In situ particle size estimation for crystallization processes by multivariate image analysis

Model-based object recognition to measure crystal size and shape distributions from in situ video images

Multi-way methods in image analysis—relationships and applications

The quality of radish roots depends largely on its cultivar, production environment, and postharvest management along the supply chain. Quality monitoring of fresh products is of utmost importance during the postharvest period. The purpose of this study is to nondestructively estimate the weight of a radish using random forests based on color and shape information obtained from images, as well as volumetric information obtained by analyzing a point cloud obtained by combining multiple forms of shape information. The explanatory variables were color and shape information obtained through an image analysis of still images of radishes captured in a constructed photographic environment. The volume information was calculated from the bounding box and convex hull applied to the point cloud by combining the shape information obtained from the image analysis. We then applied random forests to relate the radish weight to the explanatory variables. The experimental results showed that the models using color, shape, or volume information all exhibited good performance with a Pearson’s correlation coefficient (COR) ≥ 0.80, suggesting the potential of nondestructive monitoring of radish weight based on color, shape, and volume information. Specifically, the model using volume information showed very high performance, with a COR of 0.95 or higher.

Membrane assisted crystallization of active pharmaceutical ingredients (APIs) by forward osmosis and its effect on crystal size and morphology

A crystal is defined as a solid material that is inclusive of the molecules, atoms, and ions that are arranged in an orderly fashion and a repetitive pattern in a spatial arrangement. Crystal growth is a major step in the process of crystallization. The process of crystal growth and kinetics is a dynamic process that is impacted by several factors. Crystals of a compound are a form of solid state that is one the purest form of the solid. The crystals are produced through the process of crystallization that is inclusive of two primary processes. The first process is nucleation, and the second process is crystal growth. In low supersaturation or desupersaturation, crystals are grown and accelerated with nucleation. This results in larger crystal size and distribution. A supersaturated solution is present in thermodynamic equilibrium with the solid phase at a specified temperature. The state of supersaturation is critical for the operation of the crystallization experiment. The crystallization process in the pharmaceutical sciences also helps in improved downstream processes that include filtration, milling and drying. These applications demand a crystallization of even more complicated molecules that can be used as drugs and duplication at a large scale. This paper discusses the process of crystallization of paracetamol in the solvent, isopropanol. The developed crystals have been studied in this thesis by application of the process supersaturation. The developed crystals have also been analyzed by using the PAT tools. The bioavailability of the compounds is essentially dependent on three factors that include solubility, permeability, and dosage. Based on the solubility of the compounds, the physiological pH value and permeability parameters of drugs are classified into several categories. These properties are altered and are impacted by the process of crystallization and crystal growth in the chemical setting. Paracetamol has three polymorphs with the form I being most stable and form II being the least stable. The process of crystal growth and crystallization focuses on the inclusion of the development of the most stable crystal polymorph to be used as a drug and to be mass-produced and applied in the pharmaceutical industry. The crystal data of the molecule reveals that the crystal growth of the molecule is subject to change with alterations in temperature. The bond length and bond angles for the crystal polymorphs of the paracetamol remain unchanged. The optimized geometry for a free molecule crystal is planar, where the phenyl rings and the acetamide lie in different planes. Bulk crystallization of paracetamol has been done as per the controlled conditions in clinical conditions for saturation experiments. The induction and the nucleation time in the process are determined by the changes in the concentration of the solute in the process of crystal growth. The products of the process are microcrystals and are characterized for the strain, shape, and defect in the crystals using diffraction and microscopic studies. Through this thesis, the researchers aim to establish a method of crystal formation for paracetamol through the process of supersaturation and using isopropanol as a solvent with growth trajectory by second-order growth kinetics. This will help in the development of paracetamol crystals that are highly pure and can be used for replication in the medical industry for drug production and development.

