Chromatographic Separation Process Research Articles

Assessment of data‐driven modeling approaches for chromatographic separation processes

AbstractChromatographic separation processes are described by nonlinear partial differential and algebraic equations, which may result in high computational cost, hindering further online applications. To decrease the computational burden, different data‐driven modeling approaches can be implemented. In this work, we investigate different strategies of data‐driven modeling for chromatographic processes, using artificial neural networks to predict pseudo‐dynamic elution profiles, without the use of explicit temporal information. We assess the performance of the surrogates trained on different dataset sizes, achieving good predictions with a minimum of 3400 data points. Different activation functions are used and evaluated against the original high‐fidelity model, using accuracy, interpolation, and simulation time as performance metrics. Based on these metrics, the best performing data‐driven models are implemented in a process optimization framework. The results indicate that data‐driven models can capture the nonlinear profile of the process and that can be considered as reliable surrogates used to aid process development.

Bayesian optimization for quick determination of operating variables of simulated moving bed chromatography

The Simulated Moving Bed (SMB) is a continuous chromatographic separation process that operates on the principle of counter-current movement between the solid and liquid phases. Due to periodic switching of feed and product ports across numerous connected columns, adjusting SMB operating variables such as feed and product flow rates and switching time to achieve desired separations is challenging. While equilibrium theory can help narrow the search space, obtaining essential information such as accurate adsorption isotherms is crucial. This requirement, combined with often highly stringent production specifications, makes it challenging to identify even a feasible operating condition, let alone an optimal one. Trial-and-error-based approaches are often impractical as reaching cyclic steady state can be time-consuming, and any waste produced during this period can lead to significant economic losses. While rigorous dynamic models are available, they are computationally intensive and often do not accurately mirror actual process behavior. To address these challenges, the use of Bayesian Optimization (BO) is proposed to sequentially approach optimal SMB operation. Furthermore, it is suggested to employ the simpler True Moving Bed (TMB) model as a prior for the BO, which significantly accelerates convergence. This approach is demonstrated on an SMB process for cresol separation. Initially, the effectiveness of the BO using the TMB model is examined to gain insights into its behavior. Subsequently, we apply BO to the rigorous SMB model, informed by prior knowledge from the TMB model. Our results show that the developed BO framework rapidly converges to the optimal operating parameters that satisfy the purity constraints. We examine the efficiency improvements over various search algorithms and highlight the advantages of using the TMB model as a prior.

Recent progress of porous cage materials in sample preparation, chromatographic separation, and detection.

Porous cage materials with certain dimensions, sizes, shapes, and functions have been regarded as promising materials for sample preparation, chromatographic separation, and detection process. In contrast to infinite frameworks such as metal-organic frameworks or covalent organic frameworks, porous cage materials are constructed from discrete molecules containing at least one internal cavity. The well-defined cavities in porous cage materials provide opportunities for non-covalent interactions. These interactions can be programmed into the ligand design or supramolecular cage constructing using the cages as building blocks, offering various host-guest recognition with great selectivity. In this review, we desire to elucidate the fundamental principles governing the design and fabrication of porous cage materials with well-defined cavities, good solvent processability, and modifiable groups, the applications of these porous cage materials in sample preparation, chromatographic separation, and detection were discussed. The recent advantages of porous cage materials for the analysis process were summarized. We state the potential of these materials and provide an outlook for further application strategies. We expect that this review can inspire interest in the porous cage materials research area for analysis.

Characterizing Acoustic Behavior of Silicon Microchannels Separated by a Porous Wall.

