Control Of Crystallization Process Research Articles

IoT-driven reflectance-based multimode colorimeter for real-time monitoring of crystallization process: A study on oleogels

This study explores the application of an Internet of Things (IoT)-driven reflectance-based multimode colorimeter for real-time monitoring of the crystallization process in oleogels-a novel class of structured lipids gaining popularity in the food industries. These structured lipids offer a healthier alternative to solid fats, but their texture and stability rely on precise control of crystallization process. Traditional monitoring methods, such as atomic force microscopy and spectroscopy, are expensive and lack real-time capabilities. The proposed device can operate in two modes: quality testing and process monitoring modes. In the quality testing mode, the device exhibits superior color accuracy compared to a commercial device, making it a reliable tool for color assessment (ΔE values < 10). In the process monitoring mode, the device effectively tracks crystallization kinetics at different incubation temperatures (5 °C, 15 °C, and 25 °C), revealing the impact of sunflower lecithin on primary and secondary crystallization phases. Further, the temperature vs. L* data offers more profound insights into oleogel crystallization, validated by Differential Scanning Calorimetry (DSC) analysis. Additionally, the device's performance was tested by monitoring the crystallization process of butter. The results obtained from the device closely matched the DSC findings, which enhanced our understanding of the crystallization processes in butter. This showcases the potential of the device for analyzing food samples.

Recent Advances in the Application of Machine Learning to Crystal Behavior and Crystallization Process Control

Recent Advances in the Application of Machine Learning to Crystal Behavior and Crystallization Process Control

Design of Crystal Growth Dimensionality in Synthetic Wax: The Kinetics of Nonisothermal Crystallization Processes.

The demand for the development of multifunctional materials in emerging technologies has stimulated intensive research on the control of crystallization processes in numerous scientific and engineering fields. In this article, we examine the kinetics of nonisothermal melt crystallization in synthetic wax using differential scanning calorimetry (DSC) supported by polarized optical microscopy (POM) to describe crystallization modes in a multicomponent molecular system. We detected the macroscopic growth of three crystal phases and the formation of two crystal phases as a transformation from a disordered crystal mesophase into an ordered crystal. To characterize individual crystal phase formation, we examine the activation energy evaluated by isoconversional analysis and utilize the Ozawa and Mo methods to determine the kinetic details of the crystal growth from the isotropic phase. Our investigation reveals the possibility of the design of crystal growth dimensionality as three-dimensional spherulitic-like, two-dimensional rodlike, and one-dimensional needle-shaped crystal forms of shorter n-alkanes by controlling the solidification pathway of long-chain n-alkanes and the interplay of the thermodynamic and kinetic mechanisms of crystallization.

Unsupervised crystallization monitoring with NIR Spatially Resolved Spectroscopy combined with Principal Component Analysis

ATR-FTIR and NIR spectroscopies are widely deployed to monitor the liquid phase composition during crystallization. However, information about the solid phase is also essential for the crystallization process control. We propose in this study to implement Spatially Resolved Spectroscopy (SRS) based on the near-infrared spectral range, to collect relevant data related to both physical and chemical aspects of suspensions during seeded cooling crystallizations of an industrial organic compound. Different batch crystallizations with varying experimental conditions were carried out and monitored. Principal Component Analysis (PCA) was applied and score trajectories were analyzed over time. This data mining method enabled to differentiate batches according to their initial composition and to detect a secondary nucleation mechanism.

