Index 1. UPC and my Thesis work presentation 2. Complex distillation columns with energy savings 3. The work 3.1 Design 3.2 Dynamic aspects 3.3 Control 4. Conclusions and future work

Universitat Politècnica de Catalunya (UPC). Founded in 1971, it has: –9 schools and faculties (Industrial Engineering) –8 technical colleges –7 associate schools –38 departments (Chemical Engineering) –21 diplomas, 8 degrees: students last year –44 Ph.D. programs: 149 thesis during –budget 1998: 260,00 M$can

The Chemical Engineering Department 90 teachers and researchers 95 Ph.D. students Main goals: –chemical process optimisation, security and accident modelisation, reactors, water technology, fluid-particle systems, alimentary technology, waste treatment, contaminants analysis, environmental studies, molecular engineering, polymer synthesis and structure.

The thesis work Title: Energy optimisation in complex distillation columns Objective: study complex designs for energy savings already described to bring them closer to implementation –design, operation and control Status: –Petlyuk Column: centre of my studies till now –some design, some control, some operation –60% of work done

The Petlyuk Column origin Wright (1949) proposed a promising design alternative for separating ternary mixtures Petlyuk (1965) studied the scheme theoretically Most important literature since Petlyuk: Fidkowski and Krolikowski / Glinos and Malone / Triantafyllou and Smith / Kaibel / Wolf and Skogestad

The Petlyuk Column structure

Conventional designs INDIRECT TRAINDIRECT TRAIN

Distillation process in a Petlyuk Column

Petlyuk Column features No more than one component is stripped out in each section, key components A and C: –reversibility during mixing of streams in feed location (pinch zone) –no remixing effect Thermal coupling –no thermodynamic losses in heat exchanges of prefractionator reboiler and condenser –reversibility during mixing of streams at ends of columns Reported 30% of energy savings

The Divided Wall Column Thermodynamical equivalence in only one shell

Extension to other multicomponent distillations A B C D A B C D

Distinguishing features n(n-1) sections required for an n-component separation Only one condenser and one reboiler Key components in each column are not two adjacent ones, but the ones with extreme volatility

Design of the Petlyuk Column Degrees of freedom –design: number of trays per section and feed trays –operation: flowrates or flowrate ratios. Two extra DOF used to optimise the process Main design decision: separation to be carried out by the prefractionator. –Two levels of specification: two specified variables three specified variables Work presented at AIChE Meeting, Los Angeles, 1997

Short-cut methods facing multicomponent systems Most of numerical correlations used by short- cut methods solve distillation columns based on required recoveries of just key components Ability to play only with two recoveries Importance of all three prefractionator recoveries over the global economic performance of a complex distillation column

Proposed design heuristic method Decision of A and C recoveries. Design following short-cut indications (simplified model). Rigorous simulations. Change of feed tray to minimise the larger vapour flow between flows at COL2 bottom and COL3 top Repeat till vapour flows are equal Change recoveries of A and C Balance between prefractionator and main column and between upper and down main column

Simplified model of the Petlyuk Column Work presented at Congreso Mediterraneo de Ingenieria Quimica, 1996

Determination of mixtures that take major profit of the Petlyuk Column Case study with pro-II simulations: –Studied separations: different quantities of B in feed (+33%, 33%, -33%) different Easy Separation Index ( 1) –Savings compared to the best train of columns: more B in feed, more savings (23%, 20 %, 14%) more savings when ESI is close to 1 (34%)

Dynamic behaviour SPEEDUP model Neural Network simulation MATLAB model –linearised model: transfer functions Model approximations –constant relative volatility throughout the column, equimolar overflow, no heat losses equilibrium in each plate, constant pressure, liquid and vapour flow dynamics, tray hydraulics...

Dynamic features Interaction Speed, magnitude and shape of response: stiff

Neural Network simulation - MPC? The used NN –three layer –feedforward with autoregressive neurones connected to the output Sampling frequency from lowest time constant of all outputs: C in feed to B in sidestream, 6 min Training of the NN –PRBS signal applied to all inputs (until 3 manipulated variables and 3 disturbances) Work presented at III Congresso de Redes Neuronais, 1997

NN forecasting example 902 patterns epochs 3, 6, 1 neurons Sigm., linear shift param. = 1 autoregressive param. = 1

Control problem Control product compositions –3 composition specifications (holes in some operation regions) –inventory control Control to minimise energy consumption Robustness? Linearity far from nominal steady state? Disturbances rejection and set point changes achievement?

Descentralised control Skogestad: acceptable control seems feasible (no energy control, linear model) Study of descentralised control with MATLAB models Tyreus method: –Design and test inventory control 7 control valves - 5 steady state DOF = 2 inventory loops –Design composition control –Design optimisation control (energy minimisation) Work presented at CHISA ’98

Diagonal control for the Petlyuk Column Control of A, B, and C purity: For each inventory control (D-B, L-B, D-B) –Transfer function –MRI, CN, Intersivity Index For the decided control structure: D,B; L, S, V –Chose one pairing For the decided pairing: L-A, S-B, V-C –BLT tuning procedure: controller gains: 0.74, -2.33, 0.65 controller reset times: for all loops

(L-A, S-B, V-C) Controlled system MATLAB simulation Set point change in A purity example No instability problem was found, better tunning can be achieved

MIMO feedback control Controllability analysis in frequency domain –bandwidth –RGA, CN, singular values –stability (Nyquist plots) –poles and zeros MIMO robustness

Self-optimising control Published works from NTNU Problem: once the minimum is located, control is required to keep the operating point at the minimum when disturbances are loaded Solution: Improve robustness with feedback control to careful selected outputs Require: measurable output variable which when kept constant keeps minimum energy consumption (self-optimising control) Work to be presented at PRES, 1999

Studied controlled variables for indirect energy minimisation For each candidate, sensitivity to disturbances in feed composition and liquid fraction is computed: –heavy key fraction in vapour leaving top of prefractionator –middle component recovery in prefractionator –main column flow balance –Temperature profile symmetry –others The best?

Conclusions A design method Mixture characterisation for Petlyuk Column Dynamic features NN are able to simulate the Petlyuk Column Diagonal control works in our simplified model Self-optimising control fits the Petlyuk Column

Future work Better characterisation of mixtures fitting different complex distillation columns Other designs to compare with. Energy integration Robustness for different nominal steady-states HYSYS dynamic rigorous simulations Design and control together NN simulation into Model Predictive Control