Effectively Addressing Control Applications Title and description should match the submitted abstract information. The Emerson Exchange logo should not be modified.

Presenters Terry Blevins James Beall Insert the presenters and their company logos in the order that they will present.

Typical Single Input-Output Control Loop

Agenda Measurements – How to avoid signal aliasing The impact measurement status and selected status options on control. Single loop control –selection of structure based on application requirements, use of PID notch gain option, providing quick recovery from startup conditions. Application of output characterization to provide linear installed characteristics. Feedforward – Use of PID feedforward. Constructing a summing or multiplying feedforward outside the PID block, advantages and disadvantages. Implementation and commissioning of dynamic compensation.

Agenda (Cont) Override Control - Implementation and commissioning of override control using PID’s and control selector Cascade control – use of PID dynamic reset option to improve performance. Implementing MPC cascade. Ratio control – Using the Ratio block based on SP or wild flow. Adjustment of Ratio target, impact of options. Split range control and valve position control – Implementation using standard blocks, impact of options. Duty cycle and increase open/close actuation – Use of the AO with a DO channel to achieve precisely timed on/off actuation. Fuzzy Logic Control for loops that are lag dominant. DeltaV Predict MPC block in control applications – addressing difficult dynamics, impact on single loop and override control applications.

Processing Analog Inputs Two-pole hardware filter with cutoff point (-3db) at 2.7 Hz DeltaV Analog Input Module Digital Filtered Value to Controller Configurable Software Filter A/D Analog-to-digital Conversion (16 bit) Every 22 milliseconds Traditional Transmitter (4-20ma or Hart)

I/O Module Software Filter Software filtering may be applied at the I/O module to avoid aliasing. Only required if the 4-20 ma signal contains frequencies > 1/2X the control module execution rate - Value sampled by the control module - Aliased signal as see by the control module - Actual signal (with noise removed)

Filter Guideline

Analog Measurement Limit and Bad Status High or low limit is set in status if the measurement exceeds the over-range and under-range values specified for the channel The status quality will be set to BAD if the measurement excees the A/D range (open or short condition).

Adjusting Over & Under Range Detection

Analog Input Block

Analog Input Block (Measurement) 100 % FILTER 0 % SIMULATE OUT 100 % 0 % SIMULATE (VALUE +STATUS) AUTO MANUAL VALUE CONVERT SQ. ROOT LOW_CUT L_TYPE MODE XD_SCALE OUT_SCALE PV PV_FTIME (Measurement) UNIT FILTER SIMULATE_IN 29 29

Status Provided by the Analog Input A status quality of Uncertain or BAD may be created under limit conditions or Man Mode based on STATUS_OPT parameter selections. Status is set to BAD if the block mode is set to Out-of-Service.

AI Status Options

PID Block

PID Function Block + Note: For simplicity, not all modes are shown. CALC OUT CAS_IN IN SP FF_VALUE TRK_IN_D TRK_VAL MODE + BCKCAL_IN BCKCAL_OUT GAIN, RESET, RATE PV_SCALE C A 100 % 0 % OUT_HI_LIM OUT_LO_LIM BYPASS FF_SCALE TRK_SCALE OUT_SCALE LIMIT OUTPUT FF_GAIN Note: For simplicity, not all modes are shown. 38 38

Impact of IN Status Status_OPTS determine if control will continue with Uncertain status. The recommended default is Use Uncertain as Good If status is BAD, then actual mode will automatically go to MAN. An IN limit status of Constant will automatically cause the reset action to hold last value (to prevent windup under this condition).

PID Function Block Algorithm Either Series or Standard form may be selected using the FORM parameter. The default is Standard. Response is identical of either selection if rate action is not used. Whether proportional and derivative action are taken on error or PV value may be selected using the STRUCTURE parameter.

Selection of PID Form

Differences Between Forms Standard and Series have identical response for Proportional and Integral tuning (0 derivative). The Series form allows derivative (rate) and integral (reset) to be changed independently i.e. non-interacting. The Standard Form is capable of a more flexible response

PID STRUCTURE Parameter

PID STRUCTURE Parameter Example of I on error, PD on PV Change setpoint without a large disturbance in the manipulated variable (output) Level of a feed tank Base level of a column that feeds another column

PID STRUCTURE Parameter Reactor feed tank: PI on error, D on PV Controller Output – Flow to reactor

PID STRUCTURE Parameter Reactor feed tank: I on error, PD on PV Controller Output – Flow to reactor

Adjustment of Beta & Gamma If two degrees of freedom is selected as the PID structure, then BETA and GAMA may be adjusted to determine the fraction of proportional and derivative action on error vs. PV.

