4.6 Article

Refining Cannabidiol Using Wiped-Film Molecular Distillation: Experimentation, Process Modeling, and Prediction

期刊

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
卷 61, 期 19, 页码 6628-6639

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AMER CHEMICAL SOC
DOI: 10.1021/acs.iecr.2c00290

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资金

  1. United States Department of Agriculture.National Institute of Food and Agriculture (USDA-NIFA) [NC.X332-5-21-130-1, 1023321]
  2. Joint School of Nanoscience and Nanoengineering (JSNN)

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This work describes the process of stripping and refining CBD from hemp extracts using a WFMD system. By optimizing process conditions, high concentrations of CBD can be obtained.
This work describes the stripping and refining of cannabidiol (CBD) from hemp extracts using a wiped-film molecular distillation (WFMD) system. The process takes place in two stages, where the CBD gets stripped in the first stage and refined in the second stage. The main feed encompassing decarboxylated hemp extracts enters the stripping step at a CBD concentration of about 35.8 wt % and leaves at 48.0 wt %, where the majority of terpenes leave the extracts. In the refining stage, the effects of process conditions, including pressure, evaporation temperature, and condensation temperature, were examined using the response surface methodology (RSM) toward the maximum CBD concentration and recovery. A second-stage recovery of 92.66 wt % was achieved at the concentration of 80.19% by applying a pressure of 40 Pa, an evaporation temperature of 170 degrees C, and an internal condenser temperature of 20 degrees C. Analysis of the product showed that a low-pressure operation did not remove tetrahydrocannabinol from the CBD-rich product, thus proving to be an improper choice of WFMD for removing the psychoactive component. It was also found that the pressure reduction and increases in evaporation and condensation temperatures were contributing to higher CBD concentrations, although the condensation temperature had no effect on the recovery amount. The RSM and artificial neural network were further tested to assess their prediction capacity toward the efficiency and process performance. It was found that both models offer satisfying prediction capability, although the RSM had larger margins of error.

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