A rigorous and efficient distillation column model for engineering and training simulators

An inevitable need for the skilled operators to increase the safety and productivity is not new to the chemical industry. Consequently, the training of operators is considered as a very important activity in the chemical industry. Conventional training methodologies are ineffective in training the operators for seldom-occurring perilous situations. The operator training simulator (OTS) is an alternative to train operators without actually endangering the plant and personnel. This contribution covers and discusses the need for OTS, applications of OTS in the chemical industry, issues related to OTS, salient features of a good OTS, commercial software packages used to build OTS, and training configurations. In this article, applications of OTS in the chemical industry reported in the open literature from 1990 to mid-2013 are also reviewed briefly. The review shows that OTS has been successfully used in many chemical industries. Finally, this article concludes by outlining the future directions. Overall, it provides a deeper understanding of many issues about the OTS to interested researchers, vendors, modeling engineers, and application engineers aiming to stimulate further developments in this area leading to improved OTS and their increased usage in the chemical industry.

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“…ProSim Olsen et al (1997) 4 Vinyl chloride monomer (VCM) plant -Developed a real-time dynamic simulator of the Hydro Rafnes VCM plant.…”

Section: Speedupmentioning

confidence: 99%

Operator training simulators in the chemical industry: review, issues, and future directions

Patle¹,

Ahmad²,

Rangaiah

2014

Reviews in Chemical Engineering

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“…In various fields of industry, dynamic simulators have been developed to train operators or to design new control techniques, see Alves et al (2005); Olsen et al (1997); Szafnicki et al (2004). To the best of the authors' knowledge, there exists only a few dynamic simulations devoted to helium cryogenic plants: • Maekawa, Ooba, Nobutoki, and Mito (2005) have simulated a 10 kW @ 4.5 K refrigerator used in a fusion experiment;…”

Section: Existing Cryogenic Modelling Approachesmentioning

confidence: 99%

“…Large scale cryogenic plants are continuous industrial processes, very similar to petroleum (Olsen, Endrestol, and Sira, 1997), chemical (Szafnicki, Narce, and Bourgois, 2004) or sugar industries (Alves, Normey-Rico, Merino, Acebes, and de Prada, 2005). They are composed with the same kind of components (heat exchangers, valves, turbines, compressors, phase separators, etc.)…”

Section: Introductionmentioning

confidence: 99%

A process and control simulator for large scale cryogenic plants

Bradu

Gayet

Niculescu

2009

Control Engineering Practice

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This paper presents a process and control simulator for industrial helium cryogenic plants controlled by Programmable Logic Controllers (PLC). This simulator can be used for different purposes such as operator training, test of the PLC programs or the optimization of the plant. The different component models used in the simulator are detailed and explained. Various large scale cryogenic plants used for the particle accelerator LHC (Large Hadron Collider) at CERN have been modeled and simulated. The good agreement between the simulation results and the dynamic behaviour of real plants is demonstrated with experimental results. Various discussions complete the presentation.

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“…Nevertheless, the process industry is not characterized by a scarce knowledge on the involved physicochemical processes. Indeed, detailed models for some equipment/plants have already existed for the last two decades (e.g., distillation columns [7]). Therefore, throwing out all this deep knowledge and just relying on the decisions inferred by data-driven machines would be risky.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Grey-Box Modeling Methodology via Data Reconciliation and SOS Constrained Regression

2019

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Developing the so-called grey box or hybrid models of limited complexity for process systems is the cornerstone in advanced control and real-time optimization routines. These models must be based on fundamental principles and customized with sub-models obtained from process experimental data. This allows the engineer to transfer the available process knowledge into a model. However, there is still a lack of a flexible but systematic methodology for grey-box modeling which ensures certain coherence of the experimental sub-models with the process physics. This paper proposes such a methodology based in data reconciliation (DR) and polynomial constrained regression. A nonlinear optimization of limited complexity is to be solved in the DR stage, whereas the proposed constrained regression is based in sum-of-squares (SOS) convex programming. It is shown how several desirable features on the polynomial regressors can be naturally enforced in this optimization framework. The goodnesses of the proposed methodology are illustrated through: (1) an academic example and (2) an industrial evaporation plant with real experimental data.

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A rigorous and efficient distillation column model for engineering and training simulators

Cited by 14 publications

References 6 publications

Operator training simulators in the chemical industry: review, issues, and future directions

Operator training simulators in the chemical industry: review, issues, and future directions

A process and control simulator for large scale cryogenic plants

A Systematic Grey-Box Modeling Methodology via Data Reconciliation and SOS Constrained Regression

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