Development of a sustainability-oriented university laboratory: Insight into adsorption kinetics models for the removal of pollutants from aqueous solution.

The aim of the present research is to show the development of a sustainability-oriented lab that teaches adsorption concepts in a virtual environment based on the premise "learning-through-play". Kinetic results in the virtual environment are contrasted to those obtained experimentally when diverse adsorbents prepared from Agave Bagasse (Raw Fibers, Hydrothermal Fibers, and Activated Fibers) were synthesized. Comparison between virtual and real-life experiments involving removal of methylene blue in solution showed that a pseudo-first-order model could describe adsorption kinetics satisfactorily. The study is complemented with a characterization of the adsorbents through SEM, nitrogen adsorption isotherms, FTIR and Raman. In addition, the environmental impact of the synthesis of adsorbents was evaluated through well-known methodologies (GAPI, NEMI, and Eco-Scale), which agree that raw fibers are the most eco-friendly material. This research provides an exciting opportunity to advance our knowledge on developing new technologies for teaching in engineering and to compliment real-life practices that consider environmental impacts with virtual experiments.

Development of a Low-Cost Artificial Vision System as an Alternative for the Automatic Classification of Persian Lemon: Prototype Test Simulation.

In the present research work, an algorithm of artificial neural network (ANN) has been developed based on the processing of digital images of Persian lemons with the aim of optimizing the quality control of the product. For this purpose, the physical properties (weight, thickness of the peel, diameter, length, and color) of 90 lemons selected from the company Esperanza de San José Ornelas SPR de RL (Jalisco, Mexico) were studied, which were divided into three groups (Category "extra", Category I, and Category II) according to their characteristics. The parameters of weight (26.50 ± 3.00 g), diameter/length (0.92 ± 0.08) and thickness of the peel (1.50 ± 0.29 mm) did not present significant differences between groups. On the other hand, the color (determined by the RGB and HSV models) presents statistically significant changes between groups. Due to the above, the proposed ANN correctly classifies 96.60% of the data obtained for each of the groups studied. Once the ANN was trained, its application was tested in an automatic classification process. For this purpose, a prototype based on the operation of a stepper motor was simulated using Simulink from Matlab, which is connected to three ideal switches powered by three variable pulse generators that receive the information from an ANN and provide the corresponding signal for the motor to turn to a specific position. Manual classification is a process that requires expert personnel and is prone to human error. The scientific development presented shows an alternative for the automation of the process using low-cost computational tools as a potential alternative.

Evaluation of the Environmental Performance of Adsorbent Materials Prepared from Agave Bagasse for Water Remediation: Solid Waste Management Proposal of the Tequila Industry.

In the present research work, the use of agro-industrial waste such as agave bagasse from the tequila industry was carried out. The agave bagasse was treated to obtain biosorbent and hydrochar materials. Direct Blue 86 was used as an adsorbate model to evaluate the performance of both materials. The adsorption studies showed an adsorption capacity of 6.49 mg g−1 in static and 17.7 mg g−1 in dynamic, associated with a physisorption process between functional groups of the material and the dye. The characterization of the biosorbent showed that the material was mainly composed of macroporous fibers with a surface area <5.0 m2 g−1. Elemental analysis showed a majority composition of C (57.19 wt%) and O (37.49 wt%). FTIR and XPS analyses showed that the material had C-O, C=O, -OH, O-C=O, and -NH2 surface groups. RAMAN and TGA were used to evaluate the composition, being cellulose (40.94%), lignin (20.15%), and hemicellulose (3.35%). Finally, the life-cycle assessment at a laboratory scale showed that the proposed biosorbent presents a 17% reduction in several environmental aspects compared to hydrochar, showing promise as an eco-friendly and highly efficient method for the remediation of water contaminated with dye, as well as being a promising alternative for the responsible management of solid waste generated by the tequila industry.