Environmental Industry Letters

A Fuzzy AHP-TOPSIS and Mamdani Fuzzy Inference Decision-Making System for the Environmental Impact Assessment of Hydropower Projects

Farzan Khorasani — Sat, 25 Jul 2026 00:00:00 +0200

Abstract
Environmental impact assessment (EIA) of large hydropower schemes involves criteria that are simultaneously heterogeneous in scale, partly qualitative, and elicited under expert uncertainty, conditions for which fuzzy multi-criteria decision-making (MCDM) is well suited. This study develops an integrated fuzzy logic decision-making system combining fuzzy Analytic Hierarchy Process (fuzzy AHP), fuzzy Technique for Order Preference by Similarity to Ideal Solution (fuzzy TOPSIS), and a Mamdani fuzzy inference system (FIS), and applies it to a case study of six operating large hydropower schemes (Three Gorges, Itaipu, Belo Monte, Nam Theun 2, the Grand Ethiopian Renaissance Dam, and Xayaburi) evaluated against eight environmental, social and technical criteria. Criteria weights were derived with Buckley's fuzzy geometric-mean method (cross-checked against Chang's extent analysis, consistency ratio CR = 0.012), and used to rank alternatives by fuzzy-TOPSIS closeness coefficient. An independently parameterised Mamdani FIS provided a rule-based severity classification as a cross-check. The Xayaburi scheme obtained the most favourable composite ranking, driven by its comparatively small inundation footprint and low resettlement burden, despite documented severe biodiversity impacts; Monte Carlo weight-perturbation analysis (2,000 runs) confirmed this ranking is robust to plausible weight uncertainty (mean Spearman's rho = 0.85), while one-at-a-time ±20% perturbations left the top rank unchanged in all 16 scenarios. The Mamdani cross-check, however, classified five of the six schemes as “High” overall impact severity, underscoring that a favourable composite rank should not be read as low absolute impact. The framework, released with fully reproducible open-source Python code, offers environmental regulators and financing institutions a transparent, auditable decision-support tool for comparative hydropower EIA.

Safe and Sustainable by Design Assessment of Organic Rankine Cycle Working Fluids for Binary-Cycle Geothermal Power Plants: A Combined Entropy-TOPSIS Model with Global Sensitivity Analysis

Reza Haririyan Javan — Thu, 10 Sep 2026 00:00:00 +0200

Working-fluid selection is decisive for the safety, environmental footprint, and thermodynamic performance of the organic Rankine cycle (ORC) units that convert medium-enthalpy geothermal brine into electricity, yet fluid-selection studies rarely embed all three dimensions inside a single, auditable decision model. This study operationalises the European Commission Joint Research Centre (JRC) Safe and Sustainable by Design (SSbD) framework as a quantitative multi-criteria case study for a representative 150 degC binary-cycle geothermal plant. Six candidate fluids (R245fa, R1233zd(E), isobutane, isopentane, propane and ammonia) are scored on nine indicators nested within the three SSbD pillars — Safety, Environmental Sustainability, and Functionality — the last populated by a first-principles, Carnot-referenced thermodynamic sub-model rather than by literature look-up alone. Indicator weights are derived by combining objective Shannon-entropy weighting with a neutral, regulation-consistent subjective baseline, and fluids are ranked with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Under the neutral baseline, R245fa attains the highest closeness coefficient (CC = 0.810), ahead of the ultra-low-global-warming-potential (GWP) alternative R1233zd(E) (CC = 0.763). A four-tier sensitivity analysis — one-factor-at-a-time tornado analysis, a manually implemented Sobol–Saltelli variance decomposition, an 8,000-fold Dirichlet Monte-Carlo rank-stability test, and one-way thermodynamic/life-cycle-assessment parameter sweeps — shows that this ranking is not robust: the Environmental Sustainability dimension alone explains 56–62% of the output variance, and a weight shift of roughly 30% toward that pillar is sufficient to make R1233zd(E) the preferred fluid, which occurs in 32% of Dirichlet-sampled weight vectors. The results demonstrate that SSbD assessments of energy-conversion fluids are only decision-useful when reported jointly with an explicit, quantitative sensitivity envelope, and they provide a transparent, fully reproducible template for extending the SSbD framework from chemicals and materials into renewable-energy hardware design.

Physically Informed and Machine-Learning Power Prediction for a Wave Energy Converter under Bayesian Uncertainty: A Comparative Carbon-Footprint Assessment against Fossil-Fuel Generation

Hamed Darabi Kerchi — Tue, 22 Sep 2026 00:00:00 +0200

Abstract
Ocean wave energy is a large, largely untapped renewable resource, but quantitative, uncertainty-aware comparisons of its climate benefit against fossil-fuel generation remain scarce. This study develops a combined physical and machine-learning (ML) modelling framework, wrapped in a Bayesian uncertainty-quantification (UQ) layer, to estimate the annual energy production (AEP) and life-cycle carbon-footprint mitigation of a reference two-body point-absorber wave energy converter (WEC), using the PacWave South (Oregon, USA) test site as a case study. A deterministic wave-to-wire physical model (linear wave power flux, a resonance-shaped capture-width response, and rated-power saturation) is cross-validated against a data-driven Gaussian Process Regression (GPR) surrogate trained on a synthetic power-performance dataset. The physical model's hydrodynamic parameters are calibrated with a four-chain random-walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) sampler (Gelman-Rubin R-hat <= 1.01 for all parameters), while the GPR supplies an independent, non-parametric posterior-predictive uncertainty. The two approaches converge on a closely comparable AEP (physical-Bayesian: 500.8 MWh yr⁻¹; ML-Bayesian: 453.5 MWh yr⁻¹), which are propagated, together with literature-based life-cycle carbon-intensity distributions for the WEC and for coal- and gas-fired generation, through a 100,000-draw Monte Carlo simulation. The device is estimated to avoid 376 t CO₂-eq yr⁻¹ (95% credible interval, CrI: 287-479) relative to an equivalent coal-fired generator (93.3% reduction) and 220 t CO₂-eq yr⁻¹ relative to natural gas (89.1% reduction), equivalent to roughly 9.4 kt CO₂ over a 25-year design life relative to coal. These results demonstrate that combining physically grounded and data-driven models within a rigorous Bayesian framework yields credible, auditable uncertainty bounds on the decarbonisation potential of wave energy, supporting evidence-based marine-energy policy and investment appraisal.