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Nonlinear autoregressive-moving average-L2 (NARMA-L2) controller for multivariable ball mill plant. CHEMICAL PRODUCT AND PROCESS MODELING 2021. [DOI: 10.1515/cppm-2021-0031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Mineral processing facilities concern an enormous amount of dynamically complex unit operations (due to nonlinearities), for instance ball mill system. Normally, these processes need multivariable controllers to smooth actions by designing for plant constraints such as deadtimes and dynamics interactions. The present work presents a comparison between a classical PI and nonlinear moving average autoregressive-linearization level 2 (NARMA-L2) controllers based on artificial neural network (ANN) for a ball mill system. The manipulated variables of this plant are the rotation velocity (Vr) and the feeding weight (Wf), while the controlled parameters are the hold up (HU) and the mass fraction under 45 μm (P45). The simulation was built in the MATLAB software (Simulink), comparing the actions of PI and NARMA-L2 controllers in the face of operational changes in specific regions (constraints). The performance of proposed controllers was verified by the integral of absolute error (IAE), integral of squared error (ISE), or the integral of time-weighted absolute error (ITAE). The results of simulation showed the validity of the model obtained and the control technique proposed in this paper, which contributes to studies of multivariate controller designs for ball mills with significant applications. Additionally, this paper brings a first hybrid approach (PI/NARMA-L2) with successful implementation described in the literature.
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Bandsode SP, Besta CS. Dynamic analysis and decentralised control system design for diphenyl carbonate reactive distillation process. Chem Ind 2020. [DOI: 10.1080/00194506.2020.1847697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
| | - Chandra Shekar Besta
- Department of Chemical Engineering, National Institute of Technology, Calicut, India
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Yadav G, Kiran GU, Rao CS. Robust optimal centralized PI controller for a fluid catalytic cracking unit. CHEMICAL PRODUCT AND PROCESS MODELING 2020. [DOI: 10.1515/cppm-2020-0019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Fluidized Catalytic Cracking (FCC) is a complex process that arises due to feed composition, non-linearities, and dynamic mass and heat interactions in its components. FCC is difficult to model and monitor in industries, and one of the key reasons is that they are multivariable processes. Such processes are highly interacting and that makes the process of controlling even more difficult. The interaction between loops can be quantified easily by dRGA. An easy and effective way of controlling multivariable processes is to implement a centralized control system, considering the interactions between measured and manipulated variables. In this study, a centralized control system is designed for the riser section of the FCC unit. The dRGA method is modified to enhance the closed-loop response by formulating an optimization problem and obtaining an optimal controller settings. A rigorous simulation studies show an 826% reduction in ISE values, a 309% reduction in IAE values, and a 262% reduction in ITAE value of
T
r
i
s
${T}_{ris}$
from the dRGA method to the modified dRGA method. Further, IAE values for
Y
l
p
g
${Y}_{lpg}$
are reduced by 29% from dRGA to modified dRGA method and 34% from synthesis to modified dRGA method.
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Affiliation(s)
- Gourav Yadav
- Department of Chemical Engineering , National Institute of Technology Karnataka , Surathkal , Karnataka - 575025, India
| | - Gundla Uday Kiran
- Department of Chemical Engineering , National Institute of Technology Karnataka , Surathkal , Karnataka - 575025, India
| | - Chinta Sankar Rao
- Department of Chemical Engineering , National Institute of Technology Karnataka , Surathkal , Karnataka - 575025, India
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Bhookya J, Kumar Jatoth R. Fractional Order PID Controller Design for Multivariable Systems using TLBO. CHEMICAL PRODUCT AND PROCESS MODELING 2019. [DOI: 10.1515/cppm-2019-0061] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
The multivariable systems have to control by using multiloop controllers and each closed loop controller has unique characteristics. The successful model structure for design of control system is extremely subject to the accurate choice of the tuning parameters (
K_p, K_i, K_d, \lambda, \mu
) of the controller. The choice of optimal tuning parameters of Fractional Order PID (FOPID) controller leads to accurate controlling of desired level in multivariable system. Here, for multivariable system, a FOPID controller design based on the advanced optimization technique called Teaching Learning based optimization (TLBO) algorithm is proposed. The goal of paper is (i) The elimination of interaction between the control loops and (ii) Reference tracking along the disturbance in each loop. These objectives are satisfied by using four cost function, namely, integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE). Out of these cost functions, ITAE based FOPID controller design using TLBO algorithm provides better performance in terms of fast reference tracking and disturbance elimination in the loop. Moreover, the comparative analysis of convergence characteristics of each objective of the controller by using TLBO is presented. The simulation study confirms that the TLBO algorithm based FOPID controller for multivariable systems (2 × 2) are more robust and exhibits superior response with respect to other algorithm.
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Optimal tuning of decentralized fractional order PID controllers for TITO process using equivalent transfer function. COGN SYST RES 2019. [DOI: 10.1016/j.cogsys.2019.07.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Hanuma Naik R, Kumar DA, Gopikrishna Rao PV. Improved centralised control system for rejection of loop interaction in coupled tank system. Chem Ind 2019. [DOI: 10.1080/00194506.2019.1647800] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- R. Hanuma Naik
- RGM College of Engineering and Technology, Nandyal, India
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Besta CS, Chidambaram M. BLT Method for PI Controllers of Unstable Systems. INDIAN CHEMICAL ENGINEER 2018. [DOI: 10.1080/00194506.2016.1270779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Chandra Shekar Besta
- Process Control Group, Department of Chemical Engineering, Indian Institute of Technology, Madras, Chennai 600 036, India
| | - Manickam Chidambaram
- Process Control Group, Department of Chemical Engineering, Indian Institute of Technology, Madras, Chennai 600 036, India
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Besta CS, Chidambaram M. Design of Centralized PI Controllers by Synthesis Method for TITO Systems. INDIAN CHEMICAL ENGINEER 2017. [DOI: 10.1080/00194506.2015.1116961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Chandra Shekar Besta
- Department of Chemical Engineering, Indian Institute of Technology, Chennai 600 036, India
| | - Manickam Chidambaram
- Department of Chemical Engineering, Indian Institute of Technology, Chennai 600 036, India
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Affiliation(s)
- Chandra Shekar Besta
- Department of Chemical Engineering, Indian Institute of Technology – Madras, 600 036 Chennai, India
| | - Manickam Chidambaram
- Department of Chemical Engineering, Indian Institute of Technology – Madras, 600 036 Chennai, India
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