In several process industries including mineral processing, where the materials are solids or slurries, some important measurements cannot be obtained using standard instrumentation (e.g., flow, temperature, pressure, pH, power draw, etc.), but can be visually appraised, and could be automatically quantified using machine vision techniques. In general, the information to extract from process images is not well defined and is stochastic in nature compared with those typically encountered in the manufacturing industry for automatic inspection, which have well defined deterministic features. Multivariate image analysis (MIA) as well as Multiresolution analysis (MRA) have been shown to be very efficient methods for spectral/textural analysis of process images. The objective of this chapter is to illustrate these methods using three mineral processing problems: (1) on-line estimation of froth grade in a flotation process; (2) flotation froth health monitoring for appropriate reagent dosage; and (3) on-line estimation of run-of-mine ore lithotype composition on conveyor belts. In all cases, the extracted image information could be used for developing new vision sensors for advanced control of mineral processing plants.

Model Based Estimation of 2D Crystallization Kinetics From Concentration and CLD Measurements

Crystal size distribution (CSD) is an essential criterion for determining the production of high-quality crystals since it influences the efficiency of the crystallization process. Producing specified CSD in the crystallization process represents a main challenge as it depends on temperature control, which indirectly regulates the solution’s concentration and affects the crystal’s evolution. Different temperature profiles may influence the distribution of crystal products, and a suitable optimization algorithm is required to produce an optimum temperature trajectory that produces the desired CSD. Thus, this study aims to maximize the CSD of the grown seed crystals while minimizing the nucleus-grown crystals by employing the best optimization algorithm for the potash alum crystallization process. The crystallization process was developed and simulated in Matlab software using a potash alum in the water system. Four optimization algorithms were proposed with different objective functions, such as maximizing mean crystal size (I), minimizing coefficient of variation (II), minimizing nucleus-grown crystals (III), and maximizing CSD (IV). Based on the simulation results, optimization IV, which maximizes CSD, performs best with a large mean crystal size of 490 µm. Furthermore, the number of fine crystals was among the lowest at a volume distribution of 0.00071 m3/m compared to the linear profile at 0.00191 m3/m. Optimization IV employs a dissolution strategy, which manipulates two quality specifications in one algorithm (size of crystals and number of fines), which is considered the best optimal cooling profile for seeded batch crystallization by maximizing CSD and minimizing the generation of nucleus-grown crystals.

The temperature profile is a critical parameter in industrial batch cooling crystallization processes which directly impacts the crystal size distribution (CSD) and subsequent down streaming operations. Conventional cooling approaches, including natural and linear cooling, often fail to achieve optimal results. Linear cooling may trigger excessive primary nucleation by crossing metastable limits, while natural cooling leads to inconsistent nucleation and growth, resulting in smaller and non-uniform crystals. Hence, optimized cooling profiles are used to achieve the desired target CSD. This study employs reinforcement learning (RL) to optimize the temperature profile for batch cooling crystallization for two systems: paracetamol in water and potassium dihydrogen phosphate (KDP) in aqueous solution. RL dynamically adjusts the cooling trajectory by iteratively interacting with a process model, aiming to maximize mean crystal size while minimizing the coefficient of variation. A comprehensive mathematical model incorporating population balance equations, mass balance, and energy balance is developed to simulate the crystallization process. For the paracetamol system, the RL-optimization strategy resulted in a 107.5 % increase in mean crystal size compared to the natural cooling profile. In the case of the KDP system, a 12 % increase in mean crystal length is achieved relative to a linear cooling profile, along with a significant reduction in the coefficient of variation, indicating improved crystal size uniformity. The optimization results obtained using RL are also compared with that from a genetic algorithm for both cases, and RL demonstrated superior performance. This work underscores the potential of RL in advancing broader applications in chemical process optimization.

Image analysis based crystal size distribution (CSD) estimation can be very useful in the monitoring and control of crystallization process. Estimation of CSD from particle vision and measurement images is based on the characteristic length of the crystal particle alone. The estimation of the crystal width along with the length can improve the identification of the size and shape of crystals. The current work is an extended version of our multi-objective optimization based image analysis methodology, which measured only the characteristic length. In this work, we include the estimation of the crystal width as well. Also, the previous work was based on the assumption that the crystal particles align parallel to the surface of the solution due to the shear force, which will hold good only in ideal situations. Therefore, in this work, the effect of particle orientation along the depth of the solution on CSD estimation is studied. A modification factor has been proposed for particle orientation less than 10° so that a more accurate CSD can be estimated.

