The main goal of this project consisted of the conceptualization and development of a product related to the olive tree chemical features to monetize the wastes of the olive fruit exploitation. The most abundant by-product of the olive fruit harvest is the olive leaves, used as the raw material in this project. By following the steps of Chemical Product Design, it was decided to pursue the idea of creating antibacterial cotton bed sheets impregnated with olive leaves extract, mainly composed of oleuropein. The production process includes a pre-treatment of the olive leaves, oleuropein extraction, and supercritical impregnation. The estimated amount of leaves required is 39 g/m2 of cotton, considering a cotton fabric with 115 GSM.

The current methods applied to material screening for adsorption-based heat pumps are based on a fixed set of temperatures or their independent variation, providing a limited, insufficient, and unpractical evaluation of different adsorbents. This work proposes a novel strategy for the simultaneous optimization and material screening in the design of adsorption heat pumps by implementing a meta-heuristic approach, particle swarm optimization (PSO). The proposed framework can effectively evaluate variable and broad operation temperature intervals to search for viable zones of operation for multiple adsorbents at once. The criteria for selecting the adequate material were the maximum performance and the minimum heat supply cost, which were considered the objective functions of the PSO algorithm. First, the performance was assessed individually, followed by a single-objective approximation of the multi-objective problem. Next, a multi-objective approach was also adopted. With the results generated during the optimization, it was possible to find which adsorbents and temperature sets were the most suitable according to the main objective of the operation. The Fisher–Snedecor test was applied to expand the results obtained during PSO application and a feasible operating region built around the optima, enabling the arrangement of close-to-optima data into practical design and control tools. This approach allowed for a fast and intuitive evaluation of multiple design and operation variables.