Estimation of pressure effect on the crystallization process control based on a generalized theoretical model of a crystallizing metal

It is installed, that on aspects such as automation and control, liquid and/or crystallizing metal is a complex dynamic object, whose properties correspond to rheological body properties. Conclusions were made that the casting structure formation during crystallization proceeds under the influence of two main factors — heat removal to the mold wall and metallostatic pressure. Theoretical studies shown that the deformation of cast metal is carried out in order to correct defects but does not give this metal greater strength. It is established, by using the results of computational and theoretical studies that for efficient control of the crystallization process, it is necessary to calculate the solidification rate, and crystallization control can be reduced to control the metastable state interval. Keywords aluminum alloys, control, pressure, crystallization theory, mathematical model, heat flow, cooling rate

Online learning‐based predictive control of crystallization processes under batch‐to‐batch parametric drift

AbstractThis work considers a seeded fesoterodine fumarate (FF) cooling crystallization and presents the methodology and implementation of a real‐time machine learning modeling‐based predictive controller to handle batch‐to‐batch (B2B) parametric drift. Specifically, an autoencoder recurrent neural network‐based model predictive controller (AERNN‐MPC) is developed to optimize product yield, crystal size, and energy consumption while accounting for the physical constraints on cooling jacket temperature. Deviations in the kinetic parameters are considered in the closed‐loop simulations to account for the B2B parametric drift, and two error‐triggered online update mechanisms are proposed to address issues pertaining to the availability of real‐time crystal property measurements and are incorporated into the AERNN‐MPC to improve the model prediction accuracy. Closed‐loop simulation results demonstrate that the proposed AERNN‐MPC with online update, irrespective of the accessibility to real‐time crystal property data, achieves a desired closed‐loop performance in terms of maximizing product yield and minimizing energy consumption.

Engineering nucleation/crystallization to intensify the enzymatic reactions and fermentation: A review

Bioprocess such as enzymatic reaction and fermentation have experienced fast development as green and sustainable techniques, which can integrate with different separating unit operations for better performance, such as crystallization. Here we reviewed the application of crystallization in intensifying enzymatic reactions and fermentation process in the perspective of nucleation implementation and crystallization process control. Since the properties of free enzyme, immobilized enzyme, catalytic cell, microorganism etc. were different, the accomplishment of crystallization was not unique. Specifically, temperature difference, seeds, crystallization agents, electric field, co-crystal formers etc. have been used to induce nucleation/crystallization in bioprocess. How these methods can be effectively integrated with enzymatic reactions and fermentation was our original concern. Thus various ways to induce nucleation or maintain supersaturation were discussed in representative examples. The hybrid process design and control were emphasized, and the key operating parameters were highlighted.

Molecular design and crystallization process control for thin sheet-shaped organic semiconductor crystals with two-dimensional packing

A macroscopic morphology regulation from rod- to sheet-shaped crystals with the same single structure was realized via building a two-dimensional packed network in plane for organic semiconductors.

Generating Assembled MFI Nanocrystals with Reduced b-Axis through Structure-Directing Agent Exchange Induced Recrystallization.

Controlling crystal size and shape of zeolitic materials is an effective way to promote their mass transport and catalytic properties. Herein, we report a single step, Na+ - and porogen- free crystallization of MFI hierarchical architecture made up of aligned nanocrystals with reduced b-axis thickness (5-23 nm) and adjustable Si/Al ratios between 35 to 120, employing the commonly used tetrapropylammonium hydroxide (TPAOH) and tetrabutylammonium hydroxide (TBAOH) as structure-directing agents (SDAs). Homogeneous nucleation driven by both SDAs and subsequent SDA-exchange induced dissolution-recrystallization are responsible for the formation of such structure. The enhanced textural and diffusion properties account for a notable exaggeration of propene selectivity and catalyst lifetime in dimethyl ether-to-olefins (DTO) conversion. This protocol is extendable to the rational synthesis of other hierarchical zeolites through crystallization process control.

Generating Assembled MFI Nanocrystals with Reduced b‐Axis through Structure‐Directing Agent Exchange Induced Recrystallization

AbstractControlling crystal size and shape of zeolitic materials is an effective way to promote their mass transport and catalytic properties. Herein, we report a single step, Na+‐ and porogen‐ free crystallization of MFI hierarchical architecture made up of aligned nanocrystals with reduced b‐axis thickness (5–23 nm) and adjustable Si/Al ratios between 35 to 120, employing the commonly used tetrapropylammonium hydroxide (TPAOH) and tetrabutylammonium hydroxide (TBAOH) as structure‐directing agents (SDAs). Homogeneous nucleation driven by both SDAs and subsequent SDA‐exchange induced dissolution‐recrystallization are responsible for the formation of such structure. The enhanced textural and diffusion properties account for a notable exaggeration of propene selectivity and catalyst lifetime in dimethyl ether‐to‐olefins (DTO) conversion. This protocol is extendable to the rational synthesis of other hierarchical zeolites through crystallization process control.

