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Computers have had a profound impact on

- automatic control

- automation

- manufacturing

Other uses:

- mathematical modeling

- simulation

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Changing Manufacturing Requirements

and

Green: new addition for 21st Century

Good

Fast any two out of three before 1990,

Cheap

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Feedback Control Is Basic Building Block (Since 1950s)

OutputSetpoint

FeedbackController

ProcessInputs

Process ProcessOutputs

QualityMeasurement

UpdatedProcess State

ObserverFeedbackInformation

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Beginnings of Advanced Process Control (APC)

• First usage of APC was in guidance and control of aircraft/satellites.

• Due to complexity of these systems, PID control was inadequate.

• Digital computer control was required for analysis of the differential equations.

1957 – Sputnik launching

USSR/USA competition in control technology

(Maximum vs. Minimum Principle)

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1960s/1970s – a split developed between “modern” and “classical” control camps

• Time domain vs. frequency domain

• Optimization vs. PID tuning

• Automatic control became an interdisciplinary field.

• PID control was still dominant in process industries.

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Gap Between Control Theory and Practice

• Explosion of information since 1960s

• “You can get 80% of the profit with 20% of the effort”.

• “What can go wrong will go wrong”.

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“The author has been reading the chemical process control literature for over 25 years and in his opinion the vast majority of papers contained little or no material useful in the daily practice of control engineering”. (ca. 1986)

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Why APC Has Not Been Used

• There are very few pilot installations for testing control algorithms.

• Proprietary processes and great variety of processes prevent technology transfer.

• Engineers design safe self-regulatory processes – then use large inventories and blend products.

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• You can’t make any money with APC.• Inter-disciplinary problem – knowledge required

includes control theory, engineering, advanced math, statistics.

• Small yield for effort – plants have other problems to solve that will give more significant increase in production, yield, quality, etc.

• Math model of process required in process control – not easy to get for some processes.

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Three Types of Control (ca. 1975)

1. Feedback

2. Feedforward

3. Divine intervention

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Major Developments Influencing Growth of APC Since 1970s

• Energy crisis

• Distributed control hardware

• Environmental restrictions

• Quality control (international competition)

• Computing speed

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Computers (as of 1960)

Maximum

CoreAverage Storage Add ReadMonthly Capacity Time CardsRental (in 1000 (Micro- Per(1960 $) bits) sec) Min

IBM-7090 55,000 160 0.004 250

CDC-1604 34,000 32 0.005 1300

DEC-PDP1 2,200 4 0.010 (Tape Input)

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Key to Better Controllers?

• Better mathematical models and instrumentation

• Key concept in new generation of feedback controllers – they are

“Model-Based”

• Tuning based on optimization criteria

rather than frequency response but model accuracy is a requirement

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Model Predictive Control (MPC)

• Most widely used multivariable control algorithm in chemical process industries

• Makes explicit use of process model (related to Kalman filter)

• Control actions obtained from on-line optimization (QP)

• Handles process variable constraints• Unifies treatment of load, set-point changes• Many commercial packages

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Desirable Closed Loop Performance

• Tight control about a set point

• Fast, smooth set point changes

• Insensitivity to model errors

• Insensitivity to plant changes

• Ease of on-line tuning

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Early Ideas About MPC

One technique for obtaining a feedback controller synthesis from knowledge of open-loop controllers is to measure the current control process state and then compute very rapidly for the open-loop control function. The first portion of this function is then used during a short time interval, after which a new measurement of the process state is made and a new open-loop control function is computed for this new measurement. The procedure is then repeated.

Lee and Markus (1967)Foundation of Optimal Control Theory

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What is Coming Next?

• Faster hardware – MPC of units with more than 10 inputs and 10 outputs is already established in industrial practice. Larger MPC implementations and faster sample rates will probably accompany faster computing.

• Better MPC algorithms – Improved algorithms could easily have more impact than the improved hardware for the next several years.

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• MPC on the DCS - What will be the ratio of PID to MPC loops if this happens?

• Nonlinear Models - When will control based on nonlinear models become part of industrial practice? The nonlinear MPC theory and algorithms are improving steadily as are nonlinear model identification technologies.

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Figure 19.1 The five levels of process control and optimization in manufacturing. Time scales are shown for each level.

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Supply Chain Management

• Anticipate customer requirements

• Commit to customer orders

• Procure new materials

• Allocate production capacity

• Schedule production

• Schedule delivery

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Process Dynamic Modeling Approaches

• Empirical

• Semi-empirical

• Theoretical/fundamental

• Flowsheet simulator

• Nonlinear/linear

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Nonlinear Response

Reboiler duty fixed

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Some Quotes about Modeling

• All models are wrong but some are useful.

• It is much easier to prove a model wrong than prove it right.

• It is better to be approximately right than exactly wrong.

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• Models that accurately represent the plant over the full operating region are necessary.

• Very high computer speeds are required.

- Dynamic models will need to be run at

50-500 times real time to meet application objectives.

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Improved Instrumentation and Control Technologies

• Nonlinear model predictive control• Process and controller monitoring, fault

detection• Estimation and inferential control• Identification and adaptive control• Plantwide control (design vs. control) • Process sensors• Microfabricated instrumentation• Information and data handling

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Process Control – 21st Century

• Factory of the future

- B.S. engineer/operator

- Nonlinear programming

- Self-tuning controllers

- Data reconciliation, filtering

- Artificial intelligence

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Future DCS Operator

• Requests simulation optimization runs

• Analyzes/implements control moves

• Makes decisions to improve profits

• Maintenance scheduling, shutdown planning

• Analogy to airline pilot (process unit $ > airplane $)

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Manufacturing and Operations in the Future

• Operations are guided by complete information, i.e., integration of sales, marketing, manufacturing, supply, and R&D data accomplished.

• Data and information flow in a seamless fashion along the whole supply chain from raw materials suppliers through all the steps of manufacturing operations to the customer.

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• Computer networks with wireless communication capability connect all components of the supply chain.

• Individuals on a need to know basis will have instantaneous reliable access to data, information, and decision-support tools that will help them to do their job regardless of their geographical location.