Electrochemical Enhanced Degradation Modeling for Drug-resistant Pathogens in Hospital Wastewater Based on Multi-objective Improved Whale Optimization Algorithm and Multi-objective Collaborative Optimization

The concentration of drug-resistant pathogens (such as carbapenem-resistant Enterobacteriaceae and Pseudomonas aeruginosa) in hospital wastewater fluctuates drastically, and it contains complex components, including antibiotic residues and medical pollutants. These factors lead to problems in traditional disinfection processes when dealing with extreme conditions such as high-load shock, low temperature, and wastewater from infectious disease wards. These problems often result in unstable inactivation efficiency (fluctuation exceeding 30%), high energy consumption, and excessive toxic by-products (such as chlorinated disinfection by-products), which pose serious public health risks. To address this engineering pain point, this study proposes the “Multi-Objective Improved Whale Optimization Algorithm-Electrochemical Degradation Model for Hospital Wastewater Pathogens (MOWOA-EDMP)”. It aims to construct a technical framework for the collaborative optimization of “high pathogen removal rate-low specific energy consumption-low by-product risk”. Guided by the core logic of “mechanism-driven to ensure scientificity and intelligent optimization to enhance practicality”, the model adopts a dual-core architecture. 1. The mechanism modeling layer quantifies the correlation between operating parameters (current density: 5-50 mA/cm 2 , electrode potential: 0.5-2.5 V, electrolysis time: 5-180 min) and pathogen inactivation efficiency based on the Butler-Volmer electrochemical kinetic equation and Faraday’s law. It also integrates water quality characteristics such as wastewater conductivity (0.5-5.0 mS/cm) and antibiotic concentration to build a mechanism-guided statistical response surface. 2. The optimization and solution layer addresses the defects of the traditional Whale Optimization Algorithm (WOA)-easy to fall into a local optimum and slow convergence in the later stage. It introduces an adaptive weight adjustment mechanism (dynamically adjusts the exploration factor based on the water quality correction factor γ(κ)) and a Logistic chaos disturbance strategy (triggered when population diversity Dk 12%. 2) Validation using the National Hospital Environmental Compliance System’s multi-state violation dataset confirms the model’s strong adaptability across diverse conditions. It maintains robust performance in cross-regional scenarios (northern low-temperature/high-humidity, southern high-temperature/high-turbidity) and complex multi-department wastewater mixtures, achieving R 2 > 0.85 with RMSE between 0.3-0.5 log units. Its multi-objective optimization performance is significantly superior to traditional particle swarm optimization and unimproved WOA: the Pareto front hypervolume increases by 14%-17%, and the uniformity of solution set distribution improves by 16%-20%. The convergence generation is shortened by 18%-22%, and the variance of solution sets in multiple independent runs decreases by 20%-30%. The result shows that MOWOA-EDMP breaks through the limitations of traditional single-objective optimization and pure mechanism models through the deep coupling of “mechanism-data-optimization”. It can provide low-energy consumption (specific energy consumption ≤0.8 kWh/m 3 ) and high-removal-rate operating strategies for hospital wastewater disinfection units; meanwhile, MOWOA-EDMP can offer scientific decision support for pathogen risk management and process optimization for medical logistics management departments and environmental regulatory agencies. It also provides a new paradigm for the intelligent and sustainable treatment of complex medical wastewater pollutants.