Application of PAT-Based Feedback Control Approaches in Pharmaceutical Crystallization

Crystallization is one of the important unit operations for the separation and purification of solid products in the chemical, pharmaceutical, and pesticide industries, especially for realizing high-end, high-value solid products. The precise control of the solution crystallization process determines the polymorph, crystal shape, size, and size distribution of the crystal product, which is of great significance to improve product quality and production efficiency. In order to develop the crystallization process in a scientific method that is based on process parameters and data, process analysis technology (PAT) has become an important enabling platform. In this paper, we review the development of PAT in the field of crystallization in recent years. Based on the current research status of drug crystallization process control, the monitoring methods and control strategies of feedback control in the crystallization process were systematically summarized. The focus is on the application of model-free feedback control strategies based on the solution and solid information collected by various online monitoring equipment in product engineering, including improving particle size distribution, achieving polymorphic control, and improving purity. In this paper, the challenges of feedback control strategy in the crystallization process are also discussed, and the development trend of the feedback control strategy has been prospected.

Synthesis of divalent metal-silicalite MEL zeolites as efficient bi-functional adsorbents/catalysts for non-methane hydrocarbon in cooking oil fumes elimination

Divalent metallic ions (M2+ = Fe2+, Mn2+, Cu2+) have been successfully introduced into silicalite-2 via a crystallization process control method. When applied in Non-methane Hydrocarbon (NMHC) removal from cooking oil fumes, these synthesized M-S2 (M = Fe, Mn, Cu) samples present both higher adsorption capacity and good catalytic oxidation performance compared with Al-S2 composed of silicon and aluminum. XRD, FTIR, UV–Vis, SEM, STEM, N2 sorption, TG-DTA, 29Si MAS NMR and XPS were used to characterize the incorporation and dispersion of different divalent metallic ions in silicalite framework. It was found Fe2+ can be well incorporated into the zeolite framework as similarly as Al3+, then followed by Mn2+ and Cu2+, respectively. Mn2+ can exist in the form of extra-framework oxidized metallic species despite of the framework Mn species, while Cu species are mainly in the form of CuOx nanoparticles or clusters. Due to lacking SiOHAl, these divalent metal-MEL zeolites possess superior hydrophobicity compared with Al-MEL and this endows the former’s superior NMHC adsorption performance from cooking oil fumes. Moreover, owing to possess active metallic species with variable valence states, these divalent metal-MEL zeolites display good oxidation ability to VOCs. Particularly, Mn-S2 displays the best catalytic activity for NMHC, which can be attributed to both the good dispersion and the various valence states of Mn species in silicalite.

Crystal engineering of hierarchical zeolite in dynamically maintained Pickering emulsion

Crystal engineering of hierarchical zeolites is regarded as a promising way to enhance diffusion-dependent catalytic properties of zeolitic materials. Crystallization process control on crystal size and pore-structure is desirable over porogen based protocols and post-synthetic methods for the low cost, high yield and potential scalability. Herein, a tumbling crystallization of hierarchical ZSM-5 zeolite in immiscible water/toluene Pickering emulsion inspired by energy dissipating structure occurring in nature is presented. The structure and acid properties of the obtained hierarchical material have been revealed using a panoply of characterization techniques such as powder X-ray diffraction, N2 physisorption isotherms, SEM, TEM, mercury protrusion measurements, NH3-TPD and pyridine IR spectroscopy, showing that the material contains high crystallinity, and penetrating macropores. Crystallization is found to proceed through a Pickering emulsion structure maintained by emulsifying effect of constant tumbling. Such an emulsion structure has hindered attachment growth of primary nanocrystals formed at the nucleation stage to further grow into larger size via coalescense. The hierarchical zeolite exhibits architecture-dependent prolonged catalyst lifetime and light olefin yield in dimethylether-to-olefin conversion. This process control to generate hierarchical zeolites opens up new ways toward inexpensive, high level control and efficient engineering of zeolite morphology.

