Phase change materials (PCMs) serve as an efficient thermal energy storage mediums across a range of thermal systems, including solar distillations. The selection of an appropriate PCM candidate is a vital integration aspect that affects solar distillation performance. Therefore, the present research introduces a multi-criteria decision-making (MCDM) framework for identifying suitable PCM candidates for application in solar distillation systems. Evaluation indices include eighteen PCM alternatives and seven criteria, which were established from the literature. Criteria importance through intercriteria correlation (CRITIC) method was used to assign objective weights to the criteria, followed by the MAIRCA (multi-attributive ideal-real comparative analysis) approach to rank PCM alternatives. The proposed MCDM model suggests the suitability of paraffin wax followed by soy wax and beeswax PCMs for solar distillation applications, respectively. The comparative analysis, sensitivity analysis, and Kendall rank correlation effectively validated the rankings, demonstrating a robust positive correlation among the results. This study can serve as a preliminary step for experimental and simulation-based investigations aimed at optimizing the selection of PCM in the early stage, thereby reducing the time and costs associated with further analysis.

This study aims to identify the most suitable slag waste-filled polymer composite for automotive braking applications. It employs a hybrid multi-criteria decision-making (MCDM) model that integrates the “method based on the removal effects of criteria” (MEREC) and the “root assessment method” (RAM) method. Eight slag waste-filled polymer composites, evaluated using seven performance-defining criteria, were considered in the MCDM analysis. The performance evaluation criteria included the friction coefficient, wear, friction fluctuations, friction stability, fade-recovery aspects, and rise in disk temperature. The criteria were weighted through the MEREC approach, which identified fade% (0.2890) and wear (0.2829) as the most important attributes in the assessment. The RAM was employed to rank the alternatives and suggested that the composite alternative with 60 wt.% slag waste and 5 wt.% coir fiber proved to be the best composition for automotive braking applications. The results were validated using nine MCDM models and Spearman correlation coefficients, which showed that the ranking of alternatives was consistent and stable even when the normalization steps of MEREC were swapped. Statistical validation demonstrated a strong predictive accuracy (p < 0.05) with a strong correlation coefficient (>0.8) alongside a minimal mean absolute error. Furthermore, sensitivity analysis was performed by examining several weight situations to determine whether the priority weights influenced the ranking of the composite alternatives. The findings from both the correlation and sensitivity analyses confirm the proposed hybrid MEREC-RAM model’s consistency and effectiveness.

The growing global demand for safe and sustainable freshwater production has fueled interest in solar distillation systems. Although solar stills offer environmentally friendly desalination solutions, their low productivity remains a major problem. The incorporation of nanofluids based on oxide nanoparticles has emerged as a promising approach to enhance the thermophysical performance and freshwater yield of solar stills. However, selecting the most suitable nanoparticle is challenging due to conflicting thermophysical, environmental, and economic criteria. To address this decision-making complexity, this study proposes a novel hybrid multi-criteria decision-making (MCDM) framework that integrates entropy and centroidous objective weighting methods with the “Multi-Attributive Ideal-Real Comparative Analysis” (MAIRCA) ranking technique. Twelve widely reported oxide nanoparticles (SiO2, Fe3O4, MgO, ZnO, CuO, Al2O3, GO, TiO2, Co3O4, CeO2, SnO2 and ZrO2) were evaluated against eight criteria: density, thermal conductivity enhancement, specific heat capacity, thermal expansion coefficient, cost, toxicity, stability, and compatibility with the container material. Entropy-centroidous weighting identified thermal conductivity (0.2962) and cost (0.1969) as the most influential criteria, while MAIRCA ranked GO first with a score of 0.0357, followed by Al2O3 (0.0363) and SiO2 (0.0411); ZnO ranked last with a score of 0.0582. Comparative validation across eleven established MCDM methods showed strong agreement, with Spearman correlation coefficients above 0.748, p-values below 0.05, while mean absolute error values not exceeding 1.83. Sensitivity analysis further confirmed that GO remained at the top position in almost all scenarios, except when the importance of thermal conductivity started to decrease compared to its actual weight, resulting in its replacement by Al2O3. The proposed framework provides a systematic and transparent decision-support tool for nanoparticle pre-screening in solar still applications. The entropy-centroidous-MAIRCA framework can be extended to a wide range of problems related to renewable energy and thermal management optimization.