Recipient Organization
ASSURED BIOTECHNOLOGY CORPORATION
228 MIDWAY LANE, SUITE B
OAK RIDGE,TN 37830
Performing Department
(N/A)
Non Technical Summary
Formaldehyde (H2CO) has been classified as carcinogenic to humans by the International Agency for Research on Cancer (IARC) since 2004. Formaldehyde is the most important aldehyde produced commercially, and is used in the preparation of urea-formaldehyde and phenol-formaldehyde resins. One source of formaldehyde in homes, schools, and offices comes from furniture and cabinets manufactured from composite wood products such as particleboard (PB), medium density fiberboard (MDF), and hardwood plywood (HWPW). Composite wood products use urea formaldehyde (UF) based resins to bind fibers together. Formaldehyde in composites is significant enough that the California Air Resources Board (CARB) recently issued regulations to cap formaldehyde emissions from composite panels used in finished consumer products made from these panels. EPA has been petitioned to make the CARB standard into a federal regulation and is expected to publish an Advanced Notice of Proposed Rulemaking on this issue this fall. The EPA, the Consumer Products Safety Commission and the Centers for Disease Control have identified levels above 0.1 ppm, as a concern for exposure by sensitive populations. Existing standards and regulations (ANSI A208.1 and A208.2, HUD 24CFR3280.308) limit formaldehyde emissions for HWPW, PB, and MDF panels. However, formaldehyde levels in numerous FEMA trailers issued to hurricane Katrina victims greatly exceed the 0.1 ppm benchmark with some even exceeding the OSHA limit of 0.75 ppm, indicating that many high H2CO emitting products go undetected during manufacturing. As H2CO emission standards become more stringent (e.g., CARB regulations, potentially EPA), there is an urgent need for continuous monitoring of formaldehyde emissions during manufacture. Accurate on-line monitoring will allow manufacturers to gain tighter control of emission levels, reduce rejected lots, reduce claims, and improve profitability. Ultimately, real-time formaldehyde emissions monitoring will improve indoor air quality. Assured Biotechnology Corporation proposes to develop a continuous on-line monitoring system that will merge near infrared (NIR) measurements with plant process parameters to achieve high levels of prediction of H2CO. The objective of this Phase I research is to further evaluate an alpha-prototype real-time NIR spectroscopy hardware and software system to accurately detect H2CO emissions. Assured Biotechnology Corporation will use multivariate methods with the NIR spectroscopy data fused with critical process variable data to generate accurate calibration models. A commercial-ready system will be identified from Phase I results. A real-time H2CO on-line monitoring device will have great commercial potential with U.S. manufacturers and with foreign importers to the U.S. as CARB and other standards are enforced on imported products that emit H2CO.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
The goal of this SBIR Phase I research and development effort is to provide a proof of concept for a continuous monitoring system that will merge NIR spectral data with plant process variables to achieve high prediction results of H2CO emissions during wood composite panels manufacture. The proof of concept will demonstrate implementation of this system for medium density fiberboard (MDF) manufacture. MDF is a wood composite panel that uses a high resin to wood ratio and is one of the highest H2CO emitting products. The first step in Phase I will consist of improving the industrial strength of an alpha-prototype device that includes a full range NIR spectrometer, automation components for light source and referencing. Several Visual Basic programs will be enhanced to control the device, merge NIR spectral data with associated plant process variables and H2CO emission test results, store those data in a database, build partial least squares calibration models and predict H2CO emissions real-time. As soon as the first calibration models are built, validation of the constructed models will be assessed. The impact of the addition of the plant process variables to the NIR spectral data will be measured by comparing the validation performances of calibration models built with NIR spectral data only and models built with NIR spectral and plant process data. Calibration models will allow plant technical managers to identify which process variables are critical for H2CO emission variation. Near the end of the Phase I field study, stored data will be retrieved to assess other multivariate methods such as kernel partial least-squares and kernel ridge regression The overall best performing method will be implemented in the commercial ready system for Phase II. Given that the cost of a full range NIR spectrometer (1000-2500 nm) is substantive, calibration models will also be built and validated using a reduced spectral range (1000-1800 nm). A NIR spectrometer operating in that reduced range is typically one-half to one-third the cost of a full range NIR spectrometer. Findings could have a significant impact on the overall cost of the final H2CO monitoring system during commercialization.
Project Methods
Phase I of this project will require the construction of a custom near-infrared (NIR) spectroscopy device that will be installed in a medium density fiberboard (MDF) or particleboard (PB) production facility with the ultimate goal of predicting long term formaldehyde emissions of finished panels, real-time. A full three months plant trial will be considered. The spectroscopy device will be comprised of a NIR spectrometer operating in the 1000-2500 nm range, an automated white reference material apparatus operated by air cylinders, a tungsten halogen light acting as an excitation source for the wood furnish, and an air flushing system to maintain critical components free of dust. The aforementioned components will be housed in a compact aluminum housing. The device will be operated by a computer that will store collected NIR spectra into a Microsoft SQL database. The custom device will be placed on the side of the forming line, collecting NIR spectra of the wood furnish, before the furnish is being pressed into a panel. A NIR spectrum will be collected every minute, adjusting the collection time based on the production line speed. Results from the production facility testing laboratory that performs formaldehyde tests on panel samples using a small scale chamber (ASTM D6007-02) will be merged with NIR spectra collected on the furnish corresponding to the tested panel samples. As soon as a sufficient number of NIR spectra and corresponding formaldehyde test results will be gathered (ASTM E1655-05), a partial least squares calibration model will be built, allowing the NIR device to start predicting real-time the long term formaldehyde emission of panels. Accurate real-time predictions will allow the plant manager and personnel to gain knowledge of the formaldehyde emissions of finished panels several minutes before being produced and more than 24 hours before being laboratory tested. Most importantly, formaldehyde emissions of the produced panels will be known in between consecutive laboratory tests, closing an information gap of several hours. An attempt will also be made to merge spectroscopic data with plant process variables data, and relate to formaldehyde test results. Improved prediction performance is expected from the added process parameters. MDF and PB plants typically produce several different products within a 24 hours period. Individual calibration models will be constructed for the two most produced products. An attempt will also be made to build a calibration model for all product types. Formaldehyde emission values predicted by the NIR device will be compared to the values found by the small scale chamber tests (performed several times a day, at least 24 hours after the panel sample has been extracted from the production line). Correlation coefficient, root mean square error and average error will be evaluated, providing an unequivocal measure of the NIR device performance.