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The most successful organizations are the ones able to accurately predict the future of their industry and develop a vision of their place in that future. This is especially true in an era when increasingly affordable technology innovations are making it possible for businesses to compete on a nearly level playing field with large corporations. The rapid development of technology has paved the way for the constant development of new breakthrough solutions. In the current business landscape, technology‐based solutions are enabling significant optimization of processes and operations, while reducing the reliance on manual activities and people costs. Digitally enabled businesses have triggered a shift in consumer behaviors and expectations. Digitally enabled businesses have also transformed the customer journey. The digital global business landscape is also driving increased collaboration between competitors. In the current competitive landscape, companies need more than investments to penetrate and capture new markets.

A continuous adsorption study in a fixed-bed column was carried out using a chitosan–glutaraldehyde biosorbent for the removal of the textile dye Direct Blue 71 from an aqueous solution. The biosorbent was prepared from shrimp shells and characterized by scanning electron microscopy, X-ray diffraction, and nuclear magnetic resonance spectroscopy. The effects of chitosan–glutaraldehyde bed height (3–12 cm), inlet Direct Blue 71 concentration (15–50 mg l−1), and feed flow rate (1–3 ml min−1) on the column performance were analyzed. The highest bed capacity of 343.59 mg Direct Blue 71 per gram of chitosan–glutaraldehyde adsorbent was obtained using 1 ml min−1 flow rate, 50 mg l−1 inlet Direct Blue 71 concentration, and 3 cm bed height. The breakthrough curve was analyzed using the Adams–Bohart, Thomas, and bed depth service time mathematical models. The behaviors of the breakthrough curves were defined by the Thomas model at different conditions. The bed depth service time model showed good agreement with the experimental data, and the high values of correlation coefficients (R2 ≥ 0.9646) obtained indicate the validity of the bed depth service time model for the present column system.

The excessive discharge of phosphate from anthropogenic activities is a primary cause for the eutrophication of aquatic habitats. Several methodologies have been tested for the removal of phosphate from aqueous solutions, and adsorption in a flow-through reactor is an effective mechanism to reduce the nutrient loading of water. This research aimed to investigate the adsorption potential of leftover coal material to remove phosphate from a solution by using continuous flow fixed-bed column, and analyzes the obtained breakthrough curves. A series of column tests were performed to determine the phosphorus breakthrough characteristics by varying operational design parameters such as adsorbent bed height (5 to 8 cm), influent phosphate concentration (10–25 mg/L), and influent flow rate (1–2 mL/min). The amorphous and crystalline property of leftover coal material was studied using XRD technology. The FT-IR spectrum confirmed the interaction of adsorption sites with phosphate ions. Breakthrough time decreased with increasing flow rate and influent phosphate concentration, but increased with increasing adsorbent bed height. Breakthrough-curve analysis showed that phosphate adsorption onto the leftover coal material was most effective at a flow rate of 1 mL/min, influent phosphate concentration of 25 mg/L, and at a bed height of 8 cm. The maximal total phosphate adsorbed onto the coal material’s surface was 243 mg/kg adsorbent. The Adams–Bohart model depicted the experimental breakthrough curve well, and overall performed better than the Thomas and Yoon–Nelson models did, with correlation values (R2) ranging from 0.92 to 0.98. Lastly, leftover coal could be used in the purification of phosphorus-laden water, and the Adams–Bohart model can be employed to design filter units at a technical scale.

The transition to a hydrogen economy requires the development of cost-effective methods for purifying hydrogen from CO. In this study, we explore the possibilities of Cu/ZSM-5 as an adsorbent for this purpose. Samples obtained by cation exchange from aqueous solution (AE) and solid-state exchange with CuCl (SE) were characterized by in situ EPR and FTIR, H2-TPR, CO-TPD, etc. The AE samples possess mainly isolated Cu2+ cations not adsorbing CO. Reduction generates Cu+ sites demonstrating different affinity to CO, with the strongest centres desorbing CO at about 350 °C. The SE samples have about twice higher Cu/Al ratios, as one H+ is exchanged with one Cu+ cation. Although some of the introduced Cu+ sites are oxidized to Cu2+ upon contact with air, they easily recover their original oxidation state after thermal treatment in vacuum or under inert gas stream. In addition, these Cu+ centres regenerate at relatively low temperatures. It is important that water does not block the CO adsorption sites because of the formation of Cu+(CO)(H2O)x complexes. Dynamic adsorption studies show that Cu/ZSM-5 selectively adsorbs CO in the presence of hydrogen. The results indicate that the SE samples are very perspective materials for purification of H2 from CO.

