Advanced Control Gives Goodyear a One-Two Bounce

It takes plenty of raw materials and energy to make rubber for tires. And managing all those feedstocks and handling all that energy can make for quite a juggling act.

For example, Goodyear Tire & Rubber Co.'s largest U.S. chemical plant in Beaumont, Texas, produces huge amounts of rubber and C5 high-purity isoprene, and so it  had been facing ongoing hurdles in its high-purity monomer recovery unit, according to Kaylynn Johnston, Goodyear Chemical's senior engineer.

"We have fluctuations in feed composition, and this leads to variability in our operations, which means we may need to compensate with changes in temperature or other setpoints," said Johnston. "The Beaumont plant also is Goodyear's largest energy consumer, and our C5 refinery and its distillation columns are the plant's largest energy consumer, mainly due to the all the steam they use. Because of these challenges and variability, we thought advanced process control (APC) could help."

Johnston and Jon Cimino, account manager for Emerson sales representative Scallon Controls, presented "Sustaining and Extending Advanced Control Value in an Operating Plant" this week at the 2010 Emerson Global Users Exchange in San Antonio, Texas.

The C5 refinery's main operating challenges include:

• A product purification unit that involves multiple distillation trains with 11 columns, both conventional and extractive.
• Ultra-high purity product specifications that require very tight quality controls.
• Multiple large, 200+ tray columns that result in extremely long time constants.
• Different feedstock suppliers with different qualities.
• Large energy needs and production requirements.
• Safety margins required to compensate for disturbances in feed quality.

"Product loss in the overhead was reduced by 22% and steam usage was reduced by 7%." Goodyear Chemicals' Kaylynn Johnston shared her company's successful implementation of advanced distillation controls, leveraging Emerson's DeltaV APC capabilities, SmartProcess distillation optimizer as well as APC consulting services.

Next, while Emerson prepared a functional specification that included a design for the C5 process unit's 11 distillation columns, the initial implementation phase only covered the first column in the series, which was done to gradually give Goodyear and staff experience with APC technology, nurture operator acceptance, and show APC's value to Goodyear's management. APC will be added in later phases and other C5 columns by Goodyear's engineers themselves with support from Emerson's APC consultants.

"At first, the C5 refinery's operators were uncomfortable with APC, but the engineers explained its strategic value to them, and this helped their comfort level," said Cimino. "It also helped as the operators got more run-time experience with the APC tools. The dynamic simulator in SmartProces was very helpful, too. For example, we wanted to know ahead of time about some controller loading issues, so we loaded the simulator onto the demonstration system, and the simulation showed us that controller loading wouldn't be a problem."

Goodyear and Emerson also used the SmartProcess Distillation Control Module, which includes standard distillation calculations, module library, predict block and pre-configured neural blocks.

"The first column in the C5 refinery separates chemical components based on different boiling points. Tray temperatures reflect composition on that tray, but they need to be compensated for pressure. As a result, control strategies are based on 'what comes in, must go out,' and so we need material balance of overhead and feed ratio, and an energy balance of reflux and feed ratio," explained Johnston.

In the first project—to begin optimizing the distillation column—Johnston and her colleagues used SmartProcess function blocks, which can be used to set objectives for manipulated variables (MVs), controlled variabled (CVs) and constraints for overall objective functions, and then view them all in the software's optimizer window. "We were able to set minimums or maximums for critical variable, and also prioritize those variables," added Johnston. "We also could establish target setpoints for the column, and calculate setpoints for multiple variables."

Consequently, when Goodyear turned its newly optimized controllers on, it immediately gained positive results. "Because the column consumes so much energy, Goodyear's engineers wanted to minimize steam use and reduce the amount of product going out of the overhead," added Johnson. "Once it was optimized, the controller immediately started reducing distillate rate and overhead [OH] concentration. Average product loss via OH was reduced by approximately 22%. Product impurities were maintained within specifications, and there were new opportunities to reduce safety margin. And, we reduced overall steam use by 7%."

Later, in the second project, the challenge was to optimize the C5 column's extractive distillation system for isoprene, which involves four variables. "The temperature setpoint needs to be such that the sidedraw contains most of the impurities, but not too much product," said Johnston. "We first need good information for our manipulated variables, and then we can run tests in manual or automatic."

These process variables include MVs, CVs, disturbance variables (DVs) and constraints:

• MVs include controller setpoints written to by the model-predictive controller (MPC), such as first column sidedraw temperature setpoint.
• CVs include process variables that are to be maintained at a specific value or set point, such as column OH pressure.
• DVs are measured variables that may also affect the value of controlled variables, such as solvent concentration, feed flowrate, reflux remperatures, and first column OH pressure.
• Constraints (LV) are variables that must be maintained within an operating range (a special type of CV), such as product stream impurity level and percent of product in the impurity stream.

Next, models were developed for these process variables within the SmartProcess software, and Goodyear's engineers also were able to use their own knowledge to adjust were able to adjust the models' parameters. Actual process data—for pressure and OH impurity, for example—were then used to verify and validate the models.

Johnston reported this modeling and subsequent tuning resulted in improved temperature control, further reduced product loss, same and improved impurity levels. "The new control scheme in our second project also reduced the rate of the purge stream and the product concentration in the stream. Overhead product quality was not negatively impacted. We reduced product losses by 7,000 pounds per day. And, we reduced energy consumption a moderate amount," said Johnston.

To maintain its new APC tools and system in the future, Johnson added that Goodyear may need to change the limit ranges of its manipulated, controlled or constraint variables if operating parameters such as feed rate or composition change significantly. "We may also need to tweak or rebuild out models at some point, and we might need to regenerate the controller to adjust performance versus robustness, such as in 'penalty on move' versus 'penalty on error' situations."

Thanks to the success of its two projects and collaboration with Emerson, Johnston said Goodyear also plans to install SmartProcess Distillation module and APC models on its additional distillation columns in the C5 refinery process unit, and explore the potential for using APC in other units on non-distillation applications.