Lateral flow membrane microdevices are widely used for chromatographic separation processes and diagnostics. The separation performance of microfluidic lateral membrane devices is determined by mass transfer limitations in the membrane, and in the liquid phase, mass transfer resistance is dependent on the channel dimensions and transport properties of the species separated by the membrane. We present a novel approach based on an active bulk acoustic wave (BAW) mixing method to enhance lateral transport in micromachined silicon devices. BAWs have been previously applied in channels for mixing and trapping cells and particles in single channels, but this is, to the best of our knowledge, the first instance of their application in membrane devices. Our findings demonstrate that optimal resonance is achieved with minimal influence of the pore configuration on the average lateral flow. This has practical implications for the design of microfluidic devices, as the channels connected through porous walls under the acoustic streaming act as 760 µm-wide channels rather than two 375 µm-wide channels in the context of matching the standing pressure wave criteria of the piezoelectric transducer. However, the roughness of the microchannel walls does seem to play a significant role in mixing. A roughened (black silicon) wall results in a threefold increase in average streaming flow in BAW mode, suggesting potential avenues for further optimization.

A parameter estimation method for chromatographic separation process based on physics-informed neural network

Chromatographic separation processes are most often modeled in the form of partial differential equations (PDEs) to describe the complex adsorption equilibria and kinetics. However, identifying parameters in such a model requires substantial computational effort. In this work, a novel parameter estimation approach using a Physics-informed Neural Network (PINN) model is developed and tested for a binary component system. Numerical accuracy of our PINN model is confirmed by validating its simulations against those of the finite element method (FEM). Furthermore, model parameters in the kinetic model are estimated by the PINN model with sufficient accuracy from the observed data at the column outlet, where parameter fitting error can be reduced by up to 35.0 % from the conventional method. In a comparison with the conventional numerical method, our approach can reduce the computational time by up to 95 %. The robustness of the PINN model has also been demonstrated by estimating model parameters from noisy artificial experimental data.

Estimating the parameters of competitive Langmuir adsorption isotherms from measured responses to rectangular pulse injections exploiting equilibrium theory

Knowledge of adsorption isotherms is essential for the design and optimization of chromatographic separation processes. Since the experimental determination of these thermodynamic functions is a complicated and time consuming task, there is a need to develop methods which are fast and easy to apply. An attractive group of methods is based on neglecting in the analysis of measured dynamic elution profiles all kinetic effects. These methods assume the validity of an isotherm model equation and exploit the possibility to solve analytically the column mass balance equations of the equilibrium model. If just the dispersive part of an elution profile is, the method is known as “elution by characteristic point” (ECP). The ECP method has been applied successfully to analyse column effluent profiles of single component dissolved in a mobile phase. This work extends the ECP method to analyse just the shapes of elution profiles recorded after injecting samples that contain two key components to be separated. The extended ECP method requires recording only one overloaded elution profile for the two-component mixture and offers a fast and efficient way to estimate isotherm model parameters. The method is in particular attractive if there is limited access to the pure components, as for example in cases of enantiomers. The underlying theory is presented and applied for the case that the adsorption equilibria can be described satisfactorily by the classical competitive Langmuir model. Core of the theory are the available analytical equations describing the outlet concentration profiles of the two solutes for the equilibrium controlled case the. Considering a case study, it is shown that the extended ECP method can be applied successfully for columns characterized by 2500 or more theoretical plates. However, the method can be useful also for columns with lower efficiency. It provides then a rough estimation regarding the isotherm courses.

Chromatographic Analysis of the Chemical Composition of Exhaust Gas Samples from Urban Two-Wheeled Vehicles

The subject of the article was the chemical analysis of gasoline and exhaust gas samples taken from an urban two-wheeled vehicle. The main aim of the work was to identify chemical compounds emitted by a group of urban two-wheeled vehicles depending on the engine’s operating parameters. First, engine operating parameters and driving parameters of three urban two-wheeled vehicles were measured in real operating conditions. Based on the averaged results, engine operating points were determined for exhaust gas samples that were collected into Tedlar bags. The exhaust gas composition of individual chemical substances obtained in the chromatographic separation process were subjected to a detailed analysis relating the engine operating point with their emission rate, with each individual component being assessed in terms of its impact on human health. The obtained qualitative analysis results indicated the presence of alkenes, alkanes, aliphatic aldehydes, and aromatic and cyclic hydrocarbons (cycloalkanes) in the tested samples. The experiments provided a variety of conclusions relating to the operating parameters of a two-wheeler engine. Qualitative assessment of exhaust samples showed that a two-wheeled vehicle was characterized by the most varying composition of BTX aromatic hydrocarbons derivatives, which are particularly dangerous to human health and life. Therefore, the authors suggest that in the future, approval procedures regarding toxic emissions should be extended to include chromatographic tests. The presented results are an extension of previous studies on toxic emissions from urban two-wheeled vehicles in real operating conditions that were published in other journals.

