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Process variable

A process variable (PV), also known as a process parameter or process value, is a measurable quantity that characterizes the current state or behavior of a physical system or industrial process, such as temperature, pressure, flow rate, liquid level, or composition.^[1]^[2] In process control engineering, the PV represents the controlled variable that is monitored by sensors and maintained at a desired setpoint through automated adjustments to ensure operational stability, safety, and product quality.^[3] Process variables are integral to feedback control loops, where the measured PV is compared to the setpoint, and any deviation triggers manipulation of an input variable—such as valve position or pump speed—to minimize the error and regulate the process.^[2] Common examples include flow rate in piping systems, temperature in heat exchangers, pressure in reactors, and liquid levels in tanks, all of which are critical in industries like chemical manufacturing, oil refining, and pharmaceuticals.^[3] Effective management of these variables optimizes efficiency, prevents equipment damage, and complies with regulatory standards by enabling real-time monitoring and automated corrections.^[2]

Fundamentals

Definition

A process variable (PV), also known as a process value or process parameter, is the current measured or observed value of a specific quantity in a physical, chemical, or biological process that is subject to monitoring or control.^[4] In process engineering, the PV represents a key output of the system, such as level, temperature, or flowrate, which must be maintained within a desired range to ensure operational stability and product quality.^[4] The PV is distinct from related terms in control theory: the set-point (SP) is the desired or target value for the PV that the system aims to achieve, while the manipulated variable (MV) is the adjustable input, such as a valve position or pump speed, used to influence the PV toward the SP.^[4] Basic examples of process variables include the temperature within a chemical reactor, where deviations could affect reaction rates, or the flow rate through a pipeline, which impacts material transport efficiency.^[4] Process variables are standardized using the International System of Units (SI) for consistency in measurement and communication across engineering disciplines.^[5] Common examples include Kelvin (K) for temperature, Pascal (Pa) for pressure, cubic meters per second (m³/s) for volumetric flow rate, and meters (m) for liquid level.^[5]

Key Concepts

Process variables are inherently dynamic, fluctuating over time in response to disturbances such as changes in feedstock quality, equipment malfunctions, or external environmental factors. In steady-state conditions, these variables maintain constant values, reflecting a balanced operation where inputs equal outputs and no net accumulation occurs. However, transient behavior dominates during process transitions, like startups, shutdowns, or grade changes, where variables deviate from set points until a new equilibrium is achieved, often requiring active control to minimize disruptions.^[6] For a quantity to qualify as a process variable, it must meet key criteria of measurability and relevance: it should be quantifiable through reliable instrumentation and exert a significant influence on overall process performance or product quality. This ensures that the variable can be monitored and adjusted to maintain desired outcomes, distinguishing it from unobservable or insignificant factors in the system. Controlled variables, in particular, are selected based on their direct impact on quality metrics, while disturbances—uncontrolled inputs—affect them and must also be measurable for effective management.^[6]^[7] Process variables exhibit interdependence within complex systems, where alterations in one can propagate effects to others, necessitating integrated modeling for accurate prediction and control. For instance, in fluid handling processes, variations in pressure may alter flow rates, creating coupled dynamics that amplify or dampen responses across the network. This interconnectedness underscores the need for system-wide analysis rather than isolated variable treatment.^[8] The foundational ideas behind process variables trace back to early 20th-century advancements in industrial automation, particularly through pioneers like Elmer Sperry, who in the 1910s developed servo mechanisms and integrated control systems for naval applications, such as gyroscopic stabilizers and fire control directors that automated variable monitoring and adjustment. These innovations marked the shift from manual to feedback-based regulation, influencing the evolution of process control theory.^[9]

