Jeamichel Puente Torres

The chemical regeneration of exhausted granular activated carbon (GAC) from a water treatment plant to produce dialysis water used in hemodialysis treatments for chronic renal disease patients from a general Hospital of Ciego de Avila province, Cuba, was investigated. Activated carbon (AC) exhausted mainly by inorganics (Ca (Ca-chelates), Mg and Na) was regenerated using hydrochloric acid and acetic acid (one regeneration cycle). Solutions of 5%, 10%, 15% and 20%(v/v) as well as four contact times (2 h, 4 h, 6 h and 48 h) for hydrochloric acid and three contact times (2 h, 4 h and 6 h) for acetic acid at 25°C and 1 atm in a dosage of 1 g GAC/10 mL were used. Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES), thermogravimetric analysis (TGA), X-ray absorption technique (XRA) and Scanning Electronic Microscopy (SEM) were used to evaluate the effect of the regeneration on exhausted GAC. Batch and scaled column experiments were developed. Breakthrough curves were obtained to assess the AC’s performances after chemical regeneration. The removal/adsorption capacity towards free chlorine (as disinfection agent) and hardness ions (expressed in mg CaCO3/L) was estimated using the area under the breakthrough curves from scaled columns experiments. The hydrochloric acid arises as the best acidic regenerator; using a concentration of 20%(v/v), regenerated GACs using hydrochloric acid showed the highest desorption rate of inorganics as well as a free chlorine removal performance of around 30% concerning the virgin GAC sample. A new GAC management scheme is proposed for GACs used in the medical industry to improve the sustainability and economics of the water treatment process.

Green roofs contribute to more sustainable cities, but current commercial substrates suffer from important limitations. If carefully selected, biochar could serve as a viable option for a more sustainable green roof substrate. We propose a protocol to select an optimal biochar for green roof substrate amendment. Coffee husks, medium-density fiberboard, palm date fronds, and a mixture of waste wood, tree bark, and olive stone kernels are selected as residues for biochar production to develop a selection protocol. The residues are pyrolyzed at 350, 450, 500, and 550 °C in a lab-scale reactor. A pyrolysis temperature of 450 °C is selected for upscaling and is based on biochar yield, pH, salinity, and elemental composition. From evaluating the biochar characteristics after upscaling, it can be concluded that the biochar’s carbonization degree is mainly controlled by pyrolysis temperature, while yield, pH, and salinity are more dependent on the biomass properties. Ultimately, our procedure evaluates the presence of important contaminants, the biochar’s water holding capacity, salinity, pH, and carbonization degree. To validate the developed protocol, plant coverage experiments on green roofs are performed, which are quantified using a novel digital image processing method, demonstrating its efficient use to facilitate future biochar selection in substrates.

A mathematical tool has been developed to evaluate the economic advantages of in-situ chemical regeneration of fixed-bed industrial adsorbers of granular activated carbon for cooling water treatment systems in Cuban power plants. Two scenarios of activated carbon (AC) management in a power plant were compared by applying the proposed model. The economic profit by implementing the regeneration strategy as a function of the number of regeneration cycles was determined and optimized. Breakthrough curves were obtained to assess the adsorption performance of the AC after progressive saturation–chemical regeneration cycles using synthetic water and hydrochloric acid, respectively. For the first saturation cycle, the breakthrough time was 272 min and after 10 cycles, it was reduced to 58 min, indicating a decrease of the adsorption capacity of 21%. The AC adsorption performance in terms of saturation time as a function of the number of regeneration cycles was considered one of the tool parameters. The proposed tool allows to determine the optimal number of regeneration cycles for a maximum economic profit in the regeneration strategy. It was demonstrated, using the proposed tool, that after an optimum of seven regeneration cycles, the power plant expends only 26% of the total investment. The simplicity of the tool permits a rapid way to find the most profitable number of regeneration cycles by combining economic, technical and adsorption efficiency parameters in one function, thus improving the AC management strategy at an industrial scale with corresponding environmental and economic advantages, including sustainability.

Fe-TiO₂/AC and Co-TiO₂/AC composites were prepared from activated carbon (AC) derived from residues of peanut hulls and TiO₂ photocatalyst, electrochemically prepared from titanium scrap, and doped with Fe and Co, respectively. The adsorption capacity and photocatalytic activity of the Fe-TiO₂/AC and Co-TiO₂/AC composites were studied for removing and degrading Cibacron Yellow F-4G (CYF-4G) from wastewater. Doped ACs were characterized by thermogravimetry (TGA), scanning electron microscopy (SEM), X-ray diffraction (XRD), a new X-ray absorption technique (XRA), and elemental analysis (EA). Interesting relationships were found between SEM, XRA, and TGA data and the doped amount of catalyst on ACs. Optimal dye adsorption was found at a pH of 2.0. The CYF-4G adsorption kinetics are followed according to the pseudo-second order model. The experimental data revealed that the Langmuir model fits better than the Freundlich and Temkin models. A decrease in adsorption capacity was observed when the catalyst dope percentage increased. A removal and degradation efficiency of the dye close to 100% was achieved around 120 min. A synergistic adsorption and photocatalytic degradation effect of the Fe-TiO₂/AC and Co-TiO₂/AC composites could be observed when adsorption experiments were conducted under simulated visible radiation.

The X-ray absorption (XRA) method using digital image processing techniques is a reliable technique to determine the exhaustion degree of granular activated carbons (GACs). Using an innovative digital image processing technique, the identification of individual adsorbed molecules or ions in a GAC was possible. Adsorption isotherm models (Langmuir and Freundlich) were used to simulate the adsorption equilibrium data of Methylene Blue (MB), nickel, cobalt and iodine. Freundlich equation was found to have the highest value of R² compared with Langmuir. The identification of distinctive patterns applying XRA for different adsorbed ions and molecules onto GAC was explored. It is demonstrated that unique XRA configurations for each adsorbed ion or molecule are found, as well as a proportional relationship between its incident energy (needed to achieve maximum photon attenuation) and the (effective) atomic number, the adsorbate mass and the molar or atomic mass of adsorbed molecule or ion. XRA method in combination with image histogram modifications was used to obtain a digital signature of adsorbed ions/molecules, giving distinct GSI values for each one in the used energy range. Probabilistic models prove that XRA results are within relationships between effective atomic number and photonic interaction probability, reinforcing the potentialities of XRA for monitoring (multi-)ion and/or molecule combinations on GAC using advanced digital image processing techniques. It was proved that the proposed approach could assess different adsorbed ions/molecules onto GACs in water purification systems.

Petroleum crudes of different geographical origin exhibit differences in chemical composition that arise from formation and ripening processes in the crude. Such differences are transmitted to the fractions obtained in the processing of petroleum. The use of unsupervised classification/sorting methods such as principal component analysis (PCA) or cluster analysis to near-infrared (NIR) spectra for bitumens obtained from petroleum crudes of diverse origin has revealed that composition differences among bitumens are clearly reflected in the spectra, which allows them to be distinguished in terms of origin. Accordingly, in this work we developed classification methods based on soft independent modeling of class analogy (SIMCA) and artificial neural networks (ANNs). While the latter were found to accurately predict the origin of the crudes, SIMCA methodology failed in this respect.