Thermodynamic and Kinetic Equilibrium for Adsorption of Cellulosic Xylose of Commercial Cation-Exchange Resins.

The development of low-cost purification technology is an indispensable need for industrial biorefinery. Xylose is easily obtained from hydrothermal pretreatment of lignocellulosic biomass. This current study emphasizes the chromatographic monosaccharide separation process using commercial cation-exchange resins (CER) including Amberlite 120 and Indion 225 to separate xylose from a mixture of hydrolysates. To understand the performance of the two CER, the studies of equilibrium, thermodynamics, and kinetics were evaluated. In this study, with different xylose concentrations, the adsorption equilibrium was found to follow the Freundlich isotherm model well (R2 > 0.90 for both CER). The results indicated that a pseudo-second-order model represented the xylose adsorption kinetics. In addition, the activation energy of xylose adsorption onto both CER, i.e., Amberlite 120 and Indion 225 was 34.9 and 87.1 kJ/mol, respectively. The present adsorption studies revealed the potential of these commercial CER to be employed as effective adsorbents for monosaccharide separation technology.

Model based process optimization of an industrial chromatographic process for separation of lactoferrin from bovine milk

Model based process development using predictive mechanistic models is a powerful tool for in-silico downstream process development. It allows to obtain a thorough understanding of the process reducing experimental effort. While in pharma industry, mechanistic modeling becomes more common in the last years, it is rarely applied in food industry. This case study investigates risk ranking and possible optimization of the industrial process of purifying lactoferrin from bovine milk using SP Sepharose Big Beads with a resin particle diameter of 200 µm, based on a minimal number of lab-scale experiments combining traditional scale-down experiments with mechanistic modeling. Depending on the location and season, process water pH and the composition of raw milk can vary, posing a challenge for highly efficient process development.A predictive model based on the general rate model with steric mass action binding, extended for pH dependence, was calibrated to describe the elution behavior of lactoferrin and main impurities. The gained model was evaluated against changes in flow rate, step elution conditions, and higher loading and showed excellent agreement with the observed experimental data. The model was then used to investigate the critical process parameters, such as water pH, conductivity of elution steps, and flow rate, on process performance and purity. It was found that the elution behavior of lactoferrin is relatively consistent over the pH range of 5.5 to 7.6, while the elution behavior of the main impurities varies greatly with elution pH. As a result, a significant loss in lactoferrin is unavoidable to achieve desired purities at pH levels below pH 6.0. Optimal process parameters were identified to reduce water and salt consumption and increase purity, depending on water pH and raw milk composition. The optimal conductivity for impurity removal in a low conductivity elution step was found to be 43 mS/cm, while a conductivity of 95 mS/cm leads to the lowest overall salt usage during lactoferrin elution. Further increasing the conductivity during lactoferrin elution can only slightly lower the elution volume thus can also lead to higher total salt usage. Low flow rates during elution of 0.2 column volume per minute are beneficial compared to higher flow rates of 1 column volume per minute.The, on lab-scale, calibrated model allows predicting elution volume and impurity removal for large-scale experiments in a commercial plant processing over 106 liters of milk per day. The successful model extrapolation was possible without recalibration or detailed knowledge of the manufacturing plant. This study therefore provides a possible pathway for rapid process development of chromatographic purification in the food industries combining traditional scale-down experiments with mechanistic modeling.