Types and Classification

Common Process Variables

In industrial and engineering contexts, the most frequently encountered process variables are temperature, pressure, flow rate, and level, often referred to as the "big four" parameters essential for monitoring and controlling chemical and physical processes. These variables dominate process measurements, accounting for the majority of instrumentation applications in sectors like chemical engineering, where they enable precise regulation of operations such as reactions, distillation, and material transport. According to standards from the International Society of Automation (ISA), these four variables represent the core of process control.^[10]^[11] Temperature is a fundamental process variable representing the average kinetic energy of molecules in a substance, arising from the degree of thermal motion among particles. In processes like heat transfer within chemical reactors, it dictates reaction rates and phase changes, with typical industrial ranges spanning from -50°C to 500°C, though extremes can reach -200°C in cryogenic applications or over 1000°C in high-temperature furnaces. For instance, maintaining reactor temperatures around 200–300°C ensures optimal catalytic activity in petrochemical synthesis.^[12]^[13] Pressure measures the force per unit area exerted by a fluid on a containing surface, fundamentally linked to the collisions of molecules against vessel walls. It is critical in applications such as pipelines for transporting gases or liquids, where it influences flow dynamics and prevents cavitation or bursts; common ranges in chemical processes vary from vacuum levels below 0.1 bar to high pressures up to 100 bar in polymerization reactors. A representative example is monitoring pipeline pressures at 5–20 bar to sustain steady hydrocarbon flow.^[14]^[15] Flow rate quantifies the volume or mass of fluid passing through a cross-section per unit time, governed by principles of fluid dynamics such as conservation of mass and Bernoulli's equation for velocity-pressure relationships. In fluid transport systems, it ensures balanced material distribution, with typical ranges from 0.1 L/min in laboratory-scale mixing to 1000 m³/h in large-scale distillation columns. For example, controlling flow rates at 50–200 L/min in reactor feeds maintains stoichiometric ratios during chemical synthesis.^[16]^[17] Level indicates the height of a liquid or solid interface relative to a reference point in a container, based on gravitational equilibrium and volume displacement principles. It is vital for managing tank volumes to avoid overflows or dry runs, with standard ranges from 0 to 10 meters in storage vessels, adjustable via vessel geometry. In water treatment plants, level monitoring at 2–5 meters in sedimentation tanks prevents process disruptions.^[18]^[19] Beyond industrial settings, variables like humidity serve as common process parameters in environmental control systems, where it represents the water vapor content in air—typically measured as relative humidity from 20% to 80%—to regulate comfort in HVAC applications such as data centers or greenhouses.^[20]

Specialized and Derived Variables

In chemical processes, pH emerges as a specialized process variable due to its profound impact on reaction mechanisms, where even minor deviations can alter dissociation equilibria and catalyst activity, thereby dictating product purity and yield.^[21] Similarly, viscosity functions as a niche variable in fluid handling operations, quantifying a fluid's resistance to shear and influencing pipeline sizing, heat transfer rates, and energy consumption in sectors like petrochemicals and food processing.^[22] In batch reactions, concentration of reactants or products stands out as a critical specialized variable, as it directly modulates reaction kinetics according to rate laws, enabling precise endpoint determination and resource optimization in pharmaceutical synthesis.^[23] Derived process variables extend basic measurements by combining them into composite indicators that capture system performance more holistically. For instance, density in gas or liquid streams is commonly derived from pressure and temperature data via equations of state, providing essential insights for mass flow calculations and inventory control without direct sensing.^[24] In energy systems, efficiency ratios—such as the energy efficiency ratio defined as cooling capacity divided by electrical input— are computed from power consumption and output metrics, serving as derived variables to benchmark operational viability and guide retrofits in HVAC or power plants.^[25] Domain-specific applications highlight the tailored nature of these variables. In biotechnology, biomass levels represent a specialized variable pivotal for regulating nutrient feeds and harvest timing in microbial fermentations, where maintaining optimal cell densities ensures high-titer production of biologics like vaccines.^[26] Conversely, in aerospace, vibration amplitudes act as a key variable for real-time health monitoring of airframes and engines, as excessive oscillations can accelerate material fatigue and compromise flight safety under dynamic loads.^[27] These contexts demand specialized handling because standard control algorithms often fail to account for their context-dependent behaviors, such as growth phases in bioprocesses or modal resonances in structures. Challenges in managing specialized and derived variables include inherent non-linearities that amplify small input changes into large output swings—for example, pH's logarithmic response or viscosity's variation with shear rate in non-Newtonian fluids—necessitating advanced modeling for stable operation.^[21] Furthermore, many such variables rely on indirect inference from proxies like optical signals for biomass or accelerometers for vibrations, which can propagate estimation errors and reduce reliability in closed-loop control.^[28]

