AUTOMATION. Dr. Ibrahim Al-Naimi

Transcription

1 AUTOMATION Dr. Ibrahim Al-Naimi

2 Chapter four Industrial Control Systems

3 Process and Discrete Industries Level of automation. Variables and parameters.

4 Continuous and Discrete Variables/Parameters

5 Continuous and Discrete Control System Input parameter (set point ) Controller Control element /Actuator process Output variable Feedback sensor

6 Continuous and Discrete Control System

7 Continuous Control Systems The objective is to maintain the value of an output variable at a desired level (feedback control system). Most Continuous processes consist of many separate feedback loops. Examples: Control the chemical reactions of that depends on temperature, pressure, and flow rate. Control of the position of a work part relative to a cutting tool (x, y, and z coordinate values).

8 Categories of Continuous Control Systems Regulatory Control Feedforward Control Steady State Optimization Adaptive Control

9 Regulatory Control The objective is to maintain process performance at a certain level. Compensation action is taken only after a disturbance has affected the process output.

10 Feedforward Control The strategy is to anticipate the effect of disturbances and compensate for them before they can affect the process. Disturbance Feedforward Control element Input parameters Adjustment to input parameters Process Controller Performance target level Output variables Measured variables Index of performance

11 Steady State (Open Loop) Optimization Control Input parameters Process Output variables Performance measure Adjustment to input parameters Controller (1) Index of performance(ip) (3) Algorithm to determine optimum input parameter values (2) Mathematical Model of process and IP

12 Steady State (Open Loop) Optimization Control System Characteristics: Well defined IP, such as production rate. Known relationship between IP and Process variable. The values of the system parameters that optimize the IP can be determined mathematically. When these characteristics apply, the control algorithm is designed to make adjustment in the process parameters to drive the process toward the optimal state.

13 Steady State (Open Loop) Optimization Control Steady state optimal control works successfully when there are no disturbances that invalidate the known relationship between process parameters and process performance.

14 Adaptive Control Input parameters Process Output variables Performance measure Adjustment to input parameters Modification Measured variables Decision Adaptive Controller Identification Index of performance

15 Adaptive Control Adaptive control combines feedback control and optimal control by measuring the relevant process variables during operation and using control algorithm that attempts to organize some IP. Adaptive control has a unique capability to cope with time varying environment. Adaptive control system is designed to compensate for its changing environment by monitoring its own performance and altering some aspect of its control mechanism to achieve optimal performance.

16 Adaptive Control Adaptive control functions: Identification. Decision. Modification. Example: Adaptive control machining, in which changes in process variables, such as cutting force and power are used to effect control over process parameters such as cutting speed and feed rate.

17 Discrete Control System Combinational Logic Control (Event-driven changes) Sequential Control (Time-driven changes)

18 Computer Process Control Control requirements Capabilities of computer control Forms of computer process control

19 Control Requirements Whether the application involves continuous control, discrete control, or both, there are certain basic requirements that tend to be common for all process control application. These requirements are concerned with the need to communicate and interact with the process in real time basis.

20 Control Requirements Real time controller is a controller that is able to respond to the process within a short enough time period that process performance is not degraded. Real time control usually requires the controller to be capable of multitasking, which means coping with tasks simultaneously without the tasks interfering with one other.

21 Control Requirements Process initiated interrupts (Event driven changes) Depending on the relative importance of the signals, the computer may interrupt execution of current program to service a higher priority need of the process, often triggered by abnormal condition. Timer initiated actions (Time driven changes): The controller must be capable of executing certain actions at specified points in time. Computer commands and process. System and program initiated events. Operator initiated events.

22 Capabilities of Computer Control Polling (Data sampling) Interlocks. Interrupt system. Exception handling.

23 Polling (Data Sampling) Polling refers to the periodic sampling of data that indicates the status of the process. The tend is to shorten the cycle time required for polling Polling frequency. Polling order. Polling format.

24 Interlocks Safeguard mechanism for coordinating the activities of two or more devices and preventing one device from interfering with the other(s).

25 Interrupt System An interrupt system is a computer control feature that permits the execution of the current program to be suspended to execute another program or subroutine in response to an incoming signal indicating a higher priority event. Interrupt conditions: Internal interrupts: generated by the computer itself (time) External interrupts: process/operator inputs (event) A higher priority function can interrupt a lower priority function. A function at a given priority level cannot interrupt a function at the same priority level.

