Abstract
Present paper attempts to review the applications of advanced control strategies based on artificial intelligence techniques and its hybrid counterparts applicable in process industry. This chemical process industry may be textile, paper, water purification plant, sugar mill, leather, steel, or any sub-process which may be common in all these industries. It covers an exhaustive literature review.
Access provided by Autonomous University of Puebla. Download conference paper PDF
Similar content being viewed by others
Keywords
1 Introduction
The process modeling involves the development of dynamic mathematic models with state space forms describing the thermal state in the heated and cooled objects. Dynamic model-based optimal control strategies for both batch and continuous thermal processes in terms of the maximum principle, dynamic programming, heuristic search, etc. are also proposed in this study. The target of the development of optimal control for the heating processes is to provide the optimal heating patterns based on the given criteria and constraints associated with dynamic models and others. A complete hierarchical computer control structure for heating processes is proposed [1]. Nixon discussed recent advances in the use of programmable logic controllers for batch house control systems [2]. Jaggers explored that more accurate sensors make possible tighter control loops [3]. Taniguchi et al., applied advanced technology for cold strip mill installed in Japan that improved gage accuracy. The specifications and features of the mill are set forth, and the gage control system is examined in more detail [4] (Table 1).
Constantinescu et al., presented the hierarchical structure of an integrated control and management system for industrial applications. Its main levels are: distributed process control, process operations support and plant management. This structure has been specialized in the case of a cement plant. The architecture of a gracefully degrading advanced control station is given which supports the process operations in the case of medium size applications, such as cement factories. Result of the global and local repair actions, effect of the spare processing elements and influence of different types of faults have been emphasized. The main advantages of the presented advanced control station are that only the redundancy manager and the application software have been especially designed. This has led to a low cost implementation of the station [5].
Lewis discussed a practical process industry problem of considerable difficulty and defeated conventional approaches [6]. The chemical company Du Pont uses advanced process control to improve operability of plants. This enhances safety, protecting people and the environment, and defines product quality within narrow limits. It reduces costs and increases profits. It is shown that the use of advanced process control enhances safety and increases efficiency [7]. Aslin presented advanced control process for pH control in a sugar industry [8]. Jones, discussed the application of dynamic simulators in industry. The use of training simulators is quite widespread within certain process industry sectors such as refining, petrochemical and oil and gas. Due to advances in technology and a wider awareness of the benefits these simulators are increasingly used for engineering and operations purposes. In the future the distinction between training and engineering simulators can be expected to be less clear. It is thought that soon a single tool will be available to enable a simulator to be used from conceptual design through to operations support [9]. Gough shown that the use of orthogonal filters allows transfer function identification [10]. Vachtsevanos et al., presented a new technique to optimize the slasher control parameters [11].
Heaven et al., examined some of the traditional parametric identification techniques [12]. Cameron presented the results of field applications in mercury reclaim and sulfur recovery [13]. Model and rule-based controllers can provide adaptive feature within a robust controller design framework [14].
Lightbody et al., proposed to utilize model-based approaches to improve the control performance of an industrial polymerization reactor. This involves the development of a process model using system identification techniques, the simulation of the plant within the Simulink environment to allow for the design and validation of control strategies. From these studies a Smith predictor was implemented to significantly improve the polymer viscosity control. Finally, a hardware platform is developed to facilitate the implementation of sophisticated algorithms such as recursive least squares that could not be accommodated on the present DCS [15]. Rigler et al., attempted the simulation and control of a multiple stand hot strip mill [16]. Arruda et al., proposed integrating different software resources with an application example of an oil industry [17]. Graebe Goodwin et al., described three different central strategies that were implemented and evaluated viz. a dithering controller, a linear cascade controller, and a nonlinear cascade controller [18]. Wilkinson et al., reviewed chronologically the status of fuzzy logic from the start to the present scenario [19]. Zhang Qiping et al., applied APC technique on an industrial process and shown that APC technique is capable of mastering and improving the key process targets [20]. Xiaoming Jin et al., performed advanced process control techniques [21]. Li-hong Dai et al., applied a method based on human machine intelligence [22]. Ren-Chu et al., designed six MVC controllers for the ammonia synthesis process. The final industrial application resulted in good control performance and economic benefit [23]. Juneja et al., applied MPC strategy on a lime kiln process. The lime kiln model is perturbed and the responses achieved are compared for controller designed based on MPC strategy [24]. Satisfactory system performances have been achieved by implementing MPC on real plant [25]. Juneja et al., showed that FOPDT model resembles consistency parameter [26, 27]. MOR and MIMO control system analysis techniques are depicted with the aid of flow chart [28, 29]. Modeling and control features of lime kiln process are attempted [30].