Measurement Techniques

Sensors and Transducers

Sensors and transducers are essential hardware components in process control systems, designed to detect physical process variables and convert them into measurable electrical signals for monitoring and analysis. These devices form the foundational layer of measurement in industrial applications, enabling precise detection of parameters such as temperature, pressure, level, and flow.^[29] The historical development of sensors traces back to the 19th century with mechanical gauges, which relied on physical deformation to indicate measurements. A pivotal advancement was the invention of the Bourdon tube pressure gauge in 1849 by French engineer Eugène Bourdon, a curved, flattened tube that uncoils under internal pressure to drive a pointer on a dial, providing a direct visual readout without electrical components.^[30] This mechanical innovation marked a shift from rudimentary manometers and barometers to more reliable instruments for industrial pressure monitoring. The transition to electronic sensors accelerated post-1950s, driven by advancements in semiconductor technology and the need for higher precision in complex processes; by the late 1950s, electronic sensors began surpassing mechanical ones in accuracy and integration potential, though initially more expensive to produce and maintain.^[31] Key milestones included the commercialization of silicon strain gauges in 1959, enabling electrical transduction of mechanical strain for pressure and force measurements.^[32] Various types of sensors are employed to measure specific process variables, each tailored to the physical phenomenon involved. For temperature, thermocouples are widely used; they consist of two dissimilar metal wires joined at a sensing junction, where a temperature difference generates a voltage proportional to the Seebeck effect, allowing measurement over a broad range from -200°C to over 2300°C depending on the type.^[33] In pressure measurement, strain gauges detect deformation in a diaphragm or bourdon element under applied force; the gauge, typically a foil pattern bonded to a substrate, experiences a change in electrical resistance as it stretches or compresses, which is then amplified to indicate pressure levels.^[34] Ultrasonic sensors serve for non-contact level detection in liquids or solids; a transducer emits high-frequency sound waves that reflect off the surface, and the time-of-flight—calculated as the round-trip duration divided by the speed of sound in the medium—determines the distance to the surface, yielding level height.^[35] For flow, turbine meters feature a multi-bladed rotor inserted in the fluid stream; as liquid or gas passes through, it spins the rotor at a speed directly proportional to the volumetric flow rate, with embedded magnets generating pulses via a pickup coil for electronic readout.^[36] Transducers operate on principles that convert physical inputs into electrical outputs, facilitating integration with digital systems. A common mechanism is the piezoelectric effect, where certain crystals like quartz deform under mechanical stress (e.g., from pressure or vibration), producing a charge separation that generates a measurable voltage; textually, this can be visualized as a sandwiched crystal between electrodes, with applied force compressing the material to displace internal dipoles, yielding an output signal proportional to the stress magnitude.^[37] In resistive transducers like strain gauges, elongation alters the conductor's length and cross-section, changing resistance according to R = \rho \frac{L}{A}, where \rho is resistivity, L length, and A area, converting strain to a variable voltage in a Wheatstone bridge circuit.^[38] For thermocouples, the thermoelectric principle produces an electromotive force at the junction due to differing electron diffusion rates in the metals at varying temperatures, described as a closed loop where the net voltage opposes the temperature gradient.^[39] Selecting appropriate sensors and transducers involves evaluating several critical factors to ensure reliability in operational environments. Accuracy specifies the closeness of measured values to the true value, often expressed as a percentage of full scale (e.g., ±0.5% for industrial thermocouples), essential for processes requiring precise control.^[40] Range defines the minimum and maximum measurable values, such as 0-1000 psi for pressure transducers, ensuring coverage of expected process variations without saturation.^[41] Response time indicates how quickly the sensor reacts to changes, typically in milliseconds for dynamic processes like flow metering, where delays could affect safety or efficiency.^[13] Environmental robustness assesses durability against conditions like temperature extremes, humidity, or corrosives, often quantified by Ingress Protection (IP) ratings—e.g., IP67 for dust-tight and temporary immersion resistance in harsh industrial settings.^[42] These criteria guide choices, balancing performance with cost for applications from chemical plants to food processing.

