Removal of heavy metals from acid mining effluents by hydrolyzed olive cake.

The present study aims to solve two major challenges of the current society. On the one hand, it investigated the heavy metal removal from mining wastewater. On the other hand, it proposed an alternative use for olive cake. Firstly, a physic-chemical characterization of real wastewater and hydrolyzed olive cake was carried out. Secondly, a study of the affinity of the material for the different metals (chromium, manganese, copper, zinc, nickel and lead) was performed. The hydrolyzed olive cake showed a low content in ash (3.08%) and in water-soluble compounds (2.80%). The material presented the highest retention capacity for the lead (41.54 mg/g) and the lowest for the manganese (3.57 mg/g). After that, biosorption experiments in fixed-bed column were carried out using mining real water. In order to improve the biosorption capacity, the water pH was raised up to 6. The results were quite satisfactory with respect to others similar studies.

Optimization of the sugar hydrothermal extraction process from olive cake using neuro-fuzzy models.

The optimization of the hydrothermal extraction step in the biorefinery scheme in order to obtain sugars and antioxidants from olive cake was carried out. This process using water and low temperatures for the olive cake has not been previously studied by other authors. Central Composite Design of experiments was carried out. Four variables and three levels for each variable were tested: temperature (50, 70 and 90 °C) time (60, 120, 180 min), average particle diameter (0.42, 0.96 and 1.5 mm) and solid/liquid ratio (0.15, 0.20, 0.25). The main aim was to understand the relationship between the process variables and the yield of extraction of glucose, xylose, polyphenols and oligomers. The variables which most influenced the process were particle diameter and solid/liquid ratio. The experimental values were adjusted to a classical polynomial model and to a neuro-fuzzy system. The neuro-fuzzy demonstrated to be much more accurate when predicting the experimental values.

Neural fuzzy modelization of copper removal from water by biosorption in fixed-bed columns using olive stone and pinion shell.

Continuous copper biosorption in fixed-bed column by olive stone and pinion shell was studied. The effect of three operational parameters was analyzed: feed flow rate (2-6 ml/min), inlet copper concentration (40-100 mg/L) and bed-height (4.4-13.4 cm). Artificial Neural-Fuzzy Inference System (ANFIS) was used in order to optimize the percentage of copper removal and the retention capacity in the column. The highest percentage of copper retained was achieved at 2 ml/min, 40 mg/L and 4.4 cm. However, the optimum biosorption capacity was obtained at 6 ml/min, 100 mg/L and 13.4 cm. Finally, breakthrough curves were simulated with mathematical traditional models and ANFIS model. The calculated results obtained with each model were compared with experimental data. The best results were given by ANFIS modelling that predicted copper biosorption with high accuracy. Breakthrough curves surfaces, which enable the visualization of the behavior of the system in different process conditions, were represented.