authors focus on the dynamic interplay between computational intelligence and operational efficiency in wastewater treatment plants (WWTPs). In this context, energy optimization is thought of as a hybrid process that emerges at the intersection of engineered systems, environmental dynamics, and operational constraints. Despite the known energy-intensive nature of WWTPs, where pumps and blowers consume over 60% of total power, current methods lack systematic, real-time adaptability under variable conditions. To address this gap, the study proposes a computational framework that combines hydraulic simulation, manufacturer-based performance mapping, and a Memetic Algorithm (MA) capable of real-time optimization. The methodology synthesizes dynamic flow allocation, auto-tuning mutation, and step-by-step improvement search into a cohesive simulation environment, applied to a representative parallel-pump system. The MA’s dual capacity to explore global configurations and refine local adjustments reflects both static and kinetic aspects of optimization: the former grounded in physical system constraints, the latter shaped by fluctuating operational demands. Experimental results across several stochastic scenarios demonstrate consistent power savings (12.13%) over conventional control strategies. By bridging simulation modeling with optimization under uncertainty, this study contributes to sustainable operations management, offering a replicable, data-driven tool for advancing energy efficiency in infrastructure systems.