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.

A hybrid multicriteria decision-making (MCDM) framework, namely “criteria importance through inter-criteria correlation-combinative distance-based assessment” (CRITIC-CODAS) is introduced to rank automotive brake friction composite materials based on their physical and tribological properties. The ranking analysis was performed on ten brake friction composite material alternatives that contained varying proportions (5% and 10% by weight) of hemp, ramie, pineapple, banana, and Kevlar fibers. The properties of alternatives such as density, porosity, compressibility, friction coefficient, fade-recovery performance, friction fluctuation, cost, and carbon footprint were used as selection criteria. An increase in natural fiber content resulted in a decrease in density, along with an increase in porosity and compressibility. The composite with 5 wt.% Kevlar fiber showed the highest coefficient of friction, while the 5 wt.% ramie fiber-based composites exhibited the lowest levels of fade and friction fluctuations. The wear performance was highest in the composite containing 10 wt.% Kevlar fiber, while the composite with 10 wt.% ramie fiber exhibited the highest recovery. The results indicate that including different fibers in varying amounts can affect the evaluated performance criteria. A hybrid CRITIC-CODAS decision-making technique was used to select the optimal brake friction composite. The findings of this approach revealed that adding 10 wt.% banana fiber to the brake friction composite can give the optimal combination of evaluated properties. A sensitivity analysis was performed on several weight exchange scenarios to see the stability of the ranking results. Using Spearman’s correlation with the ranking outcomes from other MCDM techniques, the suggested decision-making framework was further verified, demonstrating its effectiveness and stability.

The aim of the research work is to study the physical, mechanical, and erosive wear properties of sugarcane bagasse fiber-reinforced epoxy composites. The physical (density, void content) and the mechanical (hardness, tensile strength, impact energy, flexural strength) properties of the composites were found to increase with the content of bagasse fiber. For erosive wear analysis, the experiments were carried out with the help of erosion test machine. To minimize the erosive wear rate, Taguchi technique is executed to explore the influence of five control factors including fiber content, impact velocity, impingement, stand-off distance, and erodent size at three levels. Using Taguchi (L27) orthogonal array, the optimal combination of control factors, which yielded minimum erosive wear rate, was statistically predicted and experimentally verified. The fiber content and impact velocity were the two most contributing control factors for the minimization of erosive wear rate. The important sequence of the parameters is fiber content > impact velocity > impingement angle > erodent size > stand-off distance. The optimal combination of control factors was obtained at 10 wt% of fiber content, 30 m/s of impact velocity, 30° of impingement angle, 85 mm of stand-off distance, and 250 μm of erodent size. Finally, composites worn surfaces were examined with scanning electron microscope to study the possible erosive wear mechanism. POLYM. COMPOS., 40:3777–3786, 2019. © 2019 Society of Plastics Engineers.

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.