|Butts, Christopher - Chris
|DAVIDSON, JAMES - RETIRED ARS
|TROEGER, J - RETIRED ARS
Submitted to: ASAE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 3/15/2003
Publication Date: 7/27/2003
Citation: BUTTS, C.L., DAVIDSON, J.I., LAMB, M.C., KANDALA, C., TROEGER, J.M. A DECISION SUPPORT SYSTEM FOR CURING FARMERS' STOCK PEANUTS. ASAE ANNUAL INTERNATIONAL MEETING. 2003. p. 17.
Interpretive Summary: In the United States, most of the peanuts are mechanically cured or dried to reduce the moisture content so that they can be safely stored. Peanuts are cured by forcing heated air up through them while in a drying bin or trailer at a commercial drying facility which may operate as many as 200 individual dryers simultaneously. The moisture content of the peanuts in each dryer must be monitored by periodically obtaining and shelling a sample from each dryer. A computer program was written that estimates the time required to dry each load of peanuts and suggested sampling schedules. Predicted drying times were within 2 hours of the actual drying times. These accurate predictions of drying times allow the dryer operator to minimize over drying and efficiently use the available dryers. Eliminating over drying will reduce the drying cost by approximately $2.50 per metric ton and eliminate quality losses equivalent to approximately $9.00 per metric ton.
Technical Abstract: A decision support system (DSS) to manage commercial peanut drying facilities was developed to provide estimates of peanut drying time, real-time estimates of peanut kernel moisture content, and sampling schedules. The DSS incorporated empirical equations developed from the output of PNUTDRY, a peanut drying model, to predict peanut drying time. The DSS requires data to describe the physical layout of the drying facility including the number of drying slots and the dryer characteristics for each drying slot, daily maximum and minimum ambient air temperature, maximum allowable temperature rise above ambient, the maximum allowable plenum temperature, and initial and final kernel moisture contents. A linear equation was developed to estimate peanut pod moisture from the kernel moisture content with an R2=0.988. Data from drying tests conducted during the 1988, 1989, and 1992 harvest were used to validate the drying time predictions. The estimated drying time compared favorably with the actual drying time (R2=0.74) as long as the input data was within the range of data used to develop the prediction equations. Peanut pod moisture or kernel moisture could be used with no significant effect on estimated drying time. The average predicted drying time was within 2 h of the average actual drying time and was not significantly different.