Improvement of Pockels effect by crystallization-process control in nonlinear-optical glass-ceramics

Abstract We have fabricated perfectly-surface-crystallized glass-ceramics (PSC-GCs) consisting of polar frenoite-type Sr 2 TiSi 2 O 8 by different crystallization-process conditions, i.e., isothermal treatment at 880 °C for 72 h and 960 °C for 1 h, and have examined their Pockels effect in this study. The PSC-GCs obtained by the lower-temperature process exhibited the Pockels coefficients, i.e., r 13 ∼ 3.0 pm/V and r 33 ∼ 2.4 pm/V, and the coefficients exceeded the reported values of the PSC-GCs with Sr 2 TiSi 2 O 8 phase. The improvement of Pockels effect is attributed to the higher crystal-orientation, which is provided by the lower-temperature condition.

Revisiting KAI theory and its application to mixed composite system “–PVA” fabricated at moderate elevated temperature

Ishibashi and Takagi considered ‘ferroelectric domain switching’ just equivalent to ‘phase evolution during crystal growth’. They slightly modified Kolmogorov and Avrami’s classical theory of ‘phase transformations in crystal growth’ and applied it to the switching data in ferroelectrics. Here an effort is made to elucidate mathematical steps in evolution of this new KAI model used to fit experimental switching data to estimate microscopic switching parameters. In mixed composite system (x = 0.09), the domain growth dimension () is found to expand from to when signal amplitude changed from to The cesium substitution is supposed to have enabled lone pairs on central nitrogen of nitrite group to orient constituting branches into more ordered state in the matrix of molecular chains of the PVA facilitating control of crystallization process which led to lowering of strain giving rise to the increased change in with change in pulse amplitude.

Systematic model identification and optimization-based active polymorphic control of crystallization processes

Polymorphism is an important issue in industrial crystallization, since polymorphs of the same compound can present very different properties, such as solubility, melting point or density, influencing considerably the manufacturability and bioavailability of the final product.This work proposes a model-based active polymorphic control strategy that allows obtaining large crystals of the stable polymorph at the end of a batch crystallization process, even in the case of erroneous seeding or in situ nucleation of a mixture of both the stable and metastable forms. A novel systematic experimental design was applied to estimate the kinetic parameters of dissolution, growth and secondary nucleation of the stable and metastable polymorphs of the model compound (ortho-aminobenzoic acid, OABA). Such experimental approach allows the determination of the studied kinetics without any correlation between parameters during the estimation, and without the need of off-line measurements of the crystal size distribution during the experiments.The estimated kinetic parameters were used to build a population balance model for the calculation of the optimal temperature profile needed, during a batch cooling crystallization process, for the (i) elimination of the metastable form crystals nucleated in situ or erroneously seeded and the (ii) maximisation of the size of the crystals of the stable polymorph obtained at the end of the batch process.