Yunhui Luo et al. [31] gives an improved version for the identification of FOPDT model when the response data is very less. So to approximate the step responses a B-spline series expansion are used which in turn provide more operative interpolation values for modeling computation. Least squares method diminishes the eccentricity of response between identified model and actual process by adjusting the error weight coefficients. Anindo Roy et al., [32] used the solidity framework of Hermite-Biehler theorem for FOPDT process model. The resultant simulation results illustrates that this frame work can be efficiently used for the synthesis of PID controller of the FOPDT model. The proposed method gives comparably more superior results over traditional PID tuning approaches.
Qiang Bi et al. [33] projected a robust identification technique that has been derived from a step test. This method gives improved identification result then that of the prevailing method under step testing and can be easily applied to PID auto tuning. IMC-based control system displays better control action in comparison to Ziegler Nichol’s based control system [34].
2 Conclusion
An attempt has been done to review the applications of advanced control strategies which are applicable in process industry. It covers many control strategies viz. programmable logic control, distributed control system, system identification, multivariable control system, modeling and simulation, model predictive control, etc. And many application areas such as, cold strip mill, sugar mill, paper machine head box, polymerization reactor, multiple stand hot strip mill, ammonia synthesis process, limekiln process.
References
Y. Lu, Application of modern control strategies to thermal processes in metal industry. 1987 American Control Conference (1987), pp. 1053–1058
S. W. Nixon, Advancements in batching control systems [glass industry]. Conference Record of the 1988 IEEE Industry Applications Society Annual Meeting, vol 2 (1988), pp. 1073–1075
H. T. Jaggers, A new measurement and control system for rubber calendaring. IEEE Conference Record of 1988 Fortieth Annual Conference of Electrical Engineering Problems in the Rubber and Plastics Industries (1988), pp. 28–34
T. Taniguchi, H. Tanaka, T. Kawabata, E. Yasui, T. Ooi, A new control system for reversing cold strip mill. Conf. Record IEEE Indus. Appl. Soc. Ann. Meet. 2, 1472–1477 (1989)
C. Constantinescu, C. Sandovici, Towards the fault-tolerant advanced control of a cement plant. 1990 Second International Conference on Factory 2001-Integrating Information and Material Flow (1990), pp. 168–172
D. G. Lewis, Experiences in the application of advanced control engineering in the process industry. IEE Colloquium on Case Studies in Industrial Control (1990), pp. 3/1–3/3
J. P. McCormick, K. J. Kelly, Computer based process control applications. IEE Colloquium on Case Studies in Industrial Control (1990), pp. 4/1–4/4
P. P. Aslin, Connoisseur applications in the food industry. IEE Colloquium on Automation and Control in Food Processing (1992), pp. 8/1–8/4
D. R. Jones, Current application of simulators in the process industries and future trends. IEE Colloquium on Operator Training Simulators (1992), pp. 3/1–3/4
B. Gough, Advanced adaptive control applications (in industry). Conference Record on Pulp and Paper Industry Technical Conference (1992), pp. 122–132
G. Vachtsevanos, J. L. Dorrity, A. Kumar, S. S. Kim, Advanced application of statistical and fuzzy control to textile processes. [Proceedings] IEEE 1993 Annual Textile, Fiber and Film Industry Technical Conference (1993), pp. 6/1–6/8
E.M. Heaven, T.M. Kean, I.M. Jonsson, M.A. Manness, K.M. Vu, R.N. Vyse, Applications of system identification to paper machine model development and controller design. Proc. IEEE Int. Conf. Control Appl. 1, 227–233 (1993)
M. M. Cameron, Use of advanced controls in environmental remediation. Industry Applications Society 40th Annual Petroleum and Chemical Industry Conference (1993), pp. 177–183
P. J. King, K. J. Burnham, D. J. G. James, Combined model-based and rule-based controller for process control. IEE Colloquium on Advances in Control in the Process Industries: An Exercise in Technology Transfer (Digest No. 1994/081) (1994), pp. 4/1–4/5
G. Lightbody, G. W. Irwin, A. Taylor, K. Kelly, J. McCormick, Advanced control of a polymerisation reactor. IEE Colloquium on Advances in Control in the Process Industries: An Exercise in Technology Transfer (Digest No. 1994/081) (1994), pp. 7/1–7/3
