Modeling & control of Reactive Distillation Jianjun Peng Supervisors: Dr. Edgar Dr. Eldridge

Outline Background information Research objectives Modeling Experimental plans Conclusions 02/19/2001 TWMCC

Reactive Distillation: Example A+B Conventional process C A, B B A reactor E A,B,E A C B Reactive distillation Reactive section 02/19/2001 TWMCC

The Good vs. the Bad The good + higher conversion + reduced capital cost, energy The bad - more difficult to design & control - poor understanding of the process 02/19/2001 TWMCC

AspenPlus Simulations AspenPlus Radfrac Equilibrium model Tert-Amyl Methyl Ether (TAME) system Steady state Objective: How different is reactive distillation comparing to ordinary distillation? 02/19/2001 TWMCC

Reflux Ratio Influence Modeling & Control of Reactive Distillation Reflux Ratio Influence Reactive distillation is quite different from ordinary distillation because of the complex interactions between mass transfer and reaction. The process model is highly nonlinear with multiplicity. Therefore it is quite difficult to model, design and control the process. There are growing industrial interests in reactive distillation. Several commercial applications of reactive distillation are very successful and there are many potential applications. Rate-based modeling is a new way to model chemical processes. It has several advantages over the traditional equilibrium modeling. However, the model is more complex and more different to solve efficiently. NMPC can handle nonlinearity, multiplicity and constrains and therefore is an attractive strategy for controlling reactive distillation. Model reduction and efficient optimization techniques are needed. There are very few experimental data in the open literature. Therefore the experimental data in this work will be very useful for model and controller validation and improvement, and for understanding the reactive distillation process in depth. 02/19/2001 TWMCC

Modeling & Control of Reactive Distillation Pressure Influence Reactive distillation is quite different from ordinary distillation because of the complex interactions between mass transfer and reaction. The process model is highly nonlinear with multiplicity. Therefore it is quite difficult to model, design and control the process. There are growing industrial interests in reactive distillation. Several commercial applications of reactive distillation are very successful and there are many potential applications. Rate-based modeling is a new way to model chemical processes. It has several advantages over the traditional equilibrium modeling. However, the model is more complex and more different to solve efficiently. NMPC can handle nonlinearity, multiplicity and constrains and therefore is an attractive strategy for controlling reactive distillation. Model reduction and efficient optimization techniques are needed. There are very few experimental data in the open literature. Therefore the experimental data in this work will be very useful for model and controller validation and improvement, and for understanding the reactive distillation process in depth. 02/19/2001 TWMCC

Product Rate Influence Modeling & Control of Reactive Distillation Product Rate Influence Reactive distillation is quite different from ordinary distillation because of the complex interactions between mass transfer and reaction. The process model is highly nonlinear with multiplicity. Therefore it is quite difficult to model, design and control the process. There are growing industrial interests in reactive distillation. Several commercial applications of reactive distillation are very successful and there are many potential applications. Rate-based modeling is a new way to model chemical processes. It has several advantages over the traditional equilibrium modeling. However, the model is more complex and more different to solve efficiently. NMPC can handle nonlinearity, multiplicity and constrains and therefore is an attractive strategy for controlling reactive distillation. Model reduction and efficient optimization techniques are needed. There are very few experimental data in the open literature. Therefore the experimental data in this work will be very useful for model and controller validation and improvement, and for understanding the reactive distillation process in depth. 02/19/2001 TWMCC

Research Objectives Dynamic model - for the purpose of control Model predictive control - PID may not be adequate Controller implementation - pilot plant with Delta V control system Experimental validation 02/19/2001 TWMCC

Equilibrium Models Vapor-liquid equilibrium at each stage (section for packed column) Tray efficiency or HETP One mass balance for each stage 02/19/2001 TWMCC

Rate-based Models Mass transfer equations Vapor-liquid equilibrium only at interface Transport properties - mass transfer coefficients - heat transfer coefficients NO tray efficiency or HETP 02/19/2001 TWMCC

Rate-based Models(2) Vk yi,k Lk-1 xi,k-1 fLi,k fVi,k Vapor Liquid Catalyst N, E N, E QVk Vk+1 yi,k+1 Lk xi,k QLk 02/19/2001 TWMCC

Equilibrium or Rate-based? Equilibrium models Rate-based models - Not rigorous + Rigorous + Simple - Complicated ? Tray efficiency or HETP ? Mass transfer 02/19/2001 TWMCC

Model Comparison Jin-Ho Lee etc. (1998) - individual efficiency hard to predict - rate-based model is preferred R. Baur (2000) - smaller window for multiplicity in rate-based model - rate-based model is preferred No experimental validation No details about mass transfer No details about the behavior of reactive distillation 02/19/2001 TWMCC

Modeling Mass transfer - Maxwell-Stefan equations - Overall mass transfer? - Empirical mass transfer coefficients Reaction - heterogeneous or pseudo-homogeneous? Dynamics - vapor holdup? - energy holdup? 02/19/2001 TWMCC

Model Assumptions Overall mass transfer Pseudo-homogeneous reaction Pseudo-steady state energy balances negligible vapor holdup 02/19/2001 TWMCC

Model Solution Aspen Custom Modeler (ACM) * custom models * built-in DAE solvers * built-in property models * integrated PID controllers * modeling language 02/19/2001 TWMCC

Simulation Plans Comparison with equilibrium model Comparison with more rigorous rate- based model (Sebastien Lextrait) Parameter influence - reflux ratio, boil-up ratio, pressure, feed composition Dynamic response - feed, reflux ratio, boil-up ratio 02/19/2001 TWMCC

Experimental Plans 6 inch reactive distillation pilot plant Catalytic Packing Structured Reactive Distillation Column 6 in. diameter 34 ft. T-T Backcracking Reactor 6 inch reactive distillation pilot plant TAME system Experiments - steady state - dynamic - controller implementation 02/19/2001 TWMCC

Future Work Solving the model with ACM Simulations and comparisons Experiments: steady state and dynamic MPC and NMPC implementation 02/19/2001 TWMCC

Concluding Remarks Reactive distillation is advantageous, but poorly understood. A dynamic rate-based model has been developed. Future contributions Solving the rate-based dynamic model Controller development using simulations MPC implementation on Delta V Experimental validation 02/19/2001 TWMCC