A Hybrid Fuzzy-Python MCDM Model Used in Sheffler Solar Reflector for the Selection of Sustainable Latent Heat Storage Material

Every nation is in the race for Sustainable Development Goals. The Renewable source of energies is one of the significant contributors in the mentioned area. The continuous energy coming from the Sun is an important renewable source which is harnessed by various techniques. Scheffler Solar Concentrator is one of the unique devices to concentrate solar energy by keeping a fixed focus. This fixed focus heat retention ability can be increased by deploying a Latent Heat Storage material (LHS). There are not many models and research which can guide the researchers towards LHS material selection for Scheffler associated with the SDGs. This paper considers this research gap and proposes a hybrid fuzzy-based research method for picking the most suitable LHS material in Scheffler. The Multi-Criteria Decision Making (MCDM) approach combined with Hesitant Fuzzy Linguistic (HFL) term set is used here. Analytic Hierarchy Process (AHP), TOPSIS and WSM are first used for ranking most appropriate Latent Heat Storage Material out of five LHS materials. The ranking is then validated by using fuzzy AHP in HFL environment using Python programming. The criteria and alternatives are selected by the expert opinion holding good experience in this field. This approach finds novelty in such a way that this method is applied by using fuzzy AHP in HFL environment using PYTHON for the first time involving expert opinion. This method is only employed for the thermos-physical properties of the LHS material and further research can be carried out for the thermos-chemical properties.