Tuesday 14th March 2017 – Talk – Industrial Process Automation; Skimming the Surface of 40 Years
Dr David James RCEA
David started his talk by giving a quick background to his career and interests. Initially, he trained as a physicist, went on to become a Chartered Engineer and gained a PhD in Business Research. He had worked on nuclear reactor design (both submarine and civil) before moving on process automation. He is Chairperson the IET Sussex Local Network and a STEM Ambassador. His hobbies include building and flying radio controlled model aircraft, as well as restoring and driving a classic MG.
When one looks at automated processes, they can be divided into two main types: Assembly Processes and Industrial Processes. Assembly processes manufacture a product that can subsequently be disassembled; they generally use robots or ‘pick and place’ machines. Examples include assembly of TVs, cars and phones. Industrial Processes, on the other hand, cannot easily be ‘undone’. Fluids and/ or powders are piped around the process plant and subjected to chemical, blending or separation processes. Examples include the production of plastics, soup, medicines, petrol and chemicals.
Industrial processes in turn fall into two main types: Continuous Production, which uses a constant feed of raw materials with the aim of achieving a ‘steady state’ output. Examples include refineries and bulk chemical production. The accompanying diagram shows a crude oil distillation process.
Sensors and Actuators
In general terms, sensors measure a parameter such as temperature, level, flow rate, pressure or pH; they pass the information to a control system which is used to vary the demand signal to an actuator such as a valve or motor.
• Temperature sensors include devices such as thermocouples or resistance temperature detectors (RTDs):
Batch Production processes are designed to make a specified quantity of a product and are similar to using a recipe to cook food: the process has a list of ingredients and a production procedure. It is used for products such as cheese or pharmaceuticals – the diagram shows a process for producing aspirin.
• Flow meters include orifice plates and turbine meters:
• Valves can include solenoid or pneumatically operated designs:
• Motors may be on/off or variable speed, and may drive devices such as pumps or agitators:
Control/ Monitoring Systems
These may vary from basic, manually controlled systems through simple, single loop controllers to more complex, distributed control systems such as SCADA (Supervisory Control And Data Acquisition) using computers, networked data communications and Graphical User Interfaces (GUIs) for high level process supervisory management. An example of a SCADA system is shown in the accompanying diagram.
Operator Interfaces and Process Alarms
These will vary widely depending on the complexity of the process being monitored and controlled. Two interfaces and a relatively simple process alarm are shown below.
Single loop controllers can use feedback or feedforward control, or more sophisticated PID (Proportional, Integral and Derivative) control to improve the response of a process variable to a change in reference signal. The accompanying diagram shows how simple integral control tends to give either a sluggish response to the change in reference signal (Ki = 0.5), or one which overshoots the new value significantly and then oscillates about it for some time before settling (Ki = 2). The use of Proportional, Integral and Derivative terms (Kp = 1, Ki = 1, Kd = 1) makes the system responsive with only a modest overshoot before settling to the desired value.
Multivariable Control provides the most sophisticated form of control system, where each manipulated variable can depend on two or more controlled variables. Inevitably, the complexity of the underlying mathematics is much more complex than for single loop control systems. However, modern computer-based design techniques (eg MATLAB) help designers to tailor designs to satisfy more complex system demands, or ones where single loop controllers cannot guarantee optimal operation and, in some cases, may become unstable.
In conclusion, the ability to control complex industrial processes by combining the outputs of multiple sensors and actuators using control and monitoring systems operating in accordance with a range of control concepts and schemes, and providing information to operator interfaces of varying complexity, underpins the manufacture of high quality, affordable products that meet a wide variety of needs.
Our thanks to David James for providing us with such a comprehensive overview of Industrial Process Automation. My apologies that, in drafting this report, I have not covered every area that David addressed; I hope that I have given you enough information to get a feel for the breadth and complexity of his subject.