ISSN: 2319- 8753 International Journal of Innovative Research in Science, Engineering and Technology (An ISO 3297: 2007 Certified Organization) Vol. 3, Issue 6, June 2014 Copyright to IJIRSET www.ijirset.com 13201 Modelling and Simulation of Interacting Conical Tank Systems S.Vadivazhagi 1 , Dr.N.Jaya 2 Assistant Professor, Department of ECE, MRK Institute of Technology, Kattumannarkoil, Tamilnadu, India 1 Associate Professor, Department of E&I, Annamalai University, Chidambaram, Tamilnadu, India 2 ABSTRACT: This paper describes the controller design of non linear system.The Non linear system taken up for the study is the Interacting Conical Tank Systems.In this paper, design of controllers based on Skogestad tuning is determined.For each stable operating region, a first order process model is identified using Process reaction curve method.The control is done and the responses for different operating regions are taken.Simulation is made by the MATLAB software. KEYWORDS: Non Linear System, Mathematical Modelling,Interacting Conical Systems,Skogestad PI controller. I. INTRODUCTION In most of the industries chemical process present many challenging problems due to their nonlinear dynamic behaviour.Because of inherent non linearity,most of the chemical process industries are in need of traditional control techniques.One such non linear process taken up for study is Interacting Conical systems[1]. Conical tanks are best suited for food process industries, concrete mixing industries, hydrometallurgical industries and waste water treatment industries. Its shape contributes to better drainage of solid mixtures, slurries and viscous liquids. To achieve a satisfactory performance using conical tanks, its controller design becomes a challenging task because of its non - linearity. This non - linearity arises due to its shape. It is broad at the end and becomes narrow in the lower end[10]. The primary task of a controller is to maintain the process at the desired set point and to achieve optimum performance when facing various types of disturbances . Conventional controllers are widely used in industries since they are simple,robust and familiar to the field operator.Practical systems are not precisely linear but may be represented as linearized models around a nominal operating point[4]. The work in this paper is divided in two stages. 1) Mathematical Modelling 2) Skogestad controller Implementation. A Mathematical model is developed for Two Tank Conical Interacting system using the Mass Balance Equation.The operating parameters of the process is given in a table from which the Open loop response of the process is obtained. Piecewise Linearization is carried out.The controller tuned using Skogestad method named after the originator is based on the direct method.The objective of the paper is to show that by employing the proposed tuning of PI controllers,an optimization can be achieved[2]. The Proportional Integral(PI) and Proportional-Integral-Derivative(PID) controllers are widely used in many industrial control systems for several decades.S.Nithya et al.[1] discussed about the control issues associated with the non linear systems in real time using cost effective data acquisition system.The limitations of a PI controller for a first order non linear process with dead time was discussed by R.Anandanatarajan et al.[2].D.Dineshkumar et al.[4]. implemented Skogestad PID controller for Interacting Spherical Tank System.Simulation studies were carried out for this non linear process.Sigurd Skogestad[5] proposed the best simple tuning rules in the world.The analytical tuning rules are as simple as possible and still result in a good closed-loop behaviour.A Neuro based model reference Adaptive control of a conical tank level was proposed by N.S.Bhuvaneswari et al.[6]. Paper is organized as follows. Section II describes the Mathematical Modelling of Two Tank Conical Interacting Systems and its operating parameters.Piecewise linearization is carried out around four different operating regions .The implementation of Skogestad PI controller is discussed in Section III. Section IV presents experimental results showing four different simulations for four regions. Finally, Section V presents Conclusion.