UNIVERSIT DEGLI STUDI DI SALERNO Bachelor Degree in

UNIVERSITÁ DEGLI STUDI DI SALERNO Bachelor Degree in Chemical Engineering Course: Process Instrumentation and Control (Strumentazione e Controllo dei Processi Chimici) Measuring devices of the main process variables General properties of sensors Rev. 2. 3 – February 28, 2019

SENSORS EXAMPLES IN EVERYDAY LIFE • Fever thermometer • Kitchen balance • Microphone EXAMPLES FROM THE PROCESS INDUSTRY: see also: § 13. 3 - Stephanopoulos, “Chemical process control: an Introduction to theory and practice”, Prentice Hall, 1984 February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 2

GENERAL BLOCK DIAGRAM FOR A PROCESS SENSOR Measured system Energy supplier Primary measuring element Measured entity Acquisition by the control system Signal conditioning system Transducer Mechanic/pneumatic signal Electric signal NOTE: Sometimes Primary measuring device and the Transducer coincide! February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio Real measuring instrument 3

SENSORS Definitions TRANSDUCER: Device that converts the measure of a physical quantity or a process variable (temperature, pressure, flowrate, etc. ) in a electric variable (voltage and current), connected to it. The transducer is called only with the name of the measured variable because the type of output signal is always electric, e. g. pressure transducer. TRANSMITTER: the word “transmitter” is commonly used for a transducer using a direct current as output signal, normally in the range 4 -20 m. A. It allows to transmit the signal for long distance (up to few kilometers from the measuring point). MEASURED ENTITY: physical quantity or process variable that we want to measure with a sensor. MEASURING RANGE: it is the set of values that the sensor is able to measure. They are between a minimum Xmin and a maximum Xmax The maximum value is commonly called full scale The difference (Xmax – Xmin) is commonly called span February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 4

GENERAL PROPERTIES OF SENSORS ACCURACY: It estimates the closeness of the measure to the true value of the measurement. It defines the maximum deviation between the measure provided by the sensor and the real value. The accuracy can be expressed as a percentage of the measuring range or of the full scale if the minimum of the measuring rage is zero Percentage of the measuring range of full scale (dimensionless) or it can be expressed as a percentage of the measure (that is the true value) Percentage of the measure (dimensionless): - ɛa is more constraining - εf is not useful for wide deviation of the measure - a small percentage of the full scale error can correspond to a large error if the measured variable is close to the minimum of the measuring range Bias (Xm – Xv) The accuracy is sometimes expressed as an absolute value: e. g. : for a temperature sensor, accuracy± 1 o. C of a measuring range 200 400 o. C February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 5

GENERAL PROPERTIES OF SENSORS REPEATABILITY: It is the ability to provide a measured value Xm with a deviation as small as possible with respect to a true value Xv that remains constant. Precision is synonymous with repeatability. It is calculated as standard deviation. CALIBRATION: Procedure or method of adjusting parameters or "settings" (positioning) of the sensor, which has the purpose of bringing together and possibly making Xm coincide with Xv , this latter referring to another sensor of known and great accuracy. RANGEABILITY (or TURN-DOWN): It is the ratio between the full scale value and the infimum of the measuring range at which constant values of accuracy and repeatability are considered (e. g. : 20: 1). In trade, it is the ratio between the supremum and the infimum of the measuring range. February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 6

ACCURACY vs. REPEATABILITY MEASURED VARIABLE REPEATABLE AND INACCURATE MEASURE TRUE VALUE UNREPEATABLE AND ACCURATE MEASURE TIME from Magnani, Ferretti e Rocco (2007) February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 7

GENERAL PROPERTIES OF SENSORS STATIC CHARACTERISTIC It is represented by diagram or analytic form the “static” relation (all the variables are constant with time) between the measured variables and the output signal of the measuring element (or the transmitter) In analytic form: y = y(u) where: u is the measured variable y is the output signal Example: in graphic form: February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 8

