Brief history of and introduction to process control

Transcription

1 Brief history of and introduction to process control Flavio Manenti CMIC Giulio Natta Dept.

2 Evolution of control theory BC A float regulator was employed by ancient Greece for the water clock of Ktesibios The first automatic feeback controller used in an industrial process was the James Watt s flyball governor, invented to control the speed of a steam engine Mathematical analysis of a feedback system via differential equations by Maxwell 1930s Frequency domain analysis techniques by Nyquist (1932) to explain stability problems. Next, Bode (1964) and Black (1977) Prior to the 1940s The most of chemical processing plants were run essentially manually

3 Evolution of control theory 3 40s (and early 50s) 60s With increasing labor and equipment costs and with the development of more severe, higher-capacity, higher-performance equipment and processes, it became uneconomical and often impossible to run plants without automatic control devices At this stage, feedback controllers were added to the plants with little real consideration of or appreciation for the dynamics of the process itself Rule-of-thumb guides and experience were the only design techniques Chemical engineers began to apply dynamic analysis and control theory to chemical engineering processes. Most of the techniques were adapted from the work in the aerospace and electrical engineering fields The concept of examining the many parts of a complex plant together as a single unit

4 From reacting to predicting technologies 70s Rapid rise in energy prices Additional needs for effective control systems. The design and redesign of many plants to reduce energy consumption resulted in more complex, integrated plants that were much more interacting. The challenges to the process control engineer have continued to grow over the years Richalet et al. (1978) and Cutler and Ramaker (1979) invented the Model Algorithmic Control and the Dynamic Matrix Control, both belonging to the so-called Linear Model Predictive control family 80s and 90s Large diffusion and best industrial practice in oil&gas, refinery, and petrochemical processes 4

5 Recent advances and next trends 5 Late 90s, 2000s Increase in computational power, parallel computing Diffusion of the so-called nonlinear model predictive control (NMPC) techniques The intrinsic nonlinear behavior of chemical processes leads to larger tangible benefits using the NMPC What next? Real-time dynamic optimization Dynamic model-based scheduling and planning of the production Receding/rolling moving horizon methodologies to handle market uncertainties Business-wide and enterprise-wide process control Global dynamic optimization Self-optimizing control Up to the next process control engineers/scientists

6 Introduction to process control 6 con trol transitive verb, 1. to check, test, or verify by evidence or experiments, 2. to exercise restraining or directing influence over. noun, 1. an act or instance of controlling; also: power or authority to guide or manage, 2. a device or mechanism used to regulate or guide the operation of a machine, apparatus, or system There are three general classes of need that a control system is called to satisfy: suppressing the influence of external disturbances, ensuring the stability of a chemical process, optimizing the performance of a chemical process George Stephanopoulos The best way to illustrate what we mean by process dynamics and control is to take a few real examples William Luyben Basic examples: Gravity-flow tank Heat exchanger Chemical plant

7 Gravity-flow tank 7 Atmospheric tank Incompressible (constant density) liquid is pumped at a variable rate F 0. This rate can vary with time because of changes in the upstream operations h is the height of liquid in vertical cylindrical tank F is the flow rate exiting the tank Time-dependent: F 0(t), h (t), F (t) Liquid leaves the base of the tank via a long horizontal pipe and discharges into the top of another atmospheric gravity-flow tank F 0 h F F

8 Gravity-flow tank 8 Steady-state conditions: By steadystate we mean, in most systems, the conditions when nothing is changing with time Mathematically, this corresponds to having all time derivatives equal to zero At steady-state, the flow rate out of the tank must equal the flow rate into the tank: F0 F A certain h corresponds to a given F. The value of h would be that 0 height that provides enough hydraulic pressure head at the inlet of the pipe to overcome the frictional losses of liquid flowing down the pipe. The higher the inlet flow rate, the higher the liquid level will be F 0 h F F

9 Gravity-flow tank 9 Tank design: Traditionally based on steady-state considerations and models The design of the system would involve an economic balance between the cost of a taller tank and the cost of a bigger pipe, since the bigger the pipe diameter the lower is the liquid height [m] h 120% h F 0 120% F 0 [kg/h] But when F 0(t) varies, what path will be followed by the liquid level to get to the new steady-state?