Signal Processing and Calibration

Signal conditioning refines raw analog signals from sensors into a form suitable for further processing and transmission in industrial systems. This process typically involves amplification to boost weak signals to levels compatible with analog-to-digital converters (ADCs), ensuring they fall within the dynamic range of downstream electronics. For instance, instrumentation amplifiers are commonly used for their high input impedance and ability to handle differential signals from bridges like Wheatstone configurations in pressure or strain measurements.^[43] Filtering is another core technique, aimed at removing noise and unwanted frequency components to enhance signal integrity. Low-pass filters, for example, attenuate high-frequency noise while preserving the baseband signal of interest, often implemented using operational amplifiers in active configurations or resistors and capacitors in passive ones. Analog-to-digital conversion follows, where the conditioned analog signal is sampled and quantized into digital values, enabling computational analysis and storage.^[43] Calibration ensures that processed signals accurately represent true process variables by aligning instrument outputs with known standards. The procedure begins with zeroing, where the instrument is adjusted to read zero under no-input conditions to eliminate offset errors, followed by spanning to scale the full range against reference values. Traceability to national standards, such as those maintained by NIST, is achieved through a hierarchy of certified references, ensuring measurements align with SI units via documented chains of comparison. Error sources like hysteresis—differences in output when input is approached from increasing versus decreasing directions—arise from mechanical friction in components and must be quantified during calibration to assess reliability.^[44]^[45] Data handling involves selecting appropriate sampling rates to capture signal dynamics without distortion, guided by the Nyquist theorem, which requires sampling at least twice the highest frequency component to avoid aliasing. In practice, rates of five times the signal frequency are often used in industrial data acquisition for robustness. Transmission of these signals commonly employs the 4-20 mA current loop protocol, where 4 mA represents the minimum (live zero) and 20 mA the maximum process variable value, providing noise immunity over long distances in process control networks.^[46]^[47] Quality assurance in signal processing relies on metrics such as accuracy (closeness to true value), precision (consistency of measurements), and repeatability (variation under identical conditions). Calibration curves, typically plotted as output versus input in slope-intercept form (Output = m·Input + b), reveal deviations like zero shifts (vertical offsets) or span errors (slope mismatches), with hysteresis appearing as separated ascending and descending paths. These assessments ensure processed process variables meet required tolerances, often verified through as-found and as-left tests against standards.^[48]

Role in Control Systems

Feedback and Deviation

In closed-loop control systems, the process variable (PV) is continuously measured and compared to a desired setpoint (SP) to compute the error signal, defined as e(t) = SP - PV(t).^[49] This error drives corrective actions through a feedback loop, where the controller adjusts the manipulated variable to minimize deviations and maintain system stability.^[50] Such loops are essential in process control to counteract external influences and ensure consistent performance.^[4] Deviations in the PV arise primarily from disturbances, such as changes in feed composition, ambient conditions, or equipment wear, which perturb the system away from the SP.^[51] These deviations are minimized using proportional-integral-derivative (PID) control, a widely adopted method that combines three actions to address different aspects of error dynamics. The PID control law is given by:

u(t) = K_p e(t) + K_i \int_0^t e(\tau) \, d\tau + K_d \frac{de(t)}{dt}

where u(t) is the controller output, K_p is the proportional gain, K_i is the integral gain, and K_d is the derivative gain.^[52] The derivation of this equation stems from early control theory, particularly Nicolas Minorsky's 1922 analysis of automatic ship steering, where he modeled system dynamics as a second-order differential equation incorporating inertia, friction, and external disturbances: A \ddot{\alpha} + B \dot{\alpha} + k \rho = D, with \alpha as heading deviation, \rho as rudder angle, and D as disturbance torque.^[53] Minorsky proposed a control law starting with a proportional term (\rho = p \alpha) to directly counter the deviation, ensuring basic responsiveness proportional to error magnitude. To eliminate persistent steady-state offsets from constant disturbances, he added an integral term (n \int \alpha \, dt), which accumulates past errors to drive the PV back to the SP over time. For improved transient response and damping of oscillations, a derivative term (m \dot{\alpha}) was incorporated to anticipate error changes by responding to the rate of deviation. Stability analysis via the Hurwitz criterion confirmed that appropriate gains (k_p, k_i, k_d > 0) maintain bounded responses.^[53] The proportional action provides immediate correction scaled by K_p, reducing large errors quickly but often leaving a residual offset; the integral action sums errors to nullify this offset, though excessive gain can cause overshoot; the derivative action predicts future errors to dampen rapid changes, enhancing smoothness without standalone use.^[52] System stability in feedback loops is assessed through concepts like gain margin, which quantifies the factor by which the open-loop gain can increase before the closed-loop system becomes unstable, providing a buffer against parameter variations or unmodeled dynamics.^[54] A larger gain margin indicates greater robustness to disturbances, ensuring the PV remains near the SP without oscillations or divergence.^[54] In a heating system example, the PV is the measured temperature, which may deviate below the SP due to a cold draft disturbance; the feedback loop computes the error and triggers the PID controller to adjust the fuel valve opening, proportionally increasing heat input while integrating past shortfalls to fully restore the temperature and differentiating to prevent overshoot.^[55]