General Block Diagram Of PID Reset created by positive feedback network. Automatically provides anti-reset windup protection

External Reset The FRSI_OPTS for “Dynamic Reset Limit” may be selected to enable external reset; i.e., the use of BKCAL_IN in the reset calculation. When this option is selected, then the downstream block’s CONTROL_OPTS should have “Use PV for BKCAL_OUT” enabled.

Enabling PID External Reset Utilized most often in the primary loop of a cascade Automatically compensates for poor secondary loop response

Improving Process Recovery From Saturated Conditions On recovery from a saturated condition, when the ARW_HI_LIM and ARW_LO_LIM are set inside the OUT limits, the reset will automatically be increased by 16X until the OUT parameter comes back within the the ARW limits or the control parameter reaches setpoint.

Setting ARW limits SP PV ARW_LO_LIM OUT OUT_LO_LIM

Eliminating Reset Action When Control Error is Small By setting the IDEADBAND parameter to a positive value, then reset action is held once control error is reduced below this limit.

PID Non-Linear Gain Modifier

Mode Determines the Source of Output & Setpoint SP Value Setpoint Value BLOCK AlGORITHM Operator Entry Block Algorithm (internal) A B C D Output Value Input Parameters (value + Status) (Operator Entry ROUT_OUT Select RCAS_IN CAS_IN ROUT_IN TRK_IN CAS_OUT Back-calculation Output Parameter OUT RCAS_OUT

Mode Parameter Attributes Target is the requested mode set by operator Permitted (configured) is the selection available Actual is the achievable mode given inputs status and target mode Normal (configured) is the designed mode

Rcas & Rout Modes If the RCAS_IN or ROUT_IN parameter is not updated within a time defined by SHED_TIME when in Rcas or Rout mode respectively, then the actual mode of the associated block will “Shed” based on the configured permitted MODE and the SHED_OPT parameter.

Shed Options for Rcas & Rout Modes

AO Block

Analog Output Block (Output) (Valve Position) HI/LO RATE CONVERT LIMIT CAS_IN CONVERT PV_SCALE XD_SCALE MODE OUT SIMULATE CAS AUTO SP BCKCAL_OUT Readback PV (Output) (Valve Position) HI/LO LIMIT RATE IO_OPTS 33 33

I/O Options in the AO Block The Increase to close option should be set to account for field reversal so that the SP value always indicates “implied” valve position.

AO Setpoint Rate Limits The AO setpoint rate limits apply even when the block is in CAS mode (as well as Auto). This feature may be use to limit the maximum rate at which a valve is change in automatic control. BKCAL_OUT status is set to limited if changes in OUT are limited. This prevents the PID from winding up under these conditions.

Signal Characterization in Control Path

Using AO for Duty Cycle Control Percent time on (ON_TIME) over the duty cycle period is determined by AO setpoint. Automatically repeats at end of duty cycle using current value of ON_TIME High resolution of on-off time ON OFF % time on Duty Cycle Period

Duty Cycle Control – DO Setup Typical application is manipulation of heater band input for extruder temperature control Percise adjustment to ½ of 60 hz since timing is done by DO card hardware Percent time on is determined by AO output configured to reference the discrete channel

Duty Cycle Control – DO Setup Period of duty cycle is determined by PULSE_PERIOD configured for the DO channel Period should be set to match the period of execution of the module that contains the AO block that references the DO channel

Duty Cycle – Configuring AO When you browse to a DO channel when configuring the AO block, the only selection is ON_TIME

AO With Increase-Decrease Actuator Continuous Pulse Output Channels

Proper Controller Tuning Is the fastest, quickest, and least expensive improvement one can make in the basic control system to decrease process variability. The detrimental effects of disturbances, interactions, and control valve dead band are minimized by an appropriate selection of tuning. There is always a tradeoff between performance and robustness.

