Printer Friendly

Technical viability of 'KGS' system application.


In Centralized digital control system the central computer has big memory (magnetic, semiconductor) to store data. It takes all decision according to program & status of plant parameter. In distributed control system the process is divided into parts normally function wise. The individual parts or section is controlled & monitored by local computer. The local computer communicates the data to central computer through data highway. The co-ordination functions are carried out by central computer as well as it is medium for management information system.

KGS System

The KGS system is shown in fig.1. The advancement in embedded technology is used to take care of local parameters. It gives full facility to local operator to interact with the process using keyboard & LCD display. The settable alarm method & alert message reduce operating skills & breakdown time. It has enough memory to take care of data. The data memory will be refreshed every time & data of interest of management will be sent to a pc through simple Ethernet data highway. The pc memory if not enough to take care of all data of interest then it can be transferred to special data storage element. The data of interest of respective department is also stored at the PC of that department. Data distribution can be represented as in fig. 1.

The present work evaluates, how 'KGS' system is useful in various ways to optimize output to have better quality output product. We have provided simple embedded controller at each control section which not only takes care of its own section to optimize system but at the same time communicates with supervisory pc and alerts for present status by giving data. This gives benefits in following way.

* Reduces approximation levels.

* Optimizes algorithm.

* Resolution can be increased.

* Maintenance simpler.

* Control characteristics of each element can be well observed so utilization is optimized.

* Non linearity will take care off.

* Automatic controller tuning to track error efficiently.

* Interaction and decoupling of control loops can be programmed as per requirement and system parameters.

* Low Inventory cost.


Case Study I

Most plants have pH control applications; even if in their waste--treatment areas. These areas usually have environmental compliance issues and for application like fermenters, reactors, crystallizers, pH control is critical. The pH control of sugar cane juice is well used in sugar factory. How 'KGS' is applied here to optimize performance which is shown in the fig.2.


The most important control section in sugar refinery is shown in fig.2, which directly governs the quality of the sugar. In this case the pH of the final o/p is important. It seems the normal strategy of pH control is enough but it is not the case as of the following reasons.

The pH depends on not only the acidic or alkaline component but also on influent flow variation.

Flow is the manipulated variable as well as control variable. It seems though the process gain were unity but this is not case. If the fluid is gaseous, in our case (SO2) it subject to expansion upon change in pressure hence with flow in case of liquid (sugar cane juice, lime in our case) inertia is significance. So that the flow can not start or stop without accelerating or decelerating. Thus, the time constant is important which is generally given by formula.

R = LFP s/2gA [DELTA]P

Where, L=length; F=flow; P=density; g=gravity; A=area; [DELTA]P=pressure drop.

pH control is quite difficult to the non linearity of the neutralization process. When only PH is considered non linearity of the titration curve as well as actuator response is also important. Fuzzy logic or PI controls are the schemes are normally used for pH control. To approximate the thing to satisfy normal pH control equation mainly for strong acid or base is given as.



[C.sub.o](t): excess hydrogen ion concentration in the effluent stream (mol/L)

[C.sub.u](t): excess hydrogen ion concentration of the reagent (mol/L)

[C.sub.i](t): excess hydrogen ion concentration in the influent stream (mol/L)

U(t): flow rate of the reagent (L/s)

Q(t): flow rate of the influent stream (L/s)

V: volume of the tank (L)

Normally, the reagent concentration is constant, and we control the effluent pH by varying the reagent flow. Note that the concentrations are excess concentrations, meaning that they measure the concentration of hydrogen ions in excess to that found in water. We can convert an excess concentration C to pH using the formula. pH = - log[[square root of (0.25[C.sup.2] + [10.sup.-14])] + 0.5C]

To fulfill the above requirement with optimized way we will program the individual embedded controller to take care of individual section as well as it will communicate with other embedded controller just by simple digits which indicates how much inter action level is required to optimize the performance. At the same time the data will be passed to supervisory PC to give the idea of the present status of the system. To elaborate control part in brief we can accommodate following things in algorithm of pH section.

* Drift problem in electrode.

* Actuator characteristic.