The scientific impact of this work is the protection of the environment from hazardous pollutants using a column technique. Besides its higher stability at working pH and its time persisting, Ni-alginate has a higher ability to remove lead ions compared to the other prepared beads (Sr-alginate, Co-alginate, and Ca-alginate). Also, Ni-alginate possessed a higher removal percent, 93.3%, toward Pb 2+ than the other ions, taking the sorption order of Pb 2+  > Sr 2+  > Co 2+  > Cd 2+  > Zn 2+ . Therefore, this study focused on using Ni-alginate as a selective sorbent for lead ions. Fixed-bed column was employed for the sorption process. The results for that efficiency are presented as breakthrough curves in view of the impact of various parameters; influent flow rate (1.5, 3.0, and 5.0 mL/min), lead concentration (100, 150, and 200 mg/L), and bed depth of sorbent (3.0, 5.0, and 7.0 cm). Breakthrough modeling including Thomas and Yan models was employed. The outcomes indicated that Thomas theory is more applicable. The overall outcomes indicated that Ni-alginate is recommended for selective removal of Pb 2+ from waste solutions.

The present work analyzes the behavior of an activated carbon fabricated from almond shells for the removal of cationic dyes (methylene blue, MB, and malachite green, MG) by adsorption from aqueous solutions. The carbonized precursor was activated with KOH at a 1:2 (w/w) ratio with the objective of increasing both the surface area and the pore volume. Both non-activated and activated carbon were characterized in different aspects of interest in dye adsorption studies (surface structure, point of zero charge, specific surface area, and pore size distribution). The effect of the dye’s initial concentration and adsorbent dosage on dye removal efficiency and carbon adsorption capacity was studied. Adsorption kinetics were analyzed under different experimental conditions, and different models were assayed to determine the adsorption mechanism. Dye adsorption in the adsorbent surface could be considered the rate-limiting step. Different adsorption equilibrium models were evaluated to fit the experimental data. This adsorbent allowed us to reach high Langmuir adsorption capacity for both dyes (MB: 341 mg·g−1, MG: 364 mg·g−1 at 25 °C and 0.5 g·L−1). Moreover, kinetic and equilibrium adsorption data have been used to simulate breakthrough curves in a packed-bed column using different conditions (bed length, liquid flowrate, and dye initial concentration). The simulation results showed that almond shell activated carbon is a suitable adsorbent for methylene blue and malachite green removal from wastewater.

Explicit fracture models often use analytical solutions for predicting flow in fractured media, usually assuming uniform fluid viscosity for simplicity. This assumption, however, can be inaccurate as fluid viscosity varies due to factors like composition, temperature, and dissolved substances. Our study, recognizing these discrepancies, abandons this uniform viscosity assumption for a more realistic model of variable viscosity flow, focusing on viscous displacement scenarios. This includes instances like injecting viscous surfactants for hydrocarbon recovery in fractured reservoirs or soil decontamination. This presents a significant challenge, enhancing our understanding of transport within fractures, mainly governed by advection. Our study centers on a low‐permeability rock matrix intersected by two fractures with variable apertures. We employ two methods: an analytical approach with a new solution and numerical simulations with two distinct in‐house codes, discretizing both the rock matrix and fractures with two‐dimensional triangular elements. The first code uses a Discontinuous Galerkin finite element method, while the second utilizes a finite‐volume method, allowing a comprehensive comparison of solutions. Additionally, we investigate parameter identifiability, like fracture apertures and viscosity ratios, using breakthrough curves from our analytical solution, applying the Markov Chain Monte Carlo technique. Key Points Analytical model developed for variable viscosity flow in fractured media, overcoming the common uniform viscosity assumption Analytical and numerical solutions demonstrate overall consistency, with minor discrepancies observed Breakthrough curves from the analytical solution, using Markov Chain Monte Carlo, enable confident estimation of fracture apertures

The adsorption of Congo red (CR), an azo dye, from aqueous solution using free and immobilized agricultural waste biomass of Nelumbo nucifera (lotus) has been studied separately in a continuous fixed-bed column operation. The N. nucifera leaf powder adsorbent was immobilized in various polymeric matrices and the maximum decolorization efficiency (83.64%) of CR occurred using the polymeric matrix sodium silicate. The maximum efficacy (72.87%) of CR dye desorption was obtained using the solvent methanol. Reusability studies of free and immobilized adsorbents for the decolorization of CR dye were carried out separately in three runs in continuous mode. The % color removal and equilibrium dye uptake of the regenerated free and immobilized adsorbents decreased significantly after the first cycle. The decolorization efficiencies of CR dye adsorption were 53.66% and 43.33%; equilibrium dye uptakes were 1.179 mg g–1 and 0.783 mg g–1 in the third run of operation with free and immobilized adsorbent, respectively. The column experimental data fit very well to the Thomas and Yoon–Nelson models for the free and immobilized adsorbent with coefficients of correlation R2 ≥ 0.976 in various runs. The study concludes that free and immobilized N. nucifera can be efficiently used for the removal of CR from synthetic and industrial wastewater in a continuous flow mode. It makes a substantial contribution to the development of new biomass materials for monitoring and remediation of toxic dye-contaminated water resources.