G. Rigler, H. Aberl, W. Staufer, K. Aistleitner, K. H. Weinberger, Improved rolling mill automation by means of advanced control techniques and dynamic simulation. Proceedings of 1994 IEEE Industry Applications Society Annual Meeting, vol 3 (1994), pp. 2030–2037
L. Arruda, Amaral, Gomide, An object-oriented environment for control systems in oil industry. Proc. IEEE Int. Conf. Control Appl. 2, 1353–1358 (1994)
Graebe, Goodwin, West, Stepien, An application of advanced control to steel casting. Proc. IEEE Int. Conf. Control Appl. 3, 1533–1538 (1994)
J. Wilkinson, Additional advances in fuzzy logic temperature control. IAS ‘95. Conference Record of the 1995 IEEE Industry Applications Conference Thirtieth IAS Annual Meeting, vol 3 (1995), pp. 2721–2725
Z. Qiping, G. Jinbiao, W. Xiangyu, W. Youhua, An industrial application of APC technique in fluid catalytic cracking process control. SICE 2003 Annual Conference (IEEE Cat. No.03TH8734), vol 1 (2003), pp. 530–534
X. Jin, G. Rong, S. Wang, Advanced process control and its application to industrial distillation chain. Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788), vol 4 (2004), pp. 3400–3404
D. Li-Hong, C. Xue-Bo, Y. Zheng-Jun, F. Yong-Jun, Method of intelligent object-oriented process control design and application on wire cooling system. 2009 Chinese Control and Decision Conference (2009), pp. 5775–5780
H. Ren-Chu, W. Hao, G. Xiao-Jing, Advanced process control technology implementation in ammonia plant. 2013 25th Chinese Control and Decision Conference (CCDC) (2013), pp. 1200–1204
P. K. Juneja, A. Ray, Robustness analysis using prediction based control strategy for an industrial process. 2013 IEEE International Conference on Signal Processing, Computing and Control (ISPCC) (2013), pp. 1–3
S. M. Zanoli, C. Pepe, M. Rocchi, G. Astolfi, Application of advanced process control techniques for a cement rotary kiln,” 2015 19th International Conference on System Theory, Control and Computing (ICSTCC) (2015), pp. 723–729
P. K. Juneja, M. Chaturvedi, S. Suman, K. Antil, Modeling of Stock Consistency in the Approach Flow System of the Headbox,” 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU) (2018), pp. 1–4
P. K. Juneja, M. Chaturvedi, A. K. Ray, V. Joshi, N. Belwal, Control of Stock Consistency in Head Box Approach Flow System. 2019 International Conference on Innovative Sustainable Computational Technologies (CISCT) (2019), pp. 1–5
P. K. Juneja, A. Sharma, A. Sharma, R. R. Mishra, F. S. Gill, A Review on Model Order Reduction Techniques for Reducing Order of Industrial Process Transfer Function Model. 2020 International Conference on Advances in Computing, Communication & Materials (ICACCM) (2020), pp. 346–350
P. Juneja, A. Sharma, V. Joshi, H. Pathak, S. K. Sunori, A. Sharma, Delayed complex multivariable constrained process control—a review. 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) (2020), pp. 569–573
P. K. Juneja, S. Sunori, A. Sharma, A. Sharma, V. Joshi, Modeling, control and instrumentation of lime kiln process: a review. 2020 International Conference on Advances in Computing, Communication and Materials (ICACCM) (2020), pp. 399–403
Y. Luo, W. Cai, H. Liu, R. Song, Identification of first-order plus dead-time model from less step response data. 9th IEEE Conference on Industrial Electronics and Applications (2014), pp. 1410–1415
A. Roy, K. Iqbal, PID controller tuning for the first-order-plus-dead-time process model via Hermite-Biehler theorem. ISA Trans. 44(3), 363–378 (2005)
Q. Bi, W.-J. Cai, E.-L. Lee, Q.-G. Wang, C.-C. Hang, Y. Zhang, Robust identification of first-order plus dead-time model from step response. Control. Eng. Pract. 7(1), 71–77 (1999)
S. K. Sunori, P. K. Juneja, M. Chaturvedi, N. Agarwal, Design and analysis of control systems for heat exchanger system of sugar mill. 8th IEEE International Conference on CICN 2016 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Juneja, P.K. et al. (2023). Potential Applications of Advanced Control System Strategies in a Process industry—A Review. In: Zhang, YD., Senjyu, T., So-In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. Lecture Notes in Networks and Systems, vol 396. Springer, Singapore. https://doi.org/10.1007/978-981-16-9967-2_9
Download citation
DOI: https://doi.org/10.1007/978-981-16-9967-2_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9966-5
Online ISBN: 978-981-16-9967-2
eBook Packages: EngineeringEngineering (R0)