Uses State-of-the-Art Technology Process Identification Based on Relay Self-Oscillation Principle Applicable to a wide range of processes Slow Fast Self-regulating Integrating Immune to Noise and Process Load Disturbances Minimizes Tuning Time

Multiple Input - Single Output (MISO) Control A multiple input - single output controller uses process inputs and outputs not included in a single input- single output ( SISO) controller to improve control response to disturbance and enforce operating constraints. Information needed to Tune Controller Feedback Controller (SISO) Controlled Manipulated Process Constraint Measured Disturbance (Other) (Other)

Types of Process Variables Manipulated - process input which is adjusted to maintain a controlled output at setpoint. Controlled - process output which is to be maintained at a specific value; i.e., the setpoint Disturbance - measured process input which may also affect the value of controlled outputs Constraint - process output which must be maintain within an operating range by restricting the adjustment of manipulated inputs.

First Order Plus Deadtime Process response exhibits the combined characteristics of the lag and delay response. O2 O1 63.2% (O2 - O1) O2 - O1 Input Output Gain = I2 - I1 I2 Dead Time = T2 - T1 I1 Time Constant ( T ) = T3 - T2 T1 T2 T3 Time

Integrating Response Process output changes without bound when the process input is changed by a step. O1 Input Output I2 I1 Time

Feedforward - MISO Controller By immediately correcting for a measured load disturbance through adjustment of the manipulated input, the control performance may be improved by feedforward control. Information needed to Commission Feedforward L/L & DT Feedback Controller Dynamic Compensation + + L/L Process Manipulated Control DT Measured Disturbance

Feedforward Control Measurable Disturbance

Setup of L/L Block for Dynamic Compensation Set the LEAD_TIME to Tm and the LAG_TIME to Td. The gain of the L/L should be -(Load Dist Gain/Manip Gain).

Setup of Deadtime Block for Dynamic Compensation The DEAD_TIME parameter should be set to a value of DT2 - DT1.

External Feedforward - Bias Feedforward continues even if PID is in Man mode PID correction is limited by its output limits

External Feedforward - Bias(Cont) Act on IR should be selected to allow the bias (SP) to be initialized on transition from IMAN to CAS actual mode.

External Feedforward - Ratio Feedforward continued even if PID is in Man mode Ratio correction by PID is limited by its output limits

Override - MISO Controller Automatic regulation of the process input to maintain one process output at target without violating a constraint on another output is provided by override control. Max Value Override Controller Information Needed to Tune Controllers < Controller Manipulated Process

Over-ride Control Control Meas Constraint Meas Unmeasured Disturbance

Cascade Control Secondary Primary Controller Controller Process 2 Manipulated Disturbance Disturbance

Cascade Control Sec Meas Pri Meas Unmeasured Disturbance

Cascade –Dynamic Reset Select FRSIPID_OPTS for Dynamic Reset Limit in the primary of the cascade to automatically compensates for poor response of the secondary loop. The CONTROL_OPTS in the secondary must be set for Use PV for BKCAL_OUT for Dynamic Reset Limit to provide benefit.

Split Range Control Sec Meas Unmeasured Disturbance

Split Range Control AI PID SPLT AO AO TIC 104 FY 104 IP 104A IP 104B TT104 TIC104 IP104A FY104 AO IP104B TIC 104 FY 104 IP 104A IP 104B TT 104 HEATER COOLER 44 44

Split Range Output (FY104) Valve Position (% of Span) TIC104 Output (% of Span) 100 Cooling (IP104B) Heating (IP104A) TK 23 45 45

Splitter Block OUT_1 OUT_2 SP 100 LOCK_VAL “holds ” “is zero OUT_ARRAY 100 OUT_1 OUT_2 LOCK_VAL “holds ” “is zero OUT_ARRAY 0 100 0 100 IN_ARRAY 0 100 0 100 100 0 0 100 0 40 35 100 47

Splitter Block Total Cv and gain of 0-50, 50-100% split ranged valves. 47

Splitter Block Total Cv and gain of 0-50, 25-100% split ranged valves. 47

Ratio Control IN_1 may be a flow measurement (wild flow) or setpoint of another loop Ratio based on SP will not reflect deviations in flow from setpoint (resulting in incorrect ratio)

Addressing Lag Dominated processes – Fuzzy Logic Control The greatest advantage of fuzzy logic control is evident when it is applied to processes with insignificant dead time in order to accelerate the speed of control while retaining a high-quality level of control. Where PID controller does not meet expectations, the fuzzy logic control may be considered as an alternative.