This work describes an experimental and machine learning approach for the prediction of selenite removal on chemically modified zeolite for water treatment. Breakthrough curves were constructed using iron-coated zeolite adsorbent and the adsorption behavior was evaluated as a function of an initial contaminant concentration as well as the ionic strength. An elevated selenium concentration in water threatens human health and aquatic life. The migration of this metalloid from the contaminated sites and the problems associated with its high releases into the water has become a major environmental concern. The mobility of this emerging metalloid in the contaminated water prompted the development of an efficient, cost-effective adsorbent for its removal. Selenite [Se(IV)] removal from aqueous solutions was studied in laboratory-scale continuous and packed-bed adsorption columns using iron-coated natural zeolite adsorbents. The proposed adsorbent combines iron oxide and natural zeolite’s ability to bind contaminants. Breakthrough curves were initially obtained under variable experimental conditions, including the change in the initial concentration of Se (IV), and the ionic strength of solutions. Investigating the effect of these parameters will enhance selenite mobility retardation in contaminated water. Continuous adsorption experiment findings will evaluate the efficiency of this economical and naturally-based adsorbent for selenite removal and fate in water. Multilinear and non-linear regressions approaches were utilized, yet low coefficients of determination values were respectively obtained. Then, a comparative analysis of five boosted regression tree algorithms for a selenite breakthrough curve prediction was performed. AdaBoost, Gradient boosting, XGBoost, LightGBM, and CatBoost models were analyzed using the experimental data of the packed-bed columns. The performance of these models for the breakthrough curve prediction under different operation conditions, such as initial selenite concentration and ionic strength, was discussed. The applicability of these models was evaluated using performance metrics (i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The CatBoost model provided the best fit for a breakthrough prediction with a coefficient of determination R2 equal to 99.57. The k-fold cross-validation technique and the statistical metrics verify this model’s accurateness. A feature importance assessment indicated that Se (IV) initial concentration was the most influential experimental variable, while the ionic strength had the least effect. This finding was consistent with the column transport results, which observed Se (IV) sorption dependency on its inlet concentration; simultaneously, the ionic strength effect was negligible. This work proposes implementing machine learning-based approaches for predicting water remediation-associated processes. The significance of this work was to provide an alternative method for investigating selenite adsorption behavior and predicting the breakthrough curves using a machine-based approach. This work also highlighted the importance of management practices of adsorption processes involved in water remediation.

In this study, the mechanisms of SO2 adsorption on lignite char and char-supported Fe-Zn-Cu sorbent (FZC sorbent) were investigated. The FZC sorbent was prepared by the impregnation of metal components on raw coal followed by steam gasification. Flue gas desulfurization experiments were carried out on a fixed-bed reactor at 100–300 °C by using simulated flue gas containing SO2/O2/H2O balanced by N2. The flue gas composition was monitored by using an online flue gas analyzer. The solid samples before and after desulfurization were analyzed by using X-ray diffraction (XRD), Fourier Transform Infrared (FTIR) spectroscopy, Thermogravimetric Analysis–Mass Spectroscopy (TG-MS), and Brunauer–Emmett–Teller (BET) surface area analysis. The experimental results showed that both lignite char and the FZC sorbent can effectively adsorb SO2 under the present experimental conditions. The presence of O2 and H2O in the flue gas promoted the adsorption of SO2 on the FZC sorbent. The SO2 adsorption capacity of the FZC sorbent increased with the increase in the temperature up to 250 °C. When the temperature was further increased to 300 °C, the SO2 adsorption capacity of the sorbents decreased rapidly. Under optimum experimental conditions with a space velocity of 1500 h−1, a desulfurization temperature of 250 °C, and 5% (vol) O2 and 10% (vol) H2O in the flue gas, the sorbents exhibited the longest breakthrough time of 280 min and breakthrough SO2 adsorption capacity of about 2200 mg (SO2) per gram sorbent.