26 Interrupt System Priority Level ( ranking ) Computer Function / Control Function 1 (Lowest priority ) Most operator inputs 2 System & program interrupts 3 Timer interrupts 4 Commands to process 5 Process interrupts 6 (Highest priority ) Emergency stop ( operator input )

27 Exception Handling An exception is an event that is outside the normal or desired operation of the process. Examples: Production quality problem, variables outside normal ranges, shortage of raw materials, hazard conditions, controller malfunction.

28 Forms of Computer Process Control Computer Process Monitoring. Direct Digital Control (DDC). Numerical Control and Robotics. Programmable Logic Controllers. Supervisory Control. Distributed Control System. PCs in Process Control. Enterprise Wide Integration of Factory Data.

29 Computer Process Monitoring

30 Computer Process Monitoring Control remains in the hands of humans. Categories of data collected by the computer: 1. Process data: input parameters, output variables, 2. Equipment data: status of the equipment in the work cell, machine utilization, schedule, tool changes, diagnosis, 3. Product data: maybe required by regulations for the firm own use.

31 Direct Digital Control (DDC)

32 Direct Digital Control (DDC) Improvement to the DDC system include: 1. More control options than traditional analog, such as on/off or nonlinear functions. 2. Integration and optimization of multiple loops. Such as feedback measurements integration. 3. Ability to edit the control programs, more flexibility to reprogram, no need for hardware changes as in analog control.

33 Numerical Control and Robotics Numerical control (NC): a microcomputer directs a machine tool through a sequence of steps defined by a program of instructions. Industrial robotics: the joints of the robot arm are controlled to move the end of the arm through a sequence of positions during the work cycle.

34 Programmable Logic Controller (PLC) Introduced in 1970 as an improvement on the electromechanical relay controllers used to implement discrete control. A PLC is a microprocessor-based controller that uses stored instructions to implement logic, sequencing, timing, counting, etc for controlling machines and processes. It is used for both continuous and discrete control.

35 Supervisory Control It corresponds to cell or system level control (higher level than NC and PLC) It is superimposed on those process-level control systems (NC and PLC). Has economic objectives. Could be regulatory control, feedforward control, or optimal control.

36 Supervisory Control

37 Distributed Control Systems (DCS) Multiple microcomputers are connected together to share and distributed the process control work load. Component and features: Multiple process control stations. A central control room for supervisory control. Local operator stations (for redundancy). Communications network for process and operator stations interaction.

38 Distributed Control Systems (DCS)

39 Distributed Control Systems (DCS) Benefits and advantages of DCSs: Can be enhanced in the future (after installation). Parallel multitasking is possible with multiple computers. It has built-in redundancy. Networking facilities plant management. Example : Multiple PLC s through a factory, connected by network.

40 PCs in Process Control Categories of PC implementations in process control : 1. Operator Interface: The PC is interfaced to one or more PLCs or other devices that directly control the process. Advantages : - The PC is user-friendly. - It can be used for other functions. - The failure of the PC does not disrupt the PLCs functions. - Can be easily upgraded.

41 PCs in Process Control 2. Direct Control: The PC is interfaced directly to the process and controls its operations in real time. Problems : - If the PC fails, the process fails. - PC is not designed for process control. - PC is designed to be used in an office environment. However, There is a trend for PC deployment for direct control due to several factors : Wide spread familiarity with PCs. Availability of high performance PCs.

42 PCs in Process Control Open architecture philosophy in control systems design, which means procuring hardware and software from a diverse pool of vendors; not getting the whole system from the same supplier. Availability of PC operating systems that facilities real-time control, multitasking and networking. Industrial-grade PCs can be used to cope with the harsh factory environment. Data integration is easier using one PC than using a PC and a PLC.

43 Enterprise-Wide Integration of Factory Data: It entails less management levels and more empowerment of front line workers. Enterprise Resource Planning (ERP) is a software that achieves company-wide integration of all business functions, including factory data. A key features of ERP is to use of a singe central database that is accessible from anywhere in the company.

44 Enterprise-Wide Integration of Factory Data: Capability resulting from integrating process data: 1. Managers have direct access to factory operations. 2. Production planners have access to most current data on production to help in scheduling future orders. 3. Sales personnel can provide realistic delivery dates. 4. Customers can track the status of their orders. 5. Quality performance is more predictable. 6. Production cost accounting can be updated. 7. Production personnel have access to product design.