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The main aim of this study was to examine the effects of chicken eggshell (CES) and rice husk ash (RHA) as fillers on the mechanical and the thermal insulation properties of polyurethane coatings.

CES and RHA were ground via ball milling set at different parameters, and the smallest particles size obtained were selected and used as fillers. Fillers of different weight proportions were mixed with other components such as binder, solvent and pigment to form various coating formulations and test samples were made via dip coating. A series of characterisations were conducted to analyse the thermal and mechanical properties of the coating.

The smallest particle size of CES and RHA was obtained after both of them had undergone grinding process at 400 rpm within 180 min. Morphological studies revealed that CES and RHA have irregular shape and high porosity. In crystallographic analysis, CES mainly composed of pure calcite crystal structure and RHA contained amorphous silica. Both of fillers were found thermally stable up to 520 and 710°C for RHA and CES, respectively. In RHA individual system, as the RHA proportion increases, the thermal conductivity of the coating declined. In contrast, in the CES coating system, the thermal conductivity demonstrated an opposite trend. Thermal gravimetric analysis results displayed that by adding hybrid fillers, the residue weight and the thermal stability of the coatings were increased. In addition, the adhesion strength of the coating was increased as the filler weight content increased.

Fillers with nano-range size were expected to be produced in this research for better performance of the coating. However, the obtained fillers were limited to micron size through dry grinding method. Another drawback in this research was the coating technique which is dip coating. The coated substrates do not have uniform coating thickness and this subsequently influenced the performance.

A novel attempt has been made to study the formulation coating system by mixing CES and RHA as fillers which is also known as a hybrid system.

This paper aims to propose a simple, derivative-free novel method named as Nelder–Mead optimization algorithm to estimate the unknown parameters of the photovoltaic (PV) module considering the environmental conditions.

At a particular temperature and irradiation, experimental current-voltage (I-V) and power-voltage (P-V) characteristics are drawn and considered as a reference model. The PV system model with unknown model parameters is considered as the adaptive model whose unknown model parameters are to be adapted so that the simulated characteristics closely matches with the experimental characteristics. A single diode (Rsh) model with five unknown model parameters is considered here for the parameter estimation.

The key advantages of this method are that parameters are estimated considering environmental conditions. Experimental characteristics are considered for parameter estimation which gives accurate results. Parameters are estimated considering both I-V and P-V curves as most of the applications demand extraction of the actual power from the PV module.

The proposed model is compared with other three well-known models available in the literature considering various statistical errors. The results show the superiority of the proposed model with a minimum error for both I-V and P-V characteristics.