The following tip is from the ISA book by Greg McMillan and Hunter Vegas titled 101 Tips for a Successful Automation Career, inspired by the ISA Mentor Program. This is Tip #96, and was written by Greg.

When I was leaving for college, my Dad said, “Make sure you use a good grain analyzer to optimize alcohol batch time and yield.” These words of wisdom would be useful in the years to come at “Purple Passion” parties with tubs of grain alcohol and grape juice, but their greatest value has been seen in terms of ethanol plant optimization opportunities.

For that company’s plants, I developed a control strategy that uses an at-line analysis of corn fermentability coupled with corn feed rate to provide an inferential measurement of production rate as the process variable for a simple flow controller. The operator could set the production rate for the front end of a plant that included a parallel train of batch fermenters. When the corn analyzer indicated that the corn fermentability (predicted yield) had increased, the ethanol production rate controller would cut back the corn feeder speed, immediately translating the increase in yield into a decrease in corn feed rate. Because corn is more than 50% of the cost of producing ethanol, the reduction in cost of goods sold (COGS) was significant. Furthermore, when an off-line analyzer indicated that a fermenter had reached an endpoint earlier or later than expected, a fraction of the equivalent change in fermentability was taken as a bias correction to the analyzer signal.

Most recently, I realized that I could use the slope of the batch profile for ethanol (ETOH) concentration to decide whether to end or extend the batch based on the values of additional yield and capacity.

For fed-batch reactors with concentration measurements, the slope of the batch profile can be controlled. The setpoint for the slope is set to match the best achievable batch profile. For bioreactors, the cell concentration profile can be used to control the glucose to glutamine ratio. A Nova BioProfile FLEX analyzer and auto sample can be used as an at-line analyzer for this optimization.

Concept: The slope of a key concentration during the batch can be used to control the batch profile. The slope of product concentration near the end of a batch and the analysis time interval can be used to calculate the value of additional capacity made available by terminating the batch and the value of additional yield obtained by waiting till the next analysis. A feed analyzer that predicts yield can provide a production rate controller that immediately optimizes yield for fed-batch processes or for the continuous front end of traditional batch processes.

Details: Use an off-line or at-line feed analyzer to compute the yield of a key raw material. For a continuous front end or fed-batch flow controller, compute a process variable that uses the predicted yield to provide an equivalent product flow rate. Use the production rate controller to immediately cut back on raw material feed rate for a measured increase in predicted yield. Use an enhanced PID (Tip #100) to deal with the update time from at-line and off-line analyzers. If the batch takes less or more time than normal toreach the end point, use a portion of the inferred change in yield to correct the feed analyzer.

Consider the slope of a key concentration measured at-line or off-line during the batch as a process variable for a controller to adjust an operating condition that affects the conversion rate (or cell growth rate for bioreactors). If a periodic analysis is not available during the batch, see if the cooling rate can provide an inferential measurement of conversion rate in exothermic process chemical reactors and in fermenters for alcohol production. See if an oxygen uptake rate profile can provide an inferential measurement of cell growth rate in bioreactors for pharmaceutical production. For continuous analysis and inferential measurements, use a deadtime block to create the ramp rate (Tip #89).

The deadtime for the block must be large enough to provide a good signal-to-noise ratio. From the batch profile slope (e.g., conversion rate, cell growth rate, product formation rate) near the end of the batch, compute the additional product produced in the deadtime interval or the analysis time interval for sampled measurements. Use the slope near the end of the batch to make an economic decision as to whether the batch should be terminated for extra capacity or extended for extra yield. Convert the slope to product mass flow and multiply by the analysis time interval to get the additional product mass till the next analyzer update. Divide the current product mass in the batch by the mass of each key raw material added to the batch to get the yield in terms of product per each key raw material. Divide the additional product mass per batch by this yield and multiply by the cost per unit mass of each key raw material.

Sum the results to arrive at a dollar value of the additional product yielded by extending the batch. Take the current product mass in the batch and divide by the current batch time in hours to estimate the current batch production rate. Alternately, use the production rate from the flow controller based on predicted feed yield. Multiply the production rate by the profit per unit mass and the analysis time interval to estimate the value of additional capacity made available by terminating the batch. Shorten the analysis time interval near the end of the batch to make the optimization more accurate. If the analysis of a key composition or product is not available until after the batch has been transferred, the results can be cautiously used for the next batch.

Watch-outs: Bad or missing analyzer updates must be screened and not used for computing the batch profile slope. Inlet and outlet temperatures must be synchronized for cooling rate calculations.

Exceptions: Optimization using batch slope cannot be used if batch analyzer updates are too infrequent and an inferential measurement is not available.

Insight: Feed analyzers and batch profile slope can be used to optimize batch efficiency and capacity.

Rule of Thumb: Use a feed analysis of predicted yield to create a production rate controller and use a batch profile slope for optimization of batch end point and cycle time.

About the Author
Gregory K. McMillan, CAP, is a retired Senior Fellow from Solutia/Monsanto where he worked in engineering technology on process control improvement. Greg was also an affiliate professor for Washington University in Saint Louis. Greg is an ISA Fellow and 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 magazine 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 2010. Greg is the author of numerous books on process control, including Advances in Reactor Measurement and Control and Essentials of Modern Measurements and Final Elements in the Process Industry. Greg has been the monthly "Control Talk" columnist for Control magazine since 2002. Presently, Greg is a part time modeling and control consultant in Technology for Process Simulation for Emerson Automation Solutions specializing in the use of the virtual plant for exploring new opportunities. He spends most of his time writing, teaching and leading the ISA Mentor Program he founded in 2011.

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About the Author
Hunter Vegas, P.E., has worked as an instrument engineer, production engineer, instrumentation group leader, principal automation engineer, and unit production manager. In 2001, he entered the systems integration industry and is currently working for Wunderlich-Malec as an engineering project manager in Kernersville, N.C. Hunter has executed thousands of instrumentation and control projects over his career, with budgets ranging from a few thousand to millions of dollars. He is proficient in field instrumentation sizing and selection, safety interlock design, electrical design, advanced control strategy, and numerous control system hardware and software platforms. Hunter earned a B.S.E.E. degree from Tulane University and an M.B.A. from Wake Forest University.

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