Fuzzy Logic Control vs PID Setpoint Change Load Disturbance FLC FLC PID PID FLC FLC PID PID Notice at both SP change and at load disturbance the FLC output change is more dramatic than PID. Resulting in faster return to SP. Also notice as PV approaches SP, the FLC exhibits less overshoot.

DeltaV Fuzzy Implemented As A Function Block Pre-defined Fuzzy Logic Control

Addressing Difficult Dynamics and Process Interaction - Model Predictive Control Fully integrates DeltaV Historian and off-line process identification setup is trivial Model can be easily updated. MPC with DeltaV is easy

MPC -Addressing Difficult Dynamics TemperatureProcess (1X1) MPC

MPC -Addressing Difficult Dynamics

MPC -Addressing Difficult Dynamics

Automated process testing to identify the process model

Step Response Model

Verification of identified model

Testing of control using simulated environment

Operator interface to MPC Future Values of control

MPC Replacement for PID with Feedforward Process (2X1) MPC Measured Disturbance

MPC Replacement for PID with Feedforward

MPC Replacement for PID Overrride Process (1X2) MPC Constraint UnmeasuredDisturbance

MPC Replacement for PID Overrride

Addressing Process Interaction Process (2X2) MPC

Addressing Process Interaction

Addressing Process Interaction

Layering MPC on Existing Strategy RCAS_IN RCAS_OUT PID AO AI Process (1X1) Unmeasured Disturbance

Exposing RCAS_IN & RCAS_OUT Right click on the control or AO block to expose the RCAS_IN as an Input parameter and RCAS_OUT as an Output parameter.

Layering MPC on an Existing Strategy

Example DeltaV Predict Installations Control Application Industry Location Evaporator Chemical South Africa Lime Kiln Pulp&Paper Canada Pipeline Gas Blending Power Florida Bleach Plant Pulp&Paper Canada pH Control Pulp&Paper US Distillation Column Pharmaceutical Puerto Rico

Summary The function blocks in DeltaV may be used to address plant requirement. The DeltaV MPC and Fuzzy Function blocks may be used to better address difficult dynamic and process interaction Your feedback on this presentation and input on future control topic you would like to see presented at Emerson Exchange are always appreciated. terry.blevins@EmersonProcess.com james.beall@EmersonProcess.com

Learning More About DeltaV Advanced Control Book was inspired by DeltaV Advanced Control Products. This book was introduced at ISA2002 may also be ordered through ISA, Amazon.com or at EasyDeltaV.com/Bookstore The application sections include guided tours based on DeltaV Advanced Control Products CD provides an overview video for each section and examples. Copies of the displays, modules, and HYSYS Cases are included on the CD.

DeltaV Predict and other DeltaV Advanced Control Products Overview - Courses 7201, 7202, & 7203 These courses, beginning with the 7201, overview all of the major DeltaV advanced control tools. Courses 7202, & 7203 each drill deeper into a specific advanced control product and its application. DeltaV advanced controls are unique in the process control industry, in that users do not need detailed knowledge of the underlying mathematical principles to successfully apply the DeltaV advanced controls technology. Course # 7201 DeltaV Advanced Controls Overview Course # 7202 Course # 7203 DeltaV PredictPro DeltaV Neural Implementation Implementation

Emerson Exchange – Advanced Control Presentations Effectively Addressing Control Applications Monday 1:00-2:45, 3:00-4:45, Dallas 5-6-7 Addressing Multi-variable Process Control Applications Tuesday 8-10, Grapevine D Lime Kiln Optimization Using Supervisory APC – “Model Predictive Control” & DeltaV Wednesday 3:25-4:10, Texas 4 Adaptive Control – Better Control with No Tuning!, Thursday 1:40 – 2:25 Grapevine A DeltaV and Model Predictive Control - A primer on DeltaV Predict and PredictPro Thursday 8:40 – 9:25 Grapevine A Field Experience in Property Estimation, Friday, 8:00 - 9:40am & 9:55 - 11:35am Texas 5 Utilizing adaptive Control Friday, 8:00 - 9:40am & 9:55 - 11:35am Texas 4