# Presentation: 369432

## DOE /  EPSCoR Lab Partnership PI: Dr. Asad DavariECE Dept. WVU Tech In collaboration with National Energy Technology Laboratory (NETL)Dr. Lawrence ShadleDuration: 3/15/04-3/14/07

- Real Time/ Online Modeling and Control of Cold Flow Circulating Fluidized Bed

## Outline

- Background
- Results of Primary Work
- New Proposal
- Comments

## How Collaboration Initiated

- University/Student Partnership
- (1998-2002). “Modeling and Control of Cold Flow Circulating Fluidized Bed with NNs”
- Close working Relationship Between
- Student, Faculty and Lab Scientists.
- Visit the Lab Biweekly and Summer.
- Research/ University, and
- Implementation/ Lab.

## CFB at NETL, Morgantown

- This is a cold flow circulating fluidized bed(CF-CFB), as there is no combustion component in the fluidized bed.
- This has been done to facilitate the study of dynamics of the fluidization and in absence of heating component which might cause complications in the process.
- Usually cork dust or coal powder is circulated.

## Fluidization - Definition

- Fluidization is observed when a bed of solid particles comes in contact with a vertical upward “fluid” flow, in an intermediate range of flow rates, within a confined space or volume.

## Circulating Fluidized Bed

- Solids

- High Pressure Fluid

- To Recirculation of
- the Solids

- Spent Fluid

- Solids

## Operating Conditions

- Riser Pressure ( Pr): PDT841+PDT842+PDT811A+PDT801: [0.3,0.7]psid
- Upper Standpipe Pressure (Psu): PDT853+PDT864: [0, 0.4] psid
- Mass Flow Rate (Ms): F874: [600, 10,000] lb/hr
- Aeration Rate (Fy): FY171: [100,1000] scfh

## Challenges

- Recycle nature of CFB technology allows for a better process, but also makes modeling and process many times more complex to understand.
- Tightly coupled interactions between components of the CFB result in amplification of undesirable mechanisms.
- The method of analysis must be chosen to most dependably predict and control the dynamic performance of CFB.

## Slide 9

## Modeling Using Neural Networks

- An offline model of the CFB has been modeled using Neural networks with the back propagation algorithm  and the model is trained using the Levenberg –Marquardt Optimization technique

## Modeling

- MIMO Model for predicting
- NN is trained on the first 1800 sec of data, and then used to extrapolate on the next 1800 sec data

## Neural Network Model for CFB

## Test Run Result of Original Plant with offline model

## Indirect (Adaptive) Control Architecture

## Direct Control Architecture

- 19

## Conclusions

- NN Model and controller has been implemented on the CF-CFB
- The controller has been tested for robustness and stability by implementing it on real plant and has been found to be successful for the same operating regime
- Future work would be directed in trying to improve the performance and developing an online model and controller (led to the new proposal)

##   Aim of The Proposal

- The aim of this partnership with the NETL is to conduct basic research and focus on study/investigation of online/real time modeling and control of a CFB with neural networks with specification of:
-       higher neural performance
-       fast convergence
-       computationally less intensive and
-       appreciably accurate

## How the Collaboration Works

**NETL Solid Transfer**
**Research Team**

**NETL CFB Team**

**WVU Tech/DOE**
**EPSCoR CFB**
** ****Effort**

**Industry Implementation/**
**Information Dissemination**

**On-line Modeling and Control Development**

**Implementation Constraints**

**On-line Modeling and Controller Algorithm Implementation**

**CFB Characterization, Model Verification**

## Comment on Partnership

- Interesting (real Problem) & Challenging(Implementation)
- Interaction Between Scientist ( with realistic problem)& Researcher(with theory)
- Excellent opportunity and experience for students ( they see both)