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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.

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Background: X-ray methods have proven to be reliable, accurate and sensitive techniques to study activated carbons in order to define its porous structure and functionality groups. Methods: For the first time, the characterization of the exhaustion level of granular activated carbon (GAC) used in rum production applying methods based on X-ray technology has been explored. In this work, the exhaustion degree of GAC from a target rum "filter" using X-ray radiography is evaluated. Digital processing techniques on X-ray radiography images from eight consecutive GAC layers of the industrial rum filter have been assessed. Results: The exhaustion profile for the rum "filter" applying X-ray radiography images processing is correlated with thermal desorptiongas chromatography/ mass spectrometry (TD-GC/MS) chromatograms. The total of pixel detected in the radiographic X-ray images (grey-scale) is related with the total amount of organic adsorbed compounds in the exhausted GAC. Conclusion: The proposed method opens possibilities for the rum producers to improve the management and economical use of the activated carbon at industrial scale. The proposed technique can be applied in the assessment of other adsorption systems such as: determining the poisoning level of catalyst material, the GAC exhaustion level in organic polluted water treatments, and the GAC regeneration degree and exhaustion studies on hemo-perfusion GAC cartridges.

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.

X-ray methods have proven to be reliable, accurate and sensitive techniques to study activated carbons. The studying of granular activated carbon (GAC) samples through X-ray digital radiographic images using Deep Learning, more specifically convolutional neural networks (CNN) class of model, has been explored. Results were compared to hand-engineered characterization using X-Ray absorption method (XRA). It was proved that CNNs represent a fast and reliable analytical tool for indirect information on the chemical and physical characteristics of GACs. The proposed method opens possibilities for the application of Deep Learning based models on radiographic images for the characterization and comparison of exhausted and virgin porous materials.

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Spectrophotometric measurement applied on saturated granular activated carbon (GAC) is not yet explored. A colorimetric method in the visible range has been developed in order to determine the exhaustion level of GAC used in rum production. Aqueous ammonia solution has been used as an indicative agent to determine the extraction rate of taste compounds within the rum production process and the exhaustion degree of the GAC. The colorimetric results showed excellent correlation with the iodine number and the contact pH. The proposed colorimetric method opens possibilities for rum producers to improve the management and economical use of the activated carbon at the industrial scale.