Comparative study of different approaches for multivariate image analysis in HPTLC fingerprinting of natural products such as plant resin

An Improved Multivariate Image Analysis Method for Quality Control of Nanofiber Membranes

In this paper, an image measurement method using a high-speed imaging system is proposed for the evolution of crystal population sizes during cooling crystallization processes. Firstly, to resist the negative effect from solution stirring and particle motion during crystallization, a U-net network-based image processing method is established to efficiently detect sufficiently clear crystals from the online captured microscopic images. Accordingly, the crystal size distribution model is analyzed in terms of the counted probability densities of these crystal images. Subsequently, a measurement method of size growth rate based on crystal population distribution is proposed to estimate the growth condition. An experimental case on a crystallization process of β-form LGA is used to show the effectiveness of the proposed strategy.

The effect of dissolution of carbon dioxide (CO2) in lactose solution on the particle size, morphology, yield and densities of crystallized alpha-lactose monohydrate (α-LM) was investigated. Dry ice was added into a 50% w/w aqueous lactose solution (relative supersaturation SR = 4.55) at 0, 500 and 1000 ppm in batch mode and closed system at 40°C. Agitation and sonication then were applied to the carbonated aqueous lactose solution for creating CO2 bubbles to assist the nucleation process. For ultrasound (US) treatment, the aqueous lactose solution was sonicated at 50% amplitude for 1 min. All samples (CO2 + agitation and CO2 + US + agitation) were then agitated for 3 h at 25°C upon crystallisation process. Regardless of mechanical treatment, prior addition of CO2 at higher concentration tended to induce formation of smaller crystals with triangular and trapezoidal-shaped morphologies whilst non-carbonated lactose favoured generation of bigger tomahawk-shaped crystals. A remarkable increase in α-LM yield was found in 1000 ppm carbonated sample prior to sonication as against non-carbonated counterpart. The crystal size d(0.5) was also found significantly lower (~32 μm) for the carbonated sample as compared to those of non-carbonated sample (~56 μm). The bulk and true densities of α-LM powder obtained were independent on CO2 concentration added but addition of 1000 ppm CO2 affected the packed densities of α-LM. Carbonation of lactose solution can be considered as an additional tool to manipulate crystal size, shape and yield of α-LM that may suitable for pharmaceutical and food applications.

Solubility phase diagram which consists of information on solute concentration, metastable and saturation limits against temperature, provides helpful insights in designing crystallization process to achieve desired crystal size distribution (CSD). Usually, the design of cooling crystallization process involves high supersaturation level at the beginning of the process in the metastable zone that is bounded by metastable and saturation limits. However, this high level of supersaturation causes an increment in both nucleation and crystal growth rates which induce the growth of the seed crystals as well as unwanted secondary nucleation that produce excessive fine crystals. Mitigation by employing proper temperature trajectory or policy along metastable zone to avoid unnecessary long operational time and fine crystals is needed. Thus, the purpose of this paper is to develop and simulate mathematical model of seeded batch crystallization process for the case of potash alum which is mainly used for purification in water treatment. Dynamic response of such process under open-loop operation for three cooling policies which are natural, linear and cubic is performed for evaluating the effect of different cooling policies on CSD. Simulation results based on the dynamic behaviour for all three cooling policies show cubic cooling policy obtained the best performance by achieving mean crystal size of 420 µm from the targeted CSD at 430 µm, and the mean crystal size for fine crystals is the lowest which is 35 µm compared to linear and natural cooling policies, at 40 µm and 55 µm, respectively. This information is prominent in deciding proper temperature trajectory of optimal cooling policy for potash alum crystallization process.