Factors Influencing the Prediction Accuracy of Model Peptides in Reversed-Phase Liquid Chromatography

Hydrophobicity is an important physicochemical property of peptides in solution. As well as being strongly associated with peptide stability and aggregation, hydrophobicity governs the solution based chromatographic separation processes, specifically reversed-phase liquid chromatography (RPLC). In addition, hydrophobicity is a major physicochemical property of peptides in comparison to H-bonding, electrostatic, and aromatic properties in intermolecular interactions. However, a wide range of molecular factors can influence peptide hydrophobicity, with accurate predictions depending on specific peptide amino acid compositions, structure, and conformation. It is noticeable that peptide composition, the position of the amino acid, and its neighbouring groups play a crucial role in the elution process. In light of this, the same amino acid behaved differently depending on its position and neighbouring amino acid in the peptide chain. Extra attention should be paid to the denaturation process during the course of elution, as it has been shown to complicate and alter the elution pattern. This paper reports on the key peptide properties that can alter hydrophobicity and, consequently, the RPLC elution behaviour of the peptides, and it will conclude by proposing improved prediction algorithms for peptide elution in RPLC.

Development of chromatographic process for the dynamic separation of 90Sr from high level liquid waste through breakthrough curve simulation and thermal analysis

• Numerical simulation on adsorption dynamics and temperature field was performed for scale-up column. • The heat values per unit adsorbent volume was calculated to be 2292.877 W/m 3 . • A preliminary design was developed for Sr(II) adsorption processing. 4′,4′(5′’)-Di( tert -butylcyclohexano)-18-crown-6 (DtBuCH18C6) modified with dodecyl benzenesulfonic acid (DBS) and 1-dodecanol was impregnated into silica-based polymer support (SiO 2 -P). The stability of adsorbent was evaluated according to thermal decomposition and leakage property. The leakage of total organic carbon (TOC) and dodecyl benzenesulfonic acid from the adsorbent was approximately constant and was below 2.26 wt% at 298 K at 0.5–7 M HNO 3 . The O 1s and Sr 3p XPS spectra of the adsorbent after adsorption of Sr(II), suggesting that strong binding interaction of Sr(II) and oxygen in DtBuCH18C6. The adsorbent containing DBS promoted the adsorption of Sr(II) compared to the DBS-free one, demonstrating the DBS worked as a counter ion. By adsorption kinetic and isotherm analyses, linear driving force expression with external mass coefficient ( K fa ) and Freundlich model was found to be appropriate for the accurate description of adsorption dynamics. The separation column was designed by numerical thermal analysis and simulating breakthrough curve based on the short column data. Mathematical adsorption dynamics model was built, thereby obtaining the breakthrough curve of scale-up column. Thermal analysis was conducted due to the large heat value per unit adsorbent volume of 2292.877 W/m 3 . The effect of column length and diameter on breakthrough curve and temperature field was further computationally discussed. Thus, a preliminary design for Sr(II) processing column (Φ150 mm × H700 mm, breakthrough time: 9.55 h, processing volume: 76.92 dm 3 /cycle) from high-level liquid waste was proposed.

Steroid, Triterpenoid and Phenylpropanoid From Stereospermum Suaveolens (Roxb.) DC Growing in Bangladesh

The study was carried out to investigate the composition of natural compounds of methanol extract of leaf and stem bark of Stereospermum suaveolens (Roxb.) DC by repeated chromatographic separation and purification processes over silica gel. The phytochemical investigation resulted in the isolation of a total of three compounds, which were identified as stigmasterol (1), β-amyrin (2) and 4-methoxycinnamic acid (3). The chemical structures of the isolated compounds were elucidated by analyses of their physical properties, acquired 1H NMR data and comparison with published reports. Among these, compounds 2 and 3 are first report of their isolation from this plant species. Bangladesh J. Bot. 50(4): 1191-1194, 2021 (December)

Optimization of coal tar gas chromatography conditions using probabilistic-deterministic design of experiment

