Strategic Engineering

Motivation

Rapid urbanization and escalating municipal solid waste (MSW) generation are straining traditional centralized waste systems. Cities like Singapore face land scarcity, transport congestion, and rising energy demands, making centralized landfills and incineration unsustainable. The study was motivated by the need for an adaptable planning tool that supports a transition toward a hybrid waste management architecture—one that decentralizes treatment closer to waste sources while retaining central hubs for efficiency. By embedding environmental, economic, and spatial parameters into one decision framework, the research sought to bridge gaps between engineering optimization and urban planning for long-term sustainability.

Methodologies

• Integrated Decision-Support Methodology (DSM): Three interconnected modules—(1) waste modeling and prediction, (2) system optimization, and (3) multi-dimensional assessment—guide decision-making (Fig. 2, p. 483).
• Waste Modeling and Prediction: Quantifies waste generation across urban subzones based on demographic and land-use variables. Introduces a “geography of waste” framework linking residential, commercial, and industrial activities with waste output (Figs. 3–4, pp. 484–485).
• Optimization (Mixed Integer Linear Programming): Minimizes total lifecycle costs (CAPEX + OPEX – revenues) under constraints of land use, transport distance, and capacity. Considers decentralized (on-site) and centralized (off-site) anaerobic digestion facilities (Fig. 5, p. 486).
• Multi-Criteria Sustainability Assessment: Evaluates economic (NPV, OPEX), environmental (CO₂ emissions, GWP), and social/urban indicators (land fragmentation, transport fleet size).
• Case Study Application: Singapore’s food waste management modeled across 111 waste generation sites. Simulation period = 15 years, accounting for 2% annual waste growth and energy recovery rates.

Insights

• Economic & Environmental Gains:
  The hybrid WtEMS reduced total OPEX by 50% and doubled electricity recovery revenues versus incineration-only systems. GWP improved by 18.7%, and land use efficiency rose (fragmentation ↓ 74.8%)Kuznetsova2019-RSER.
• Operational Efficiency: Hybrid configurations reduced transport fleet requirements by 15.3%, easing congestion and emissions. Optimal configuration = 84% centralized, 16% decentralized facilities (Fig. 10–13, pp. 493–495).
• Strategic Insight: The integrated model provides planners with a flexible optimization tool that balances cost, resilience, and sustainability—offering a blueprint for megacities seeking circular waste–energy transitions.

Training

Relevant lectures and skills:

• Agent-Based Urban Planning (as future direction)
• Urban Infrastructure Optimization
• Mixed Integer Linear Programming (MILP)
• Multi-Criteria Decision Analysis
• Life Cycle Assessment (LCA)
• Economy of Scale and System Design

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