Corn Analysis Modeling and Control

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1 Corn Analysis Modeling and Control For Ethanol Yield Optimization Standards Certification Education & Training Publishing Conferences & Exhibits

2 Presenter Greg is a retired Senior Fellow from Solutia/Monsanto and an ISA Fellow. Greg was an adjunct professor in the Washington University Saint Louis Chemical Engineering Department Presently, Greg contracts as a consultant in DeltaV R&D via CDI Process & Industrial and is a part time employee of Experitec and MYNAH. Greg received the ISA Kermit Fischer Environmental Award for ph control in 1991, the Control Magazine Engineer of the Year Award for the Process Industry in 1994, was inducted into the Control Process Automation Hall of Fame in 2001, was honored by InTech Magazine in 2003 as one of the most influential innovators in automation, and received the ISA Life Achievement Award in Greg is the author of numerous books on process control, his most recent being Advanced Temperature Measurement and Control. Greg has been the monthly Control Talk columnist for Control magazine since Greg s expertise is available on the web site: 2

3 Plant Auto Sampler (Gamet model B-310)

4 Off-Line Version of NIR-T Analyzer (Foss Infratec 1241)

5 Virtual Plant Uses Actual Graphics and DCS Configuration with Process Model Operator Graphics and Historian Virtual Plant Laptop or Desktop or Control System Station Advanced Control and PIDPlus Field Instrumentation Model Process Model 5

6 Enhanced PID Algorithm for Variable and Large Update Times K c Elapsed Time K c Elapsed Time + + T D T D + + Link to PIDPlus White Paper Whitepapers/WP_DeltaV%20PID%20Enhancements%20for%20Wireless.pdf PID integral mode is restructured to provide integral action to match the process response in the elapsed time (reset time set equal to process time constant) PID derivative mode is modified to compute a rate of change over the elapsed time from the last new measurement value PID reset and rate action are only computed when there is a new value If transmitter damping is set to make noise amplitude less than communication trigger level, valve packing and battery life is dramatically improved Enhancement compensates for measurement sample time suppressing oscillations and enabling a smooth recovery from a loss in communications further extending packing -battery life

7 Virtual Plant Model Configured in DCS using Composite Template Library Analyzer Slurry Tank 7

8 Innovative PID System to Optimize Ethanol Yield and Carbon Footprint Corn Production Rate Enhanced PID setpoint AC 1-4 SC 1-4 AY 1-4 AT 1-4 XY 1-4 NIR-T Fermentable Starch Correction Average Fermentation Time Enhanced PID XC 1-4 Feedforward DX 2-4 Slurry Solids Enhanced PID DC RCAS 2-4 FC 1-5 Dilution Water FT 1-5 FC 1-6 Backset Recycle FT 1-6 DT 2-4 Coriolis Meter Slurry Tank 1 Slurry Tank 2 Lag and Delay DY 2-4 Predicted Fermentable Starch 8

9 Optimization of Corn Feed by At-Line Corn Analyzer & Production Rate PID The PID AC1-4 controller on the first slurry tank manipulates corn feeder speed. The PID is a production rate controller with its PV and SP scaled gpm ethanol. The PV is computed based on corn feed rate and corn analysis of fermentable solids. The Dynamic Reset Limit prevents an output faster than the corn feeder can respond. For the slurry tank model running 24x real time the analyzer will pull a sample every 4 seconds and communicate a result with a latency of 2 seconds. If the change in analyzer from the last significant change is less than the threshold sensitivity setting of 2 gpm for production rate, there is no update to the PV. Since the PID will wait till the effect of its feedback correction is seen and will ignore noise, the PID gain can be set equal to the inverse of the process gain to provide a full immediate correction. In order to prevent an oscillatory response from this great increase in gain, the reset time must also be decreased to be about equal to the loop deadtime to balance the proportional and integral contribution. This decrease in reset time is counterintuitive because one would think of increasing the reset time to suppress oscillations. Changes in analyzer sample time or communication latency do not affect the tuning because PID action is suspended until there is a PV update. For changes in predicted yield (fermentable starch), the PID makes a single adjustment in corn feed rate bringing the production rate back to setpoint. Production setpoint can be easily set to match corn supply, market demands, and backend constraints with familiar PID functionality and operator interface.