* Auto tuning of controller.

* PI or fuzzy scheme whatever is preferred.

By applying 'KGS' we will get optimized process reaction curves. The expected process reaction curves at individual level as well as overall is shown in fig 3.


Case Study II

The flow diagram with desire temperature of inter-stage heating for gasoline production in moving bed reactor is shown in fig.4. The allowable temperature range for this reaction can be carried out is quite narrow. i.e., Above 530 deg/c it is not desirable and below 430 deg/c the reaction does not take place.

The temperature conversion curve shown in fig. 5 also indicates how the endothermic reaction approaches to equilibrium.



By applying KGS we get following advantages.

* The same embedded hardware controller can be used for individual stages with different

* Temperature and flow control set point, so inventory and spare require is same.

* As the same controller is compatible, replacement and maintenance is easy.

* Each controller can take care off narrow temp range so the quality of the final product can be well observed.

Case Study III

The KGS is applied in Bioreactor where close monitoring on process is required for better quality production & to avoid the batch rejection.

Various phases of bacteria cell growth are shown in fig.6. In these phases we have to keep air, oxygen, Motor speed under control, to get universal output throughout the reactor. Each phase has its own characteristics.

Phase I: Lag Phase

Phase II: Exponential Growth phase

Phase III: Antibiotics produced during the stationary phase

Phase IV: Death phase

With KGS, individual embedded controller can monitor the status to get particular behavior of graph with optimized algorithm. The algorithm can be written to suit particular condition i.e. Oxygen, Motor Speed & inward flows to get better output. Any one can monitor the status of bacteria at N no of points in Bioreactor & can take the control action.



The 'KGS' system will have positive impact on the efficiency and reliability of the plant where it will be used. We can monitor the process efficiently to improve performance as each element in control system loop is under well observation & taken care by optimized algorithm implemented in embedded controller which can be modified as and when required. The comparison with set point or reference point will be very quick and accurate and the error detection will be improved to high degree of accuracy. The technical skill required is also less, only the thing we have to change simply the set points to suit new control loop. As the universal embedded control with simple A to D & D to A converter with keyboard & display used the inventory and maintenance cost can be reduced.


[1] Control system design--Graham C, Goodwin, Steford F Graebe, Mario E Salgado. Prentice Hall, csd.

[2] Resources, Tools and Basic information for Engineering and Design of Technical Applications.

[3] The engineering Tool

[4] "pH Control Rules of Thumb, Facts and Humor," by Jim Cahill. Nov.29, 2007, Emersan Process Experts, in Education in Regulatory Compliance in pH Control.

[5] Techniques to improve pH measurement performance by Greg McMillian. Modeling and Control Dynamic World of Process Control, Oct. 2, 2006

[6] Elements of chemical reaction engineering (second edition), H. Scott Fogler, P.H.I.

[7] Process Control Systems--Application, Design, and Tuning (third edition), F.G. Shinskey Mcgraw-hill International Edition,

[8] Chemical and process thermodynamics--second Edition by B.G.Kyle, Kansas State University, P.H.I.

[9] pH control using PI control Algorithms with automatic tuning method, E.Ali, Chemical Engineering Research & Design July 2001

[10] Luyben-Process Modeling Simulation for Chem. Engineering-mcgraw hill

[11] Chemical Reaction Engineering By Octave Levenspien.--Willy India

Geeta Khare (1) and R.S. Prasad (2)

(1) Professor, Shivajirao S. Jondhle College of Engineering & Technology, Asangaon, M.S. 421 601 India E-Mail:

(2) Prof. & H.O.D., E&C Dept., K.N.I.T., Sultanpur, U.P.
COPYRIGHT 2008 Research India Publications
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2008 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:kaleidoscopic governing system
Author:Khare, Geeta; Prasad, R.S.
Publication:International Journal of Applied Engineering Research
Article Type:Report
Geographic Code:1USA
Date:Feb 1, 2008
Previous Article:Laboratory study of waste tires fill used with foundations in expansive soils.
Next Article:Atmospheric barrier discharge reactor for surface processing.

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters