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Nomenclature
Introduction
Water sources play an essential role in human life due to their great usefulness and versatility in different activities, e.g., industrial processes, agricultural and livestock activities, hospitals, shopping centers, rural areas, and domestic households, among others 1). Using this resource generates different types of wastewater that are loaded with pollutants. A large portion of this water, which originates from industrial activity, is considered to be of great concern due to its properties, as is the case with the toxic substances it contains 2)(3). This toxicity depends on various factors such as chemical composition, which varies from sector to sector, making the elimination of these substances a challenge, as each effluent contains unique pollutants that require customized treatment 4)(5). Among the various pollutants present in industrial wastewater are heavy metals, a worldwide concern due to their high toxicity, their detrimental impact on both the environment and human health, and their high strength and non-biodegradability 6).
Despite being an essential metal, chromium is considered to be one of the 16 main toxic pollutants with adverse effects on human health. This metal, which can be ingested, inhaled, or absorbed through the skin 7, is used in various manufacturing sectors, such as the metallurgy, tanning, cement, textile, and dyeing industries. These sectors represent major sources of pollution 8). Chromium ranks second in abundance among the heavy metal pollutants, surpassed only by lead. It is naturally found in three forms: metallic, trivalent, and hexavalent, with the latter, Cr(IV), being the toxic one. This form has a variety of detrimental health effects, such as digestive, urinary, reproductive, and immune system dysfunctions. Therefore, it is necessary to treat any water sources contaminated with Cr(IV) that are discharged into the environment 9). In Colombia, the limit for chromium in drinking water, as dictated by Law 0631 of 2015, is 0.5 mg/L 10, while the World Health Organization’s permitted level is 0.05 mg/L for Cr 11.
Adsorption is an effective method for removing unwanted pollutants, given its feasibility and easy scalability. This method is also able to effectively remove various inorganic chemicals that affect the adsorption system 12). This technique has several advantages that make it a very attractive method for use in water treatment. Among these advantages are its efficiency in removing various types of pollutants, including heavy metals, dyes, emerging contaminants, and other types of substances 13; its applicability at low temperature and pressure, since, compared to other water treatment processes, it does not require extreme conditions; its versatility in dealing with pollutants in both gaseous and liquid form 14; the regenerative capacity of the materials used in the adsorption process, which allows reusing biomaterials multiple times depending on the type of biomass employed and its characteristics 15; the speed at which this technique removes pollutants, allowing for effective and fast processes; and its versatility in enabling a modular and scalable design of the adsorption process in the form of columns or adsorbent beds, modeled on a small or a large scale as required 16.
This technique may involve one of two different adsorption methods: physisorption, wherein adsorption stems from the presence of weak attractions between the pollutant and the adsorbent material, generated by short-range electrostatic forces (i.e., van der Waals forces) 17; and chemisorption, where adsorption takes place due to the presence of chemical bonds between the adsorbate and the adsorbent, causing a stronger and more selective interaction. This process is less reversible than physisorption and requires considerably more energy for the desorption of the adsorbed species 18. The adsorption process can be carried out in two ways: in batches or in columns 19.
This technique has been recognized as one of the simplest and most economical strategies due to its remarkable ability for heavy metal removal 20)(21. Different biomaterials have been developed and used to this effect, such as Ulva flexuosa, a species of algae 22; babano shell 23; rapeseed (Brassica napus) 24; and coconut 25, among others. Cocoa (Theobroma cacao L.) is a widely cultivated product in Colombia, and it generates a large amount of residual biomass, comprising the husk of the cob, the husk of the bean, and the pulp. Among the different byproducts of this plant, the husk is a promising material for the production of adsorbent materials, given its abundance, low cost, and renewable nature. The cocoa husk is composed of cellulose, hemicellulose, and lignin, and it offers a high adsorption capacity, which makes it a sustainable alternative for the elimination of pollutants in aqueous media, e.g., heavy metals 26. Most adsorption studies have been conducted at the laboratory level under simple conditions and on small scales. This is due to different limiting factors, such as resource, space, and time availability, among others. In light of the above, several computational tools like Aspen Plus 27) or ChemCAD 28, have been developed for modeling processes on a larger scale, but scaling to solids in adsorption towers is still in its early stages. Therefore, researchers have searched for new ways to scale their proposals under the existing limitations. Among them, the Aspen Adsorption software has proven to be a tool for adsorption columns. This tool allows modeling multi-scale adsorption columns by simulating key phenomena such as mass transfer, adsorption equilibrium, and fluid dynamics. It allows predicting performance at the laboratory, pilot, and industrial scales, optimizing system design and operation based an experimental data. Its use in treating chromium-contaminated water also demonstrates its ability to respond to various operating conditions, improving process efficiency and supporting scaling from laboratory-level to industrial applications. This tool constitutes an advantage for a broader exploration of different parameters or operating variables such as flow, bed height, and pollutant concentration, among others, facilitating the optimization of the process before designing a pilot or building industrial plant.
