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Model Predictive Control System Design for Boiler Turbine Process Sandeep Kumar Sunori; Pradeep Kumar Juneja; Anamika Bhatia Jain
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 5: October 2015
Publisher : Institute of Advanced Engineering and Science

MPC is a computer based technique that requires the process model to anticipate the future outputs of that process. An optimal control action is taken by MPC based on this prediction. The MPC is so popular since its control performance has been reported to be best among other conventional techniques to control the multivariable dynamical plants with various inputs and outputs constraints. In the present work the control of boiler turbine process with three manipulated variables namely fuel flow valve position, steam control valve position and feed water flow valve position and three controlled variables namely drum pressure, output power and drum water level deviation [8] has been attempted using MPC technique. Boiler turbine process is very complex and nonlinear multivariable process. A linearized model obtained using Taylor series expansion around operating point has been used.

Multiloop and Prediction Based Controller Design for Sugarcane Crushing Mill Process Sandeep Kumar Sunori; Pradeep Kumar Juneja; Anamika Bhatia Jain
International Journal of Advances in Applied Sciences Vol 4, No 4: December 2015
Publisher : Institute of Advanced Engineering and Science

In the present work a sugarcane crushing mill is presented as a MIMO system with high multivariable interaction.A linear model of the plant is taken with flap position and turbine speed as manipulated variables and mill torque and buffer chute height as controlled variables.The multiloop PI controller has been designed for this plant by first investigating the RGA and the value of Niederlinski index of this plant.The decoupling of this system is done and the respective open loop and closed loop step responses are observed and compared with those of the composite MIMO system. Also the performance of multiloop controller is compared with controller designed using model predictive control system strategy for this plant.

Model Predictive Control System Analysis for Sugarcane Crushing Mill Process Sandeep Kumar Sunori; Pradeep Kumar Juneja; Anamika Bhatia Jain
Bulletin of Electrical Engineering and Informatics Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

MPC is a computer based technique that requires the process model to anticipate the future outputs of that process. An optimal control action is taken by MPC based on this prediction. The MPC is so popular since its control performance has been reported to be best among other conventional techniques to control the multivariable dynamical plants with various inputs and outputs constraints. In this paper the performance of an MPC controller on a single stage of milling train of sugar mill is analyzed. A linear model of the plant is taken with flap position and turbine speed set point as manipulated variables and mill torque and buffer chute height as controlled variables. The set point tracking responses are compared for constrained and unconstrained cases. The effect of presence of unmeasured disturbance also is investigated.

Model Predictive Control System Analysis for Sugarcane Crushing Mill Process Sandeep Kumar Sunori; Pradeep Kumar Juneja; Anamika Bhatia Jain
Bulletin of Electrical Engineering and Informatics Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

MPC is a computer based technique that requires the process model to anticipate the future outputs of that process. An optimal control action is taken by MPC based on this prediction. The MPC is so popular since its control performance has been reported to be best among other conventional techniques to control the multivariable dynamical plants with various inputs and outputs constraints. In this paper the performance of an MPC controller on a single stage of milling train of sugar mill is analyzed. A linear model of the plant is taken with flap position and turbine speed set point as manipulated variables and mill torque and buffer chute height as controlled variables. The set point tracking responses are compared for constrained and unconstrained cases. The effect of presence of unmeasured disturbance also is investigated.

Model Predictive Control System Analysis for Sugarcane Crushing Mill Process Sandeep Kumar Sunori; Pradeep Kumar Juneja; Anamika Bhatia Jain
Bulletin of Electrical Engineering and Informatics Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

MPC is a computer based technique that requires the process model to anticipate the future outputs of that process. An optimal control action is taken by MPC based on this prediction. The MPC is so popular since its control performance has been reported to be best among other conventional techniques to control the multivariable dynamical plants with various inputs and outputs constraints. In this paper the performance of an MPC controller on a single stage of milling train of sugar mill is analyzed. A linear model of the plant is taken with flap position and turbine speed set point as manipulated variables and mill torque and buffer chute height as controlled variables. The set point tracking responses are compared for constrained and unconstrained cases. The effect of presence of unmeasured disturbance also is investigated.

Predictive modeling and optimization of paper mill using hybrid machine learning techniques Abhijit Singh Bhakuni; Sandeep Kumar Sunori; Pradeep Juneja
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i2.pp692-702

The paper has played a vital role in the life of humans from ancient times covering a vast range of applications such as writing, packaging, and printing. The present paper is presenting a comprehensive review of various optimization and control methodologies, ranging from conventional to advanced ones, pertaining to the paper mill. The final goal of these control strategies is to upgrade the mill’s production and quality in presence of multiple technical challenges such as nonlinear and multivariable nature of the involved processes, various disturbance parameters, and time delays. In this work, the integration of machine learning with paper mill process is illustrated. For any manufacturing process, the final product quality is the key goal. There are various traditional techniques which have already been practiced for final produced paper quality in paper mills. This paper highlights the capability of support vector machine (SVM) algorithm to assess the produced paper quality, capturing the two crucial inputs viz. the pulp consistency and the headbox level. The basic goal of this research is twofold, firstly it presents an exhaustive literature survey exploring various strategies which are practiced currently in the domain of control and optimization of various paper mill processes. Secondly, it intends to develop and evaluate various SVM and SVM-RF hybrid models using MATLAB for assessment of quality of final product on basis of two parameters- pulp consistency and head box level. Finally, genetic algorithm has been employed in MATLAB for multivariate optimization.

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