Modelling and control of crystallization process

Batch crystallizers are predominantly used in chemical industries like pharmaceuticals, food industries and specialty chemicals. The nonlinearnature of the batch process leads to difficulties when the objective is to obtain a uniform Crystal Size Distribution (CSD). In this study, a linearPI controller is designed using classical controller tuning methods for controlling the crystallizer outlet temperature by manipulating the inletjacket temperature; however, the response is not satisfactory. A simple PID controller cannot guarantee a satisfactory response that is why anoptimal controller is designed to keep the concentration and temperature in a range that suits our needs. Any typical process operation hasconstraints on states, inputs and outputs. So, a nonlinear process needs to be operated satisfying the constraints. Hence, a nonlinear controller likeGeneric Model Controller (GMC) which is similar in structure to the PI controller is implemented. It minimizes the derivative of the squared error,thus improving the output response of the process. Minimization of crystal size variation is considered as an objective function in this study. Modelpredictive control is also designed that uses advanced optimization algorithm to minimize the error while linearizing the process. Constraints arefed into the MPC toolbox in MATLAB and Prediction, Control horizons and Performance weights are tuned using Sridhar and Cooper Method.Performances of all the three controllers (PID, GMC and MPC) are compared and it is found that MPC is the most superior one in terms of settlingtime and percentage overshoot.

Modelling and control of crystallization process

Abstract Batch crystallizers are predominantly used in chemical industries like pharmaceuticals, food industries and specialty chemicals. The nonlinear nature of the batch process leads to difficulties when the objective is to obtain a uniform Crystal Size Distribution (CSD). In this study, a linear PI controller is designed using classical controller tuning methods for controlling the crystallizer outlet temperature by manipulating the inlet jacket temperature; however, the response is not satisfactory. A simple PID controller cannot guarantee a satisfactory response that is why an optimal controller is designed to keep the concentration and temperature in a range that suits our needs. Any typical process operation has constraints on states, inputs and outputs. So, a nonlinear process needs to be operated satisfying the constraints. Hence, a nonlinear controller like Generic Model Controller (GMC) which is similar in structure to the PI controller is implemented. It minimizes the derivative of the squared error, thus improving the output response of the process. Minimization of crystal size variation is considered as an objective function in this study. Model predictive control is also designed that uses advanced optimization algorithm to minimize the error while linearizing the process. Constraints are fed into the MPC toolbox in MATLAB and Prediction, Control horizons and Performance weights are tuned using Sridhar and Cooper Method. Performances of all the three controllers (PID, GMC and MPC) are compared and it is found that MPC is the most superior one in terms of settling time and percentage overshoot.

Unraveling the non-classic crystallization of SAPO-34 in a dry gel system towards controlling meso-structure with the assistance of growth inhibitor: Growth mechanism, hierarchical structure control and catalytic properties

Understanding silicoaluminophosphate formation mechanism lays the foundation for their structure manipulation via crystallization process control. Crystallization of SAPO-34 from a dry gel using tetraethyl ammonium hydroxide as structure-directing agent was monitored to unravel the formation mechanism. The initial gel was found to form a lamellar precursor first, which subsequently underwent phase transformation to discrete SAPO-34 nanocrystallites. The nanocrystallites thereafter mutually aligned with neighboring ones via a non-classic oriented attachment growth mechanism, affording large crystals as a result of grain boundary elimination. A new protocol to prepare hierarchical SAPO-34 was designed by hindering the aggregation of primary nanocrystallites with a growth inhibitor 1,2,3-hexanetriol. The structure of hierarchical SAPO-34 was characterized by XRD, N2 physisorption, mercury intrusion, SEM, TEM, as well as 27Al, 29Si, 31P MAS NMR spectra and compared with a conventional SAPO-34. More Si islands were formed via combined SM3 (Al+P pairs substitution by 2Si) and SM2 (P substitution by Si) mechanism for hierarchical SAPO-34 as Si was not fully incorporated into the precursor lamellar phase. NH3-TPD showed that hierarchical SAPO-34 has comparable acidic strength to conventional SAPO-34. The obtained hierarchical SAPO-34 is comprised of <100 nm crystallites and possesses well-connected mesopores, both factors are crucial to mass transfer in zeotype materials. Hierarchical SAPO-34 exhibited a 1.5 times lifetime increase in catalytic chloromethane to olefin conversion with respect to a conventional counterpart.

Установка для выращивания кристаллов белков в земных и космических условиях с активным управлением процессом кристаллизации

Установка для выращивания кристаллов белков в земных и космических условиях с активным управлением процессом кристаллизации