The development of physicochemical methods for the analysis of objects of complex composition requires the application of methods of mathematical experiment design. This article investigates the possibility of using probabilistic-deterministic design of experiment (PDDoE) for obtaining a mathematical model of the chromatographic separation process of coal tar hydrogenation products on an Agilent 7890A device with an Rxi-5ms column. It is shown that the relationship between the column heating rate and the carrier gas pressure with the values averaged for the entire chromatogram can be established with a high accuracy. It is noted that the accuracy of modeling the individual characteristics of the mixture components’ peaks is less, but remains sufficient for many practical needs. Nonlinear multiple correlation coefficients (NMC) for the dependence of the average retention time and average intensity on the considered factors are more than 0.99; they are more than 0.98 for the average peak width. NMC for the dependence of the resolution with the relation to the peaks of naphthalene and 2-ethylphenol is more than 0.8 at a significant level that sufficient for practice. The quality of the mathematical model was checked by triple registration of the chromatogram at the values of the column heating rate and carrier gas pressure that were not used in the training experiment. The measurement results are excellent squared with those calculated using the obtained generalized equations. The PDDoE method can be recommended as a method for mathematical design of an optimization experiment in gas chromatography.

On the potential use of two-photon polymerization to 3D print chromatographic packed bed supports

The continuous quest for chromatographic supports offering kinetic performance properties superior to that of the packed bed of spheres has pushed the field to consider alternative formats such as for example monolithic and pillar array columns. This quest seems bound to culminate in the use of 3D printing technology, as this intrinsically offers the possibility to produce supports with a perfect uniformity and with a size and shape that is fully optimized for the chromatographic separation process. However, to be competitive with the current state-of-the-art, structures with sub-micron feature sizes are required. The present contribution therefore investigates the use of the 3D printing technology with the highest possible resolution available today, i.e., two-photon polymerization (2PP). It is shown that 2PP printing is capable of achieving the required ≤ 1 µm printing resolution. Depending on the laser scan speed, the lower limit through-pore size for a tetrahedral skeleton monolith with a theoretical 80% external porosity was found to be at 800 nm, when printing at a scan speed of 50 mm/s with a laser power of 10%. For a scan speed of 10 mm/s, the minimal through-pore size dropped to 500 nm. However, this very high resolution comes at the cost of excessively long printing times. The total printing time for a column volume equivalent to that of a typical nano-LC column (75 µm i.d. cylindrical tube with length L = 15 cm) has been determined to correspond to 330 and 470 h for the 50 mm/s and the 10 mm/s scan speed respectively. Other issues remaining to be solved are the need to clad the printed skeleton with a suitable mesoporous layer for chromatographic retention and the need to add a top-wall to the printed channels after the removal of the non-polymerized resin. It is therefore concluded that 2PP printing is not ready yet to replace the existing column fabrication methods.

Adaptive Soft-Sensor Modeling of SMB Chromatographic Separation Process Based on Dynamic Fuzzy Neural Network and Moving Window Strategy

Adaptive Soft-Sensor Modeling of SMB Chromatographic Separation Process Based on Dynamic Fuzzy Neural Network and Moving Window Strategy

ANFIS soft sensing model of SMB chromatographic separation process based on new adaptive population evolution particle swarm optimization algorithm

Aiming at predicting the purity of the extract and raffinate components in the simulated moving bed (SMB) chromatographic separation process, a soft-sensor modeling method was proposed by adoptig the hybrid learning algorithm based on an improved particle swarm optimization (PSO) algorithm and the least means squares (LMS) method to optimize the adaptive neural fuzzy inference system (ANFIS) parameters. The hybrid learning algorithm includes a premise parameter learning phase and a conclusion parameter learning phase. In the premise parameter learning stage, the input data space division of the SMB chromatographic separation process and the initialization of the premise parameters are realized based on the fuzzy C-means (FCM) clustering algorithm. Then, the improved PSO algorithm is used to calculate the excitation intensity and normalized excitation intensity of all the rules for each individual in the population. In the conclusion parameter learning phase, these linear parameters are identified by the LMS method. In order to improve population diversity and convergence accuracy, the population evolution rate function was defined. According to the relationship between population diversity, population fitness function and particle position change, a new adaptive population evolution particle swarm optimization (NAPEPSO) algorithm was proposed. The inertia weight is adaptively adjusted according to the evolution of the population and the change of the particle position, thereby improving the diversity of the particle swarm and the ability of the algorithm to jump out of the local optimal solution. The simulation results show that the proposed soft-sensor model can effectively predict the key economic and technical indicators of the SMB chromatographic separation process so as to meet the real-time and efficient operation of the SMB chromatographic separation process.

