Environmental Industry Letters

An Integrated Multi-Criteria Decision-Making Framework for Supervisory Control System Design in Smart Municipal Solid Waste Management

Shirin Naderi, Mohammad Safarpour, Reyhane Salehnejad, Amirhossein Ahmadi — Tue, 10 Mar 2026 00:00:00 +0100

The transition of municipal solid waste (MSW) management from periodic, manually scheduled operations to continuously monitored, data-driven systems require a decision layer capable of converting heterogeneous sensor and operational data into reliable control actions. This study proposes and computationally validates an integrated multi-criteria decision-making (MCDM) framework as the cognitive decision layer of a four-layer supervisory control architecture comprising sensing, edge/communication, cognitive decision, and actuation layers. Eight criteria covering economic, environmental, technical, social, and control dimensions, including control-loop responsiveness and data-integration scalability, are used to evaluate five representative MSW management alternatives: sanitary landfilling, waste-to-energy incineration, material recovery facilities, composting/anaerobic digestion, and an Internet of Things (IoT)-enabled smart integrated recovery system. Criterion weights are determined using a hybrid approach combining the subjective Analytic Hierarchy Process (AHP) and objective Shannon entropy method, achieving a strong AHP consistency ratio (CR = 0.0047). The resulting weights are applied to three independent ranking methods: TOPSIS, VIKOR, and PROMETHEE II. Their results are consolidated using Borda rank aggregation and evaluated through Spearman rank-correlation analysis. All three methods consistently identify the IoT-enabled smart integrated recovery system as the best alternative, with TOPSIS, VIKOR, and PROMETHEE II values of CC = 0.7045, Q = 0.000, and net flow = 0.3665, respectively. A one-at-a-time sensitivity analysis with ±30% weight perturbations confirms the stability of the top-ranked alternative. The complete computational pipeline is implemented in open, reproducible Python code. The proposed framework provides control and environmental engineers with a transparent and auditable approach for integrating multi-criteria reasoning into real-time MSW management and identifying criteria requiring careful practical assessment.

A Genetic-Algorithm-Based Multi-Objective Decision Support System for Cascade Reservoir Water Resource Management

Mon, 16 Mar 2026 00:00:00 +0100

Growing volumes of publicly available hydrological, meteorological, and reservoir-operation data create new opportunities for evidence-based water resource management. However, converting these data into actionable operating strategies requires optimisation frameworks capable of addressing the non-linear, non-convex, and conflicting objectives of reservoir operation. This study develops and evaluates a genetic-algorithm-based decision support system (DSS) for a three-reservoir cascade serving irrigation, municipal water supply, hydropower generation, and flood control. A real-coded, constraint-handling Non-dominated Sorting Genetic Algorithm II (NSGA-II) is implemented in Python and coupled with a mass-balance simulation model driven by a ten-year stochastically generated monthly inflow record calibrated to a monsoon-influenced river basin. The optimisation considers a 45-parameter cyclic hedging-rule policy, producing implementable and repeatable management strategies rather than fixed release schedules. Eight independent runs (150 individuals, 300 generations) produce a consolidated Pareto front of 417 non-dominated policies balancing water-supply deficit, hydropower generation, and flood-control performance. A TOPSIS-based compromise-selection layer converts these solutions into operating recommendations under three managerial preference profiles. Compared with a conventional Standard Operating Policy baseline, the balanced GA-optimised policy increases hydropower generation from 6,453.9 to 6,564.2 GWh over ten years and reduces the flood-control exceedance index by 90%, while the water-supply-priority policy increases generation to 6,789.3 GWh. The results demonstrate a deliberate reliability-resilience-vulnerability trade-off, with GA policies rationing water more frequently but moderately, thereby redistributing risk across the cascade. Sensitivity and convergence analyses further confirm the robustness of the optimisation framework.

A Machine Learning-Based Smart SCADA Framework for Predictive Fault Detection and Optimisation-Based Control of an Urban Wastewater Treatment Plant

Besnik Hajdari, Mazen El-Rayes — Thu, 23 Apr 2026 00:00:00 +0200

Supervisory control and data acquisition (SCADA) systems in urban wastewater treatment plants (WWTPs) traditionally act as passive data historians rather than predictive decision-support tools, leaving operators to react to effluent-quality excursions only after laboratory analysis confirms them. This study develops and fully implements, on a real, openly available case study [1–3], an end-to-end machine learning-based smart-SCADA architecture that (i) discovers and classifies plant operational states, (ii) predicts effluent quality ahead of laboratory confirmation using leakage-free soft sensors, and (iii) closes the loop with an event-triggered, optimisation-based controller. The case study is the UCI Water Treatment Plant dataset (527 daily records, 38 operational variables, expert-labelled operational states) from an urban plant in the Barcelona metropolitan area. Unsupervised clustering of the standardised feature space showed only weak agreement with the plant experts' own fault categorisation (adjusted Rand index = 0.062), motivating a supervised approach: a class-balanced support-vector classifier achieved the best fault-detection performance (ROC-AUC = 0.999, balanced accuracy = 0.928), while an unsupervised Isolation Forest achieved comparable discrimination without requiring any labelled fault history (ROC-AUC = 0.999). Soft-sensor regression models predicting effluent BOD5, COD and suspended solids from upstream measurements and a one-day autoregressive term achieved hold-out R² of 0.297–0.301. Coupling these models to a derivative-free, surrogate-model-based controller that searches within the historically trusted operating envelope reduced the total predicted exceedance of illustrative regulatory discharge limits by 93.8% on a 105-day chronological hold-out, while fully resolving 28.6% of individual limit exceedances, using an intervention triggered on only 10.5% of days. The results, reported with explicit uncertainty and limitation analysis rather than optimistic point estimates alone, indicate that a modest, software-only augmentation of legacy SCADA infrastructure can meaningfully improve predictive fault awareness and compliance margins without new hardware instrumentation.