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What is Polarography? Polarography is the measurement of the current that flows in solution as a function of an applied voltage . Polarography is a voltammetric technique in which chemical species (ions or molecules) undergo oxidation (lose electrons) or reduction (gain electrons) at the surface of a dropping mercury electrode  (DME) at an applied potential.  The actual form of the observed "polarographic wave" depends upon the manner in which the voltage is applied and on the characteristics of the working electrode.  The working electrode is often a Dropping Mercury Electrode (DME), and the polarographic wave thus has oscillations imposed on it from the variations in mercury drop size.  As above, you can see that, as variation in potential in graph (A), we can see variation of current with respect to potential in graph (B). Those are graph of polarograph. then, let's see how that works ! Principle: Polarography consists of electrolysing a solution of an electroactive s

The implementation of the PID controller has evolved from early mechanical and pneumatic designs to analog circuits using transistors, and lately to the microprocessor and digital systems. The digital PID so designed is based essentially on architectures including multipliers, adders, and some other logic circuits. Another advantage of digital control is that the error signal is first sampled and the controller output is computed numerically through a digital processor. Control action = past control action + correction by controller  For PID controller Lets consider the following diagram   Input = e(t)   Output = u(t)    The transfer function in the PID controller in continuous time is given by Equation 1 Where k= gain Ti = integral time Td = derivative time Taking Laplace to transform on both sides we obtain the continuous controller Equation 2 Write the above equations in gain terms Equation 3 To implement in digital write Equation 3 in z – transform by simply replacing s= (1-z^1) By

In the previous blog, we learn about instrumentation documents overview. In that document's example, we saw types of icons & symbols.  In this blog, we will learn about it in detail. If you haven't seen the previous blog, then click down below: Instrumentation Documents Overview As per PFD, P&ID, and other documents, there is variation in lines, equipment symbols, valve types, measurement devices, symbols for functional diagrams, etc. Line Types : Process/Instrument Line connections : Instrument Bubbles : Process Valve Types : Valve Actuator Types : Liquid Level Measurement Devices : Flow Measurement Devices (flowing left-to-right) : Process Equipment : Functional Diagram Symbols : Single-Line Electrical Diagram Symbols :  Fluid Power Diagram Symbols : That's it for this blog. Thanks & Keep Learning.

What is Instrumentation Documents? Every technical discipline has its own standardized ways of making descriptive diagrams, and so instrumentation has its own way also. The scope of instrumentation is so broad, so no one form of diagram is sufficient to capture it all. so types of instrumentation diagrams are listed below: Process Flow Diagram (PFD) Process & Instrument Diagram (P&ID) Loop Diagram (Loop Sheet) Functional Diagram Let's learn it in detail. Process Flow Diagram (PFD) This document contains  the interconnections of process vessels, pipes, and flow paths of process fluids . The main focus of this diagram is the process itself.  The proper form of a diagram to represent the “big picture” of a process is called a process flow diagram.   Example of Process Flow Diagram Process & Instrument Diagram (P&ID) P&ID shows the layout of all relevant process vessels, pipes, and machinery, but with instruments superimposed on the diagram showing what gets measured

After learning Fuzzy Inference System Overview, let's go towards the learning of Fuzzy Inference Methods. If you haven't gone through it, just click below for that. Click here to get an overview of FIS:   "  Fuzzy Rule-Based System  " Methods of FIS: Mamdani Fuzzy Inference System Takagi - Sugeno Fuzzy Model (TS Method)  Mamdani Inference System This System proposed by Ebhasim Mamdani in 1975 . It was anticipated to control a steam engine and boiler combination by synthesizing a set of fuzzy rules obtained from people working on the system. Mamdani Fuzzy Inference System   Steps need to be followed for computing output of the FIS: Determining a set of fuzzy rules Fuzzifying the inputs using the input membership functions Combining the fuzzified inputs according to the fuzzy rules to establish a rule strength Finding the consequence of the rule by combining the rule strength and the output membership function Combining the consequences to get an output distribution  D

Let's first learn some definitions of the state-space models. State : State represents the status of a dynamical system, with the minimum set of variables ( known as state variables) such as the knowledge of these variables at t=to, together with the knowledge of the inputs for, completely determines the the behavior of the system. State Variable : The variables that represent the status of the system at any time t, are called state variables. State vector : A set of state variables expressed in a matrix is called a state vector. State-space : Any n-dimensional state vector determines a point (called the state point) in an n-dimensional space called the state space. State Trajectory : The curve traced out by the state point from to in the direction of increasing time is known as the state trajectory. State Model: The state vectors with input/output equations constitute the state model of the system. Transfer functions provide a system’s input-output mapping only. For the State-sp

It is easy for us to express our views in our natural language so that it becomes easy to understand rather than talking in boolean. Comparing ourselves even Fuzzy logic has the same significance in expressing conditional statements. The expression for the representation is given below - IF antecedent THEN consequent  The expression as stated above is referred to as the Fuzzy IF-THEN rule base . Canonical Form This is a representation of a system, for a given condition if true a number of restrictions have to be obeyed. Following is the canonical form of Fuzzy Logic Rule Base Rule 1 - If condition C1, then restriction R1 Rule 2 - If condition C1, then restriction R2 . . . . Rule n - If condition C1, then restriction Rn. There are three general forms in which the canonical rules can be formed. They are: 1. Assignment Statements 2. Conditional Statements 3. Unconditional Statements let's learn it one by one. 1.  Assignment Sta tements These kinds of statements use " = " (e

The root locus method can also be used for discrete-time systems without many modifications since the characteristic equation of a discrete control system is of the same form as that of a continuous-time control system. In many LTI discrete time control systems, the characteristics equation may possess any of the below 2 forms. 1 + G(z)H(z) = 0 1 + GH(z) = 0 To combine both, let us define the characteristics equation as: 1 + L(z) = 0 ----------(1) where,L(z) = G(z)H(z) or L(z) = GH(z).  L(z) is popularly known as the loop pulse transfer  function. From equation (1), we can write L(z) = −1 L(z) is a complex quantity that can be split into two equations by equating angles and magnitudes of two sides. This gives us the angle and magnitude criteria as Angle Criterion:  ∠  L(z) = ±180 ° (2k + 1), k = 0, 1, 2.... Magnitude Criterion :  |L(z)| = 1 The values of z that satisfy both criteria are the roots of the characteristics equation or close loop poles. Before constructing the root locus,