I’ve never seen a piece of equipment that didn’t do what it was supposed to. That doesn’t mean it did what its user thought it could do. In solids processing, we often struggle with a piece of equipment because it was a poor selection or installed improperly. Sometimes, a device is used because we happened to have one, and it worked well on a similar material.
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This is a good economic choice but may not be a good technical choice. How do you resolve this? Start with reliable physical property data.
The first physical property information I ask for when I take on a crystallization problem or design is the solubility curve. My request usually results in a deer-in-the-headlights look or — worse — the presentation of one from . A related issue is the meta-stable zone width and the potential for polymorphism or solvation, especially with solutes that cross at a given temperature. There are eight specific steps to ensure the solubility curve accurately represents your product and operations.
Slight changes in composition can significantly alter the solubility and meta-stable zone width. Your product may not match what’s reported in the chemistry books. You may need to account for batch-to-batch variations in the operation, as well. Consider on-line instrumentation to maintain the correct concentration in the crystallizer.
Crystallization is often confused with precipitation; they do share a similar phase change. However, undesirable reactions can occur due to a shift in solubility, where an impurity becomes more concentrated and reacts with either the solute or solvent. When reactions occur, you may need to change the method of generating supersaturation.
Polymorphic transformation from a metastable polymorph to a stable polymorph will reduce the solubility and alter the particle size. Solvates are less soluble and have slower dissolution characteristics. Both of these changes can cause a crystallizer to produce an undesirable — often poorly filtering — product.
One curve does not represent the system. Some solutes may take years to reach equilibrium. Knowing that the system is saturated is important. For example, it may be possible to start with a solution saturated at room temperature, cool it and then reheat it to just below the starting temperature to observe the crystallization process. However, if the rate of de-supersaturation is slow (because of slow kinetics or not enough crystal surface area), the solution may still be supersaturated.
The best way to demonstrate equilibrium is to show that undersaturation and oversaturation coincide with the same saturation concentration. The rate of approach to equilibrium depends on the available surface area of crystals exposed to the solvent. If you know the mass of excess solute and the particle size distribution, you can estimate the surface area, which gives a crude idea of the kinetics.
Perhaps the least appreciated aspect in determining solubility is temperature control. For steep solubility curves, minor temperature fluctuations can damage the results, especially when studying the meta-stable zone width. Most systems can tolerate 0.1°C variation, a practical limit for industry.
During any solubility study, the solution must be fully mixed. However, exercise caution in selecting an agitator; you must avoid secondary nucleation around it.
Both the solids phase and solution require analysis to develop the solubility curve. If possible, obtain the particle size distribution to ensure equilibrium. I recommend turning off the agitator and allowing the crystals to settle. Then, take a sample of the crystal-free liquor to determine the solute concentration. Finally, remove the solids by filtration. This last step requires great care to avoid altering the sample through evaporation, crystal growth or dissolution. In-situ sampling is best.
Knowing the width helps in selecting a crystallizer and establishing how to operate it. Evaluate at least three temperatures and solute concentrations. The procedure involves observing the temperature at which solids form and dissolve. The resulting plot of the lower temperature where crystals form defines the meta-stable zone width. The upper temperature verifies the solubility curve.
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With this information, solving crystallization problems is a breeze. The secret is recognizing that you need these important physical property details. The next step in selecting the proper equipment for the process is, you guessed it, physical properties for drying. This technology is slightly more complicated and involves subjective properties, such as stickiness, aeration density and frangibility.
However, a drying curve is a minimum requirement along with properties such as heat capacity, particle size distribution and density. The most common drying curve is from an oven batch operation.
A better option is to use a thin bed of solids to capture something close to single-particle drying — this allows for the simulation of many types of dryers. An alternative is to estimate from the oven drying curve the critical and equilibrium solvent content and then use a fluid-bed or rotary device to gather more precise data.
A drying curve test should include at least three constant inlet temperatures that are below any temperature limitation. In each test, the inlet conditions (temperature, flow, humidity and pressure) should remain constant.
You can generate the drying curve in three ways:
The latter approach doesn’t disturb the bed of solids and is much more accurate, especially in the later stages of drying. You must adjust loss-in-weight data for buoyancy effects and composition of the loss in weight.
Removing a sample for analysis reduces the dry mass of solids under study and makes correcting the overall mass balance difficult. Also, disturbing the bed can mix the solids and promote drying, resulting in an overly optimistic drying rate. Along with the solvent content as a function of time, you need the temperature of the bed.
It’s amazing how well you can select the correct equipment for processing solids once you have good physical properties.
Scaling specialty chemical production from lab-scale or pilot-scale to full-scale manufacturing is a high-risk endeavor. A recent incident in Pennsylvania, where a company experienced an explosion after a major scale-up, highlights the critical risks associated with this transition.
Successfully scaling up specialty chemical production requires careful planning, safety-first execution, and expert collaboration. This blog explores the top five challenges companies face when scaling specialty chemical production and provides solutions and a checklist to help manufacturers avoid costly mistakes.
The most common challenges are:
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