A study published by 29) delved into the performance of dolochar as a Cd(II) adsorbent, using Aspen Adsorption to simulate a large-scale process. The results, obtained via the response surface methodology, show that, with optimal bed height, inlet concentration, flow rate, and fixed biosorbent mass values, the CD(II) ion adsorption capacity and depletion time of the packed dolochar bed are 1.85 mg/g and 11.39 hours, respectively.
Using Aspen Adsorption V11, the study by 30 simulated the adsorption of Pb(II) on tire-based activated carbon (TAC) and commercial activated carbon (CAC) in a fixed-bed column while considering different concentration ranges, bed heights, and flow rates. The optimal conditions found in this study included a concentration of 500 mg/L, a bed height of 0.6 m, and a flow rate of 9.88×10−4 m3/s, which yielded breakthrough times of 488 and 23 s for TAC and CAC, respectively, with removal capacities of 114.26 and 7.72 mg/g.
In light of the above, the objective of this study was to model an industrial column packed with Theobroma cacao L. for the adsorption of Cr(VI) in solution, using Aspen Adsorption to conduct a parametric sensitivity analysis and evaluate the performance of the system by altering key parameters. Our work demonstrates the potential of computational tools for predicting the performance of adsorption columns and provides a solid basis for the design and simulation of large-scale adsorption systems that employ agro-industrial materials for water treatment.
Materials and methods
Parameterization and modeling
In this study, we used Aspen Adsorption V12.1 to simulate an adsorption column packed with Theobroma cacao L. for the removal of Cr(VI) in an industrial stream. A parametric study was carried out regarding the inlet flow rate, the initial concentration, and the column height, with the aim of determining the extent to which modifying these factors affects adsorption performance. We also conducted a sensitivity analysis that considered the breakthrough profile. Different parameter ranges were used to evaluate the performance of the adsorption system. For the inlet flow rate, values of 250, 200, 150, 100, and 50 m3/day were employed; for the bed height, we established values of 3, 4, and 5 mΣ30]; and, finally, for the initial concentration of Cr(VI), 5000, 3500, 2000, and 1000 mg/L were considered 31)(32.
To design our proposal, studies on the use of columns for the removal of heavy metals from industrial wastewater were used as a basis, considering the different parameters required to simulate the process in Aspen Adsorption. In this vein, we established a bed diameter of 1 m 33, a bulk density of 0.0365 g/cm3 for the biomaterial 34, a bed porosity of 0.67 (m3 of voids per m3 of bed), a total vacuum porosity of 0.4 35)(36, and a constant mass transfer coefficient of 1.37×10−4 s−1 (37.
To understand the behavior caused by the interactions between the adsorbate and the adsorbent during the process, we employed the Freundlich isothermal model 38, while the linear driving force (LDF) kinetic model was used to determine the rate at which the adsorption occurred 39. Fig. 1 presents the simulation flowchart for the column.
Mathematical fundamentals
Mass balance
The equation used by Aspen Adsorption for the mass balance of the adsorption column is presented below:
Results and discusión
Evaluating the mathematical models using Aspen Adsorption
The Freundlich isothermal model and the LDF kinetic model were evaluated while considering the above-presented parameters and ranges. The results indicated that, by reducing the height of the column and increasing the flow rate, the break and saturation times can be reduced. This is due to the fact that, with a low bed height and a high flow rate, the fluid passes through the bed more quickly, which results in reduced process times. The results for the break time (TR) and the saturation time (TS) are shown in Table II.