New models involving quantum chemical parameters for assessing the chromatographic retention process

Knowledge about the theoretical relationship between the analyte properties and the critical chromatographic parameters is mandatory for a better interpretation of the separation mechanism and a more leisurely development of quantitative studies. In a preliminary stage of this work, we introduce the Gumbel distribution, the extreme value distribution type-I widely used in other fields, as a novel tool for modelling the chromatographic peak shape. Further, we develop mathematical models to evaluate the effect of the experimental variables and various quantum parameters on the chromatographic indices, such as the retention time, capacity factor, asymmetry factor, tailing factor and number of theoretical plates. Finally, we propose a mechanistic behaviour for the chromatographic separation process based on the structure-retention relationship of fifteen selected drugs involving several molecular quantum parameters.

Dihydroxyacetone Production: From Glycerol Catalytic Oxidation with Commercial Catalysts to Chromatographic Separation

A process for catalytic production of dihydroxyacetone through glycerol oxidation and dihydroxyacetone purification by chromatographic processes is herein presented. Glycerol oxidation over commercial Pt/AC and Pt–Bi/AC catalysts was performed on a batch reactor operating under base-free conditions, using water as a solvent and oxygen as the oxidant agent. The highest dihydroxyacetone yield obtained was 36%, using the Pt5%-Bi1.5%/AC catalyst, whereas the monometallic Pt/AC catalyst was selective to glyceric acid. It is herein reported for the first time dihydroxyacetone separation from unreacted glycerol and reaction byproducts (organic acids). Two commercial polystyrene-divinylbenzene ion-exchange resins were used as a stationary phase, one in the hydrogen form and the other in the calcium form, and water was used as the mobile phase. Adsorption equilibrium data for the species present in the reaction mixture were determined in both resins through single-component adsorption breakthrough experiments. A continuous chromatographic separation process was designed based on the simulated moving bed (SMB) technology and simulated using the gPROMS model builder software. A two SMB cascade was then considered, performing the pseudobinary mixture separation on the first SMB. The extract stream from the first SMB containing dihydroxyacetone and glycerol was fed to the second SMB, obtaining dihydroxyacetone with a purity of 97%. Considering the overall unit, a dihydroxyacetone productivity of 64 kgDHA (LAds day)−1 was obtained with an eluent consumption of 1218 LDes kgDHA–1.

Unified design of chromatographic separation processes: Partial separations and waste fractions

Chromatographic batch and simulated moving-bed (SMB) processes where products are collected in less than 100% purity, or part of the effluent is discarded as waste, were analyzed. The Unified Design method was used to visualize the regions of feasible operating parameters under ideal and non-ideal conditions. Binary separations with linear and Langmuir isotherms were considered. The feasible regions of batch chromatography and the classical four-zone SMB are identical for partial separations under ideal conditions. For Langmuir isotherms, the feasible region of an ideal batch column is an excellent approximation for an efficient SMB process when purity constraints are low. Chromatographic processes with waste fractions are represented by two operating points on the Unified Design operating parameter plane. In contrast to batch chromatography, introducing an additional waste stream to an SMB process was found to not improve its performance.

Experimental application of individual column state and parameter estimation in SMB processes to an amino acid separation

Abstract The simulated moving bed (SMB) process is a highly efficient continuous chromatographic separation process. Due to its hybrid process dynamics that lead to discontinuities and sharp fronts on the state trajectories, optimal SMB process operation is challenging. Process performance can be improved by applying model-based optimizing control methods. For this, online information about states and individual column parameters are required. The strategy for simultaneous state and parameter estimation used here exploits the switching nature of the SMB process. The successful experimental application of the strategy is demonstrated for the continuous separation of two amino acids on an SMB pilot plant where extra-column equipment effects need to be considered.