10 Correction in Corn Feed Rate for Change in Yield or Desired Production Rate Predicted Yield Production Rate Corn Feed Rate 10

11 Control and Optimization of Second Slurry Tank Solids The PID DC2-4 on the second slurry tank is scaled 0-40 wt % fermentable solids. Slurry solids is inferred from a Coriolis meter density measurement in recycle. The output of the PID manipulates the dilution water to the first slurry tank. Since the dilution water is added to the first slurry tank while the solids is measured in the second slurry tank, there is an equivalent deadtime from the residence time of the two slurry tanks in series plus mixing and injection delays. The loop deadtime is about 80 seconds for a model running 24x real time. A feedforward signal is computed for the dilution water based on corn feed rate, steam injection, and backset recycle. An update time of 12 seconds is set on the inferential measurement of fermentable solids to ignore feedforward timing errors. A deadtime block with a deadtime parameter of 80 seconds in the external feedback path from the analog output to the PID provides deadtime compensation. A threshold sensitivity setting is used to ignore measurement noise. Since the PID compensates for deadtime, ignores noise, and waits out feedforward timing errors, the PID gain can be increased to provide a faster setpoint response. The feedforward signal from changes in Slurry Tank 1 prevent the changes in production rate from affecting the solid concentrations in Slurry Tank 2. The feedforward signal is the production rate setpoint minus the water flow from other sources such as backset. The remote cascade (RCAS) setpoint is the desired solids concentration multiplied by a fermentable starch factor predicted by the corn analyzer. The RCAS prediction passes through a delay and lag set equal to the deadtime and residence time, respectively, so the change in setpoint coincides with the change in solids measurement from a change in corn feeder speed based fermentable starch.

12 Feedforward Correction for Change in Yield or Desired Production Rate Feedforward Slurry Solids Dilution Water Flow 12

13 Control and Optimization of SSF Batch Cycle Time The PID XC1-4 uses an at-line High Performance Liquid Chromatograph (HPLC) for ethanol in simultaneous saccharification & fermentation (SSF) batches. Saccharification uses an enzymatic reaction to convert maltose, maltriose, and higher polymer Dextrins to glucose. Fermentation employs yeast to produce ethanol from glucose. In older plants, saccharification and fermentation are in separate vessels. The HPLC provides an inferred measurement that is a running average of the fermentation time (time to reach ethanol endpoint) in most recent SSF batches. The model has multiple SSF vessels each with a saccharification-fermentation time of 8 minutes (240x speedup), 80 seconds (24x speedup) for charging, and 40 seconds (24x speedup) for draining and preparing for next batch. When the HPLC indicates the ethanol has reached the desired end point as, the running average of batch times is updated (the actual batch cycle time is fixed). The running average is the time to the desired end point. After an initial delay of 10 minutes, a new batch time result is available and the running average is updated every 2 minutes in the SSF model with 240x real time kinetics. The update is faster for a decrease in batch time from an increase in actual fermentable solids. The enhanced PID output biases a correction to analyzer measurement of fermentable solids, which changes the inferred production rate. The first slurry tank PID controller adjusts the corn feeder rate to bring the production rate back to setpoint. An unmeasured increase in fermentable solids will show up as an early achievement of the batch end point resulting in a cutback in corn feed rate providing an immediate improvement in ethanol yield.

14 Typical SSF Batch Concentration Profiles for 240x Model Kinetics Dextrins (DP4+) Glucose (DP1) Yeast End Point Ethanol Maltose (DP2) Maltriose (DP3) 14

15 Conclusion Ethanol producer contracts with Monsanto for a flat yearly services fee. In exchange Monsanto provides producer an instrument with the calibration, maintenance, plus assistance with data analysis & training. Producer gets access to a web based data base system to pull data and run reports and yearly calibration improvement. Motivation is to improve ethanol industry. Auto samplers are purchased directly from Gamet. MYNAH offers virtual plant & Experitec offers DCS for yield optimization The at-line analyzer and control system provides Rapid optimization of corn feed rate Minimization of corn cost and carbon footprint Online manipulation and monitoring of production rate No advanced control software needed Familiar PID operator interface and standard configuration Easy to tune and extremely stable control system due to enhanced PID Fast prediction of yield updated by actual batch endpoints Process knowledge from changes in yield and feeder speed Recognizable patterns from reduced variability by PID control

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