Startup of a distillation column using intelligent control techniques

Fabro, João Alberto; Arruda, Lúcia Valéria Ramos de; Neves, Flávio

doi:10.1016/j.compchemeng.2005.09.012

Cited by 29 publications

(20 citation statements)

References 9 publications

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“…The chosen solution is defined as the best second-order TSK FS representing the soft sensor evolved by the NNCoGFS approach chosen among all runs of the considered fold. Table 5 shows the characteristics of the best solution provided by NNCoGFS when compared with other benchmark approaches: the RecurrNN previously described, the NNPredCont that is a soft sensor proposed (Fabro et al 2005) to be used in the predictive control of the same problem addressed here and the best solution obtained by the well-known statistical method, the principal component analysis (PCA).…”

Section: Neuro Selection and Coevolution Phasesmentioning

confidence: 98%

“…The main goal of the procedure is to reach a production condition as soon as possible. This situation is achieved when a set of variables (such as feed flow, reboiler and condenser levels and production concentration) achieve their steady state values (Fabro et al 2005).…”

Section: The Binary Distillation Columnmentioning

confidence: 99%

“…In Fabro et al (2005) neural-based estimators (neural soft sensors) have been developed to infer the top and bottom compositions by using the temperature readings.…”

Section: The Binary Distillation Columnmentioning

confidence: 99%

“…The simulation data has been obtained from Fabro et al (2005), in which a binary distillation column that separates water from methanol has been modeled by means of HYSYS Ó1 .…”

Section: The Binary Distillation Columnmentioning

confidence: 99%

“…The first problem addressed in Sect. 4 is the same described in Fabro et al (2005), where a neural network soft sensor has been designed to infer product composition in a distillation column. The second problem is addressed in Ansari and Tadé (2000) where a mechanistic nonlinear model is developed to replace an analyzer in a heavy oil fractionator.…”

Section: Introductionmentioning

confidence: 99%

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A neuro-coevolutionary genetic fuzzy system to design soft sensors

2008

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This paper addresses a soft computing-based approach to design soft sensors for industrial applications. The goal is to identify second-order Takagi-Sugeno-Kang fuzzy models from available input/output data by means of a coevolutionary genetic algorithm and a neuro-based technique. The proposed approach does not require any prior knowledge on the data-base and rule-base structures. The soft sensor design is carried out in two steps. First, the input variables of the fuzzy model are pre-selected from the secondary variables of a dynamical process by means of correlation coefficients, Kohonen maps and Lipschitz quotients. Such selection procedure considers nonlinear relations among the input and output variables. Second, a hierarchical coevolutionary methodology is used to identify the fuzzy model itself. Membership functions, individual rules, rule-bases and fuzzy inference parameters are encoded into each hierarchical level and a shared fitness evaluation scheme is used to measure the performance of individuals in such levels. The proposed methodology is evaluated by developing soft sensors to infer the product composition in petroleum refining processes. The obtained results are compared with other benchmark approaches, and some conclusions are presented.

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Section: Neuro Selection and Coevolution Phasesmentioning

confidence: 98%

Section: The Binary Distillation Columnmentioning

confidence: 99%

“…In Fabro et al (2005) neural-based estimators (neural soft sensors) have been developed to infer the top and bottom compositions by using the temperature readings.…”

Section: The Binary Distillation Columnmentioning

confidence: 99%

“…The simulation data has been obtained from Fabro et al (2005), in which a binary distillation column that separates water from methanol has been modeled by means of HYSYS Ó1 .…”

Section: The Binary Distillation Columnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A neuro-coevolutionary genetic fuzzy system to design soft sensors

2008

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Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Dutta

Upreti

2021

Can J Chem Eng

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In the last three decades, artificial intelligence (AI) has been increasingly and vigorously utilized for process control in chemical, biochemical, and biomedical engineering. These disciplines frequently involve fairly