Integration with Controllers

Process variables (PVs) integrate with proportional-integral-derivative (PID) controllers by providing real-time feedback for tuning parameters that minimize error in linear systems. The Ziegler-Nichols method, a seminal closed-loop tuning approach, involves increasing the proportional gain until sustained oscillations occur, then applying rules to set the integral and derivative terms based on the ultimate gain and period.^[56] This heuristic ensures stability and performance for PVs like temperature or flow in single-loop control.^[56] In model predictive control (MPC), PVs serve as inputs to dynamic models that forecast future behavior over a prediction horizon, enabling optimization of manipulated variables while respecting constraints.^[57] Originating in the process industry during the late 1970s, MPC uses PV predictions to compute control actions that minimize a quadratic cost function, particularly effective for systems with delays and interactions.^[57] Fuzzy logic controllers handle nonlinear PVs by mapping imprecise inputs, such as error and its rate of change, to control outputs via linguistic rules and membership functions, avoiding explicit mathematical models.^[58] This approach excels in uncertain environments where PVs exhibit nonlinearity, like chemical reactions, by defuzzifying aggregated rules to produce smooth actions.^[58] For multivariable systems, multiple PVs are managed through decoupling techniques that eliminate interactions between loops, often via state feedback or inverse models to achieve independent control.^[59] Hierarchical control structures layer supervisory optimization over local loops, prioritizing PVs based on process priorities to maintain overall stability.^[59] The integration of PVs shifted from analog pneumatic systems, prevalent before the 1970s, to digital programmable logic controllers (PLCs) introduced in the late 1960s, which replaced relay-based wiring with ladder logic for flexible PV monitoring and actuation.^[60] By the 1970s, PLCs enabled scalable handling of PV signals through standardized I/O modules, facilitating the transition to computerized control.^[60] In supervisory control and data acquisition (SCADA) systems, PV data is aggregated via protocols like OPC for real-time visualization and alarming, integrating with PLCs to oversee distributed processes.^[61] PV trends inform adaptive control by adjusting parameters online to track changes in system dynamics, with machine learning enhancements post-2010 incorporating reinforcement learning to optimize policies from historical PV data.^[62] These methods enable self-tuning in varying conditions, improving robustness over fixed-gain approaches.^[62]