GENERAL PROPERTIES OF SENSORS SENSITIVITY It is defined as the quotient of differences: S(y) = Δy/Δu RESOLUTION It is the smallest change of the measured variables which the sensor can detect on the output signal y NOTE: the resolution is usually considered on the whole measuring range of the instrument SENSITIVITY THRESHOLD It is the instrument resolution at the minimum of its measuring range. February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 9

HYSTERESYS It corresponds to the maximum difference between values of the output signal in going and return way in calibration procedure. It is expressed as a percentage of the full scale (% f. s. ). February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 10

DYNAMIC CHARACTERISTIC The dynamic characteristic is a characteristic of the instrument sensor that describes how the instrument responds to a variation in the magnitude of the measured quantity. The dynamic characteristic is expressed by a characteristic time called response time. It depends on the sensor, on the assembling, etc. EX. : Dynamic characteristic representing the “step change response” of a thermometer STATIC ERROR Other dynamic properties sometimes used are: TIME • Transfer function: • Bandwidth February 25, 2021 from Magnani, Ferretti e Rocco (2007) Process Instrumentation and Control - Prof. M. Miccio 11

SENSORS Classifications Ø Function and measured variable (T, P, . . . ) Ø Based on the physical principle and the related technology adopted (e. g. : optic, piezoelectric, . . . ) Ø Based on end use or application Ø Based on energetic behavior: § active sensor: it converts the energy of the input signal without the aid of external supplier (e. g. : the photovoltaic cell is active because it directly changes the energy of light in electrical energy) § passive sensor: it needs energy from an external supplier to convert the input signal Ø Smart sensors NEW ! Ø Soft sensors NEW ! February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 12

SMART SENSOR It indicates a more "intelligent and gifted" measuring device compared to a traditional sensor equipped with a transducer, able to: • process a signal into a digital one • transmit information to the external environment as digital signals • have internal redundancy circuits • memorize and provide the configuration, tuning and operating parameters • other auxiliary functions February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 13

SOFT or VIRTUAL SENSOR Definition A mathematical model, correlating difficultly measured quality variables with the frequently and easily measured process variables Purpose of soft sensor • real-time predictions of the quality variables, monitor processes, design tighter control, fault detection and diagnosis. from Biao Huang, o H Presentation at ISA Fort Mc. Murray 04/18/11 February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 14

SOFT or VIRTUAL SENSOR Properties of soft sensor • low cost alternative of expensive online analyzer • easily implemented on existing hardware and no additional investment • provide real-time estimation of quality variable, handling time delay and slow sampling rate of lab data from Biao Huang, o H Presentation at ISA Fort Mc. Murray 04/18/11 http: //www. riveja. lt/en/sensors/soft-sensors February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 15

SOFT or VIRTUAL SENSOR Definitions Soft sensor is a common name for a software-implemented algorithm where several measurements are processed together. There may be dozens or even hundreds of measurements. The interaction of the measurement signals can be used for calculating new quantities that can not be measured. • Soft sensors or inferential calculators are operators’ virtual eyes. • Soft sensors create windows to a process where physical equivalents are unrealistic or even impossible. • Sensor output can be a control signal, advisory information for operators, predictions of product quality, information on process faults or outliers in data, etc. from Antanas Verikas “Soft Sensors for Monitoring” February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 16

SOFT or VIRTUAL SENSOR An Example February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 17

SOFT or VIRTUAL SENSOR Techniques • • • Neural networks (NN) Neuro-fuzzy systems Kernel methods (support vector machines) Multivariate statistical analysis Data fusion (Dempster-Shafer theory, for example) Image analysis from Antanas Verikas “Soft Sensors for Monitoring” February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 18

SENSOR SELECTION Selection criteria • • Measuring range and span Accuracy, repeatability, sensitivity and resolution Rangeability Dynamic characteristic Reliability Costs (purchase, installation, maintenance) Installation problems and hazard Construction material related to the fluid properties February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 19

SENSORS Measured process variables • Temperature • Pressure • Flow rate • Level • stream composition • p. H • dissolved oxygen • Humidity • Turbidity • Opacity • . . . February 25, 2021 Process Instrumentation and Control - Prof. M. Miccio 20