10 Gravity-flow tank 10 Considering the process dynamics after a step change in the inlet flowrate, from a certain value to the maximum value considered in designing the tank, the liquid level however exceeds the tank height: Speed [ft/s] Liquid Level [ft] Volumetric Flowrate 5.5 "Res.ris" u 1: time [s] Tank Level time [s] "Res.ris" u 1:3 Not consistent with the real behavior 120% F 0 Maximum flow entering the tank

11 Heat exchanger 11 An oil stream passes through the tube side of a tube-in-shell heat exchanger and is heated by condensing steam on the shell side The steam condensate leaves the heat exchanger through a steam trap Objective: control the temperature of the oil leaving the heat exchanger Steam Valve F S Oil feed Heat exchanger F, T 0 T TC TT Controller Trasmitter [ma signal] Thermocouple [mv signal] TRAP

12 Chemical plant 12 Feed tank component A Condenser CW Feed tank component B Overhead product Reflux drum Feed pump CSTR Bottom cooler CW Reflux Feed preheater CW Distillation column Reboiler Steam Bottom product

13 Chemical plant 13 Feed tank component A FC PC PC Condenser CW Feed tank component B FC Overhead product LC Reflux drum Feed pump TC CSTR Bottom cooler LC CW TC Reflux Feed preheater CW TC Distillation column Reboiler FC Steam LC Bottom product

14 Chemical plant 14 We introduced the minimum amount of controls that would be needed to run this plant automatically without constant operator attention Even in this simple plant with a minimum of instrumentation, the total number of control loops is 11 The most chemical engineering processes are multivariable

15 General concepts to be familiar with Dynamics: time-dependent behavior of a process The behavior with no controllers in the system is called the openloop response The dynamic behavior with feedback controllers included with the process is called the closedloop response Variables Manipulated variables: typically flow rates of streams entering or leaving a process that we can change in order to control the plant Controlled variables: flow rates, compositions, temperatures, levels, and pressures in the process that we will try to control: either trying to hold them as constant as possible or trying to make them follow certain desired time trajectory Uncontrolled variables: variables in the process that are not controlled Load disturbances: flow rates, temperatures, or compositions of streams usually entering the process We are not free to manipulate them 15

16 General concepts to be familiar with They are set by upstream or downstream parts of the plant. The control system must be able to keep the plant under control despite the effects of these disturbances Example: distillation column 16 LOAD DISTURBANCES Feed flow rate F Feed composition z Distillate composition x D Bottom composition x B Level reflux drum M R CONTROLLED VARIABLES Level base M B Reflux flow rate R Pressure P Reboiler duty Q R MANIPULATED VARIABLES Distillate flow rate D Bottom flow rate B CW flow rate F W Tray N temperature Tray 1 temperature UNCONTROLLED VARIABLES

17 General concepts to be familiar with Feedback control The traditional way to control a process is to: 17 measure the variable that is to be controlled compare its value with the desired value (called the setpoint to the controller) feed the difference (the error, the deviation) into a feedback controller that will change a manipulated variable to drive the controlled variable back to the desired value The information is thus feed back from the controlled variable to a manipulated variable Disturbance Manipulated variable Control valve PROCESS Controlled variable FEEDBACK CONTROLLER Setpoint MEASUREMENT DEVICE

18 General concepts to be familiar with Feedforward control The disturbance is detected as it enters the process and an appropriate change is made in the manipulated variable such that the controlled variable is held constant Thus, we begin to take the corrective action as soon as a disturbance entering the system is detected (instead of waiting for the disturbance, as we do with feedback control) 18 Disturbance Manipulated variable Control valve PROCESS Output MEASUREMENT DEVICE FEEDFORWARD CONTROLLER Setpoint

19 General concepts to be familiar 19 with Stability A process is said to be unstable if its output becomes larger and larger (either positively or negatively) as time increases No real system really does this, since variables can move within certain constraints A linear process is at the limit of stability if it oscillates, even when undisturbed, and the amplitude of the oscillations does not decay Most processes are openloop stable All the real processes can be made closedloop unstable (if the controller gain is made large enough) Thus, stability is of vital concern in feedback control systems The performance of a control system is the ability to control the process tightly It usually increases as we increase the controller gain (within the stability limits) The robustness of the control system is the tolerance to changes in process parameters. It decreases when a small change will make the system unstable