Applications

Industrial Processes

In chemical plants, process variables play a pivotal role in optimizing separation and reaction processes. In distillation columns, temperature serves as a key process variable, particularly the reflux temperature, which is manipulated to maintain stable composition profiles and product purity by adjusting reflux flow rates. For example, advanced regulatory control strategies calculate net internal reflux based on heat balances involving overhead vapor and reflux temperatures to counteract disturbances like feed enthalpy changes. Similarly, pressure monitoring in chemical reactors is essential for controlling reaction rates, gas solubility, and safety, as deviations can lead to hazardous conditions such as explosions or incomplete reactions; this is achieved through pressure regulators and relief valves integrated into control loops.^[63]^[64]^[65] In the oil and gas sector, flow and pressure are fundamental process variables in pipelines, where they ensure efficient hydrocarbon transport while mitigating risks like surges or blockages; flow rates are measured using differential pressure devices such as orifice plates, and pressure is monitored via transmitters to maintain operational integrity across varying viscosities and pipe diameters. A notable case study from a hydrocracker unit in a refinery illustrates upset recovery: during high-throughput operations, elevated diesel outlet temperatures (exceeding 50°C) and fluctuating flow rates (up to 117.8 m³/hr) triggered an upset, but installing an active redundant cooler allowed real-time monitoring and adjustment of these variables, reducing temperatures to 48°C and stabilizing flow to 100 m³/hr, thereby preventing shutdowns and enhancing safety.^[66]^[67] Manufacturing processes rely on process variables like level and speed to achieve precision and throughput. In filling lines, level control monitors and adjusts product volumes in containers using checkweighers or vision systems to detect over- or under-filling in real time, enabling automatic corrections without halting production and ensuring compliance with packaging standards. For assembly processes, speed control—often as velocity in serial operations like welding or part placement—modulates machine trajectories to match production demands, reducing defects from excessive or insufficient rates while integrating with feedback systems for consistent output.^[68]^[69] Standards and regulations standardize process variable handling in industrial settings. The ISA-5.1 standard establishes uniform instrumentation symbols and identification codes for process variables, facilitating clear depiction of measurement and control systems across diagrams in sectors like chemical and petroleum engineering. Additionally, OSHA's Process Safety Management (PSM) standard mandates monitoring of process variables such as pressures, flows, and temperatures through hazard analyses and operating procedures to prevent catastrophic releases of hazardous chemicals.^[70]^[71]

Modern and Emerging Contexts

In the context of Industry 4.0, process variables (PVs) such as temperature, vibration, pressure, and flow rates are streamed in real-time through Industrial Internet of Things (IIoT) networks, enabling edge computing for predictive maintenance. Edge devices process these PVs locally to reduce latency and bandwidth demands, allowing machine learning models to forecast equipment failures before they occur. For instance, in waste-to-energy plants, IIoT platforms monitor syngas heating value and flue gas temperature as key PVs, achieving prediction accuracies with R² values exceeding 0.98 through neural network-based models integrated with 5G-enabled gateways. This approach minimizes downtime by detecting anomalies in operational data streams, contrasting with traditional batch processing in earlier industrial paradigms.^[72] In biotechnology, PVs like cell density and dissolved oxygen levels are pivotal in bioreactor operations for optimizing microbial and mammalian cell cultures. Cell density, often reaching densities above 10^10 cells per liter in perfusion systems, directly influences nutrient uptake and product yields, monitored via spectroscopy or soft sensors to maintain viability during fed-batch processes. Dissolved oxygen, controlled through aeration and agitation via PID controllers or model predictive algorithms, ensures metabolic efficiency, with oxygen transfer rates (OTR) measured dynamically to prevent hypoxic conditions that limit growth. These PVs are integrated into supervisory control and data acquisition (SCADA) systems for real-time adjustments in biopharmaceutical production, enhancing scalability for therapeutic protein synthesis.^[73]^[74] Renewable energy systems leverage PVs to enhance performance and reliability in dynamic environments. For wind turbines, vibration and acceleration serve as critical PVs, captured by accelerometers and strain gauges in structural health monitoring setups, enabling early detection of fatigue in floating offshore installations through operational modal analysis. In solar photovoltaic arrays, efficiency-tracking PVs such as irradiance, panel temperature, and shading factors are monitored to optimize power output, often via power electronics interfaces that adjust for environmental variations. These measurements support predictive strategies that extend asset life, with vibration data from supervisory control and data acquisition (SCADA) systems providing 10-minute resolution insights for maintenance scheduling.^[75] Emerging trends since 2020 highlight the integration of artificial intelligence (AI) with digital twins for advanced PV anomaly detection, addressing limitations in traditional control by simulating real-time system behaviors. AI-enhanced digital twins employ deep learning techniques, such as long short-term memory (LSTM) networks with Bayesian optimization, to identify deviations in PVs like temperature or pressure, facilitating proactive interventions in manufacturing and energy sectors. Machine learning models within these twins enable predictive maintenance through pattern recognition and fault diagnosis via transfer learning, reducing false positives in anomaly alerts. For example, in additive manufacturing, AI-driven twins achieve real-time quality prediction by analyzing PV streams, aligning with ISO 30186 standards for adaptive systems. This evolution supports interdisciplinary applications, from urban energy grids to biotech simulations, by bridging physical and virtual domains for enhanced decision-making.^[76]

