The first principles models were developed based on heat- and mass-transfer systems and validated using operating data from plant tests also used to develop the empirical models.
The MPC application obtains the current process information and, based on its dynamic model, calculates a sequence of future process moves over a specified time period. An optimization algorithm determines the correct of the manipulated variables so that several objectives can be achieved simultaneously. The control system dynamically compares the predicted trajectory with the process response and corrects for any differences detected. Because of the dynamic trajectory compensation and the taking into account of process constraints, the control system is stable and operationally reliable. Using small steps, the stable process is led gradually to its optimal operating point while larger steps can bring prompt corrections if disturbances affect the system.
The application for the distillation system controls the production rate and quality of the 190-proof product that goes to the sieves. It also keeps the pressure and temperature constraints within the limits to avoid flooding and reduces ethanol losses. Table 1 lists some of the control components for the distillation process.
Table 1. The application predicts changes in controlled variables to moves in manipulated variables and corrects as necessary.
In addition to controlling the differential temperature and pressure in the three columns, the application contains virtual online analyzers for the amount of ethanol in the bottoms of the beer and side stripper columns. The controller also includes models for measured disturbances such as beer feed and hydroheater temperature (e.g., waste heat flashed to the side stripper column). The hydroheater temperature provides a prediction of steam changes going to the side stripper column from the cook flash tank.
Anhydrous alcohol quality control is the most important objective for the molecular sieves. The MPC application controls total ethanol production rate and end product quality by manipulating the sieve pressure and feed flow rate. The controller includes disturbance models for feed composition and temperature. Besides 190-proof flow rate and composition, distillation and sieves variables are linked through the 190-proof tank level.
Pavilion’s MPC technology provides a number of operational advantages:
- Model updates and controller tuning can be made while the system is running, enabling rapid and correct implementation of changes in process dynamic behavior or in process limits.
- Models don’t require further adjustment unless there are major modifications to the process.
- The process is kept stable during fluctuations such as changes in the weather, feed composition or temperature.
In addition, BSE has realized significant economic benefits:
- Higher average anhydrous alcohol production has resulted in a 10.24% increase in ethanol production.
- The distillation/sieve/evaporator energy utilization per pound of steam/gallon of ethanol has been reduced by 9.6%.
The APC project has allowed BSE to achieve record production rates by finding optimal process operating conditions, which also has cut the amount of energy needed per gallon of ethanol produced. Thanks to the APC project, BSE has overcome operational constraints that limited its process. Pavilion provided BSE with a tool that continually makes full use of the process’s operating capabilities.
- Wald, Mathew, “Is Ethanol for the Long Haul?,” Scientific American, p. 42 (Jan. 2007).
Jacob Duke formerly was plant manager of Badger State Ethanol’s Monroe, Wis., plant. Lina Rueda is an advanced process control engineer for Pavilion Technologies, Austin, Texas. E-mail her at LRueda@pavtech.com.