20 Certain basic considerations 20 First: The simplest control system that does the job is the best Complex elegant control systems look great on paper but soon end up on manual in an industrial environment Bigger is definitely not better in control system design Second: You must understand the process before you can control it The use of complex controllers do not lead to overcome ignorance about the process fundamentals Learn how the process works before you start designing its control system

21 Course program 21 Nonlinear Systems Optimizers Differential systems Stiff systems ODE,DAE,PDE,PDAE Efficiency Solvers Mathematical Modeling DCS, OTS, Plantwide control, Soft sensing, process transients, grade/load changes Dynamic Simulation Optimization Parallel Computing Economy Just in time Market-driven Logistics Corporate Supply Chain Planning Scheduling Dynamic optimization Distributed predictive control Data Reconciliation Outlier Detection Robust methods Linear/nonlinear Regressions Performance Monitoring Yield Accounting Soft sensing Raw Data Model Reduction Decisions Model Predictive Control Uncertainties Optimal production Optimal grade changes Multi-objective Real-time optimization High accuracy Reliable process control Production improvement

22 Distributed control system (DCS) 22 The information is transferred from the field to the control-room (and decisions are sent back to the field) through different steps: Instrumentation installed by the field Thermocouples, flow and pressure measurements, analyzers Field barriers (junction boxes) Fieldbus Redundancy, ring, switches Marshalling (technical room) Cabinets, racks, I/O modules, CPU slots, redundancy, internal redundancy Servers OPC, web-server, operations/alarm, engineering, diagnostics, alarm management, archive/historian, HMI, synchronization Clients (Software) Data processing, steady-state simulators, dynamic simulators, soft sensing, data reconciliation, maintenance scheduler, advanced process control, model predictive control, process optimization, dynamic optimization, supply chain management, production accounting, blending, operator training simulators, performance monitoring, enterprise resource planning, movement tracking

23 Distributed control system (DCS) 23 Courtesy of S. Cavagnaro and S. Garramone, SIEMENS GmbH The general architecture: Office LAN (Ethernet) WEB Client IT CLIENT Client Station OS Server CPU WEB Server OS LAN (Ethernet) Industrial Ethernet ROUTE Control BATCH Server CPU CAS Engineering / Maintenance Station SERVER FIELD CPU CPU BUS CPU BUS CPU Fieldbus Fieldbus Filedbus

24 Application server Courtesy of G. Bussani, HONEYWELL Inc 24

25 Control areas Courtesy of G. Bussani, HONEYWELL Inc. 25 TECHNICAL AREA FIELD INSTRUMENTATION FIELD MARSHALLING TECHNICAL AREA DCS FUNCTIONS

26 Generations of DCS Courtesy of S. Cavagnaro and S. Garramone, SIEMENS GmbH 26 Classical Next Generation OPC Web Server (Java) Operation/ Alarm Engineering Diagnostic AMS Archive Engineering

27 Simpler and safer architecture 27 Courtesy of S. Cavagnaro and S. Garramone, SIEMENS GmbH Remote Clients Enterprise Level Remote Thin Clients Control Room Thin Clients OPC System SW Web Server (Java) System SW Operation/ Alarm Data System SW Engineering Data System SW Diagnostic Data System SW AMS Data System SW Archive Data System SW Built-in Web Server OPC Server AMS Integrated Java / xml Integrated Automation SIS Controls Integrated IEC Dual Path and Media Redundant Field

28 IT and security 28 Courtesy of S. Cavagnaro and S. Garramone, SIEMENS GmbH Wireless infrastructures Secure access (local commissioning, diagnostics, and maintenance; cyber security; intrusion detection and prevention) Access Points Wireless Clients Thin Clients DMZ HMI level Firewall/Router Proxy Module Terminal Server Firewall Intranet DMZ HMI level Firewall/ VPN Router Terminal Server Firewall OPC Tunnel VPN Tunnel Internet Thin Client Siemens Remote Service Application Application Server Automation Automation Server IO modules

29 Service and maintenance Courtesy of S. Cavagnaro and S. Garramone, SIEMENS GmbH 29 Online communication Know-how as if we were there Field Engineer On standby for all events Regional service centers Your local task force at your site Remote expert Center Experts available any time Spare parts logistics The key to increase availability 24 hours a day 365 days a year Wherever needed

30 Cutting edge solutions Courtesy of M. Rovaglio, INVENSYS ltd SCHNEIDER ELECTRIC GmbH DCS are continuously and fast evolving (i.e. new trends in controlroom and field operator training) 30