1. Nugget: Determining Optimal Sensor Locations for State and Parameter Estimation Juergen Hahn Artie McFerrin Department of Chemical Engineering Texas A&M University College Station, TX 77843-3122 ACS PRF #43229-G9

2. Nugget: ACS PRF #43229-G9 In order to monitor, control, and optimize a particular process in modern chemical plants, it is essential to know the value of many of the process variables/parameters. However, it is not necessary to directly measure every variable for which a value is required. By strategically measuring some of the variables, the remaining ones can be computed using a soft sensor. It is also possible to estimate many of the physical parameters of the process using a related approach. It is the focus of our work to determine the optimal number of sensors as well as their location, while simultaneously taking into account their cost. Additionally, our developed techniques are taking into account that most plants are nonlinear in nature and contain uncertainty in the process parameters.

3. Nugget: ACS PRF #43229-G9 We have derived a novel-approach for determining the best locations for sensors which can be used for retrofitting existing plants as well as for determining sensor networks for new plants. The key ideas are:  Develop and use a sensor configuration measure which is viable for nonlinear systems over an operating region  Formulation of an optimization problem for sensor network design  Solution of an optimization problem by genetic algorithms

4. Nugget: ACS PRF #43229-G9 The technique is best illustrated by an example where up to six temperature sensors are placed along the height of a distillation column with 30 trays.

5. Nugget: ACS PRF #43229-G9 In the following slides, the top figures show the sensor location measure along the height of the column and the red dots represent the location of the individual sensors. The blue shaded region in the figures at the bottom of the page indicate the amount of information that the designed sensor network will return for estimating the temperature of the trays. The graphical interpretation of these results is that the optimization problem maximized the blue- shaded area for a given number of sensors.

6. Nugget: ACS PRF #43229-G9

8. 2 Sensors

9. Nugget: ACS PRF #43229-G9 3 Sensors

10. Nugget: ACS PRF #43229-G9 4 Sensors

11. Nugget: ACS PRF #43229-G9 5 Sensors

12. Nugget: ACS PRF #43229-G9 6 Sensors

13. Nugget: ACS PRF #43229-G9 For this chosen example, the developed technique returns results which are inline with what plant operation engineers have determined from many years of experience. However, since we now have a systematic procedure for designing sensor networks, it is possible to determine instrumentation requirements for new plants and individual pieces of equipment within a few hours instead of requiring years of experience and plant data.