Influence of the inlet flow rate
By means of a parametric sensitivity analysis, the influence of the inlet flow rate on the performance of the adsorption column was evaluated. To this effect, inlet flow rates of 250, 200, 150, 100, and 50 m3/day were considered. Fig. 2 shows the breakthrough profiles obtained from the Freundlich-LDF model. Note that the efficiency increases with the flow rate, but the breakthrough and saturation times are reduced. This behavior is due to the fact that a high inflow rate has a positive effect on mass transfer, causing a faster accumulation of the pollutant and decreasing the number of available active sites, which leads to reduced times. This behavior is also evident in the efficiency values obtained: 95.2 % for 250 m3/day, 94 % for 200 m3/day, 92.1 % for 150 m3/day, 88.4 % for 100 m3/day, and 78.1 % for 50 m3/day (44,45).
Influence of initial concentration
For the parametric sensitivity analysis of the initial Cr(VI) concentration, values of 5000, 3500, 2000, and 1000 mg/L were considered. Fig. 3 shows the breakthrough profiles obtained from the simulation of the Freundlich-LDF model. Note that, when this parameter is increased or decreased, the rupture times (TR) and saturation times (TS) obtained for each column configuration are very close to each other. Similarly, the efficiencies achieved show a similar trend, indicating that, under these conditions, the influence of concentration on the difference between TR, TS, and the overall system performance is limited. This phenomenon is due to the high presence of Cr(VI) in the flow, which causes an early break in the curve—this behavior is also observed at different times. Nevertheless, varying this parameter does not affect the adsorption performance, as the observed difference was less than 1 %. This can be attributed to different factors, such as the number of active sites available in the adsorbent, the strong affinity between the adsorbent and the adsorbate (which allows quickly reaching adsorption equilibrium), and the operating conditions considered, among others 46.
Influence of bed height
We also analyzed the influence of bed height on the performance of the adsorption column used for the removal of Cr(VI) in aqueous solution. The values considered in this analysis were 3, 4, and 5 m. Fig. 4 shows the breakthrough profiles obtained from the simulation of the Freundlich-LDF model. The breakthrough and saturation times increase with bed height, whereas efficiency is reduced. This is due to the fact that, with a larger adsorption surface, the fluid entering the column takes longer to exit. In addition, there are more active sites available, which extends the useful life of the adsorbent material, since it does not saturate as quickly, resulting in increased process times. On the other hand, the efficiency values obtained after the simulation were 92.9 % for 3 m, 90.6 % for 4 m, and 88.4 % for 5 m 47.
Comparison with other results in the literatura
The data obtained after conducting various simulations of the industrial cocoa-packed adsorption column were compared against the results of studies published in the specialized literature. It should be noted that this comparison has relative value, since each study was carried out under different inlet flow rate, initial concentration, bed height, and biomaterial conditions. The results of this work indicate that Theobroma cacao L. offers an acceptable in Cr(VI) removal. Table III shows de aforementioned comparison.
Conclusions
This study presented an innovative approach to modeling and simulating large-scale adsorption columns using agro-industrial waste (cocoa residues in this case) to remove heavy metals such as Cr(VI) from water systems. This work provides valuable quantitative data that contribute to the development and understanding of industrial adsorption processes. A parametric sensitivity analysis allowed evaluating the effect of varying the column’s bed height, inlet flow rate, and initial pollutant concentration on the efficiency of the process. The results that increasing the bed height increases the break and saturation times but reduces the adsorption efficiency. On the other hand, high inlet flows improve the adsorption efficiency but decrease the biomaterial’s saturation times. It is also noteworthy that the initial concentration does not have a significant effect on adsorption efficiency. These results constitute a robust technical basis for the design and optimization of industrial effluent treatment systems, as they allow anticipating the behavior of the system prior to its full-scale implementation.