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### Nyquist Theorem in Context of Data Acquisition for Industrial Signals
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What Is a 4-20 mA Current Loop? | Fluke
### Summary of 4-20 mA Protocol in Process Control
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Calibration Errors and Testing | Basic Principles of Instrument ...
Hysteresis errors are almost always caused by mechanical friction on some moving element (and/or a loose coupling between mechanical elements) such as bourdon ...
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[PDF] PID Theory Explained - Experimentation Lab
Mar 29, 2011 · This is called a closed loop control system ... Steady-State error is the final difference between the process variable and set point.
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[PDF] introduction to - process control
1.4 CONTROL SYSTEM EVALUATION. A process-control system is used to regulate the value of some process variable.Missing: engineering | Show results with:engineering
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Process Control Introduction — Dynamics and Control - APMonitor
Aug 17, 2021 · A feedback control system consists of a sensor, actuator and controller that are connected with information flowing in a loop.
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[PDF] Nicolas Minorsky and the Automatic Steering of Ships - Robotics
his main interests have been computer control and the history of control engineering. He is the author of a book on the history of control engineering and ...
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Introduction: Frequency Domain Methods for Controller Design
The gain margin is defined as the change in open-loop gain required to make the closed-loop system unstable. Systems with greater gain margins can withstand ...
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[PDF] Optimum Settings for Automatic Controllers
By J. G. ZIEGLER' and N. B. NICHOLS,' ROCHESTER, N. Y.. In this paper, the three principal control effects found in present controllers are examined and ...
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[PDF] A survey of industrial model predictive control technology - CEPAC
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Fuzzy control theory: A nonlinear case - ScienceDirect.com
We analyze the performance of a simple fuzzy controller with linear and nonlinear defuzzification algorithms.
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[PDF] DECOUPLING IN THE DESIGN AND SYNTHESIS OF ...
Necessary and sufficient conditions for the "decoupling" of an m-input, m-output time-invariant linear system using state variable feedback are determined.
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[PDF] History of Control History of PLC and DCS
Jun 15, 2012 · The early history of the PLC goes back to the 1960's when control systems were still handled using relay control. During this time the control ...
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Sensors Data Analysis in Supervisory Control and Data Acquisition ...
Apr 14, 2021 · This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection.
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[PDF] A Historical Perspective of Adaptive Control and Learning - arXiv
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Use Model Predictive Control to Improve Distillation Process
Typical process variables (PVs) are temperatures, pressures, and compositions at different points in the column. The main controlled variables (CVs) are feed ...
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The Dos and Don'ts of Distillation Column Control - ScienceDirect
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Process Pressure - an overview | ScienceDirect Topics
Pressure is another key process variable since its level is critical for boiling, chemical reaction, distillation, extrusion, vacuuming, and air conditioning.
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Process Control Automation in Oil & Gas (Downstream) - EPCM
In oil & gas downstream, the critical process variables which are very important to control include pressure, level, flow, temperature, and density.
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Process Safety and Performance Improvement in Oil Refineries ...
May 11, 2020 · The case study presents the excerpts of process safety and performance improvement of a HCU cooling system by installing an additional ...
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Fill Level Inspection and Control | METTLER TOLEDO
Fill level inspection with checkweighing, x-ray inspection, or vision inspection helps manufacturers to control against over-filling and underfilling ...
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[PDF] Manufacturing Processes and Process Control - MIT
To help define internal variables in the process as well as the inputs and outputs, the basic output causality of the process model is shown in Fig. 3 using a ...
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ISA5.1, Instrumentation Symbols and Identification
The purpose of this standard is to establish a uniform means of designating instruments and instrumentation systems used for measurement and control.ANSI/ISA-5.1-2024 · ISA-TR5.1.03-2024 · ISA-TR5.1.02-2024
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1910.119 - Process safety management of highly hazardous chemicals. | Occupational Safety and Health Administration
### Summary: How OSHA PSM Requires Monitoring of Process Variables
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https://www.osha.gov/laws-regs/regulations/standardnumber/1910/1910.119
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