The meat processing industry produces a huge quantity of by-products, approximately 150 million tonnes per year. The live weight of the animals is distinguished as edible, inedible, and discardable by-products, with the discardable parts equating to 66%, 52%, and 80% of the overall live weight of cattle, lamb, and pigs, respectively. Only a small percentage of those by-products are nowadays exploited for the production of high added value products such as animal feed, glue, fertilizers, etc., whereas the main management method is direct disposal to landfills. As such, the current disposal methodologies of these by-products are problematic, contributing to environmental contamination, soil degradation, air pollution, and possible health problems. Nevertheless, these by-products are rich in collagen, keratin, and minerals, being thus promising sources of high-value materials such as bioenergy, biochemical and other biomaterials that could be exploited in various industrial applications. In this paper, the possible utilization of slaughterhouse by-products for the production of various high added value materials is discussed. In this context, the various processes presented provide solutions to more sustainable management of the slaughterhouse industry, contributing to the reduction of environmental degradation via soil and water pollution, the avoidance of space depletion due to landfills, and the development of a green economy.

Nowadays, evaluating fabric touch can be a great interest of industries to match the quality needs of consumers and parameters for the manufacturing process. Modeling helps to determine how structural parameters of fabric affect the surface of a fabric and also identify the way they influence fabric properties. Moreover, it helps estimate and evaluates without the complexity and time-consuming experimental procedures. In this research paper, the linear regression model was developed that was utilized for the prediction and evaluation of surface roughness of plain-woven fabric. The model was developed based on nine different half-bleached plain-woven fabrics with three weft Yarn counts 42 tex, 29.5 tex & 14.76 tex, and three weft thread densities (18 picks per c, 21ppc & 24 picks per c) and then the surface roughness of plain-woven fabric was tested by using Kawabata (KES-FB4) testing instrument. The findings reveal that the effects of count and density on the roughness of plain-woven fabric were found statistically significant at the confidence interval of 95%. The weft yarn count has a positive correlation with surface roughness values of plain-woven fabrics. On the other hand, pick density has a negative correlation with the surface roughness values of plain-woven fabrics. The correlation between measured surface roughness by KES-FB4 and calculated surface roughness by the model equation show how they are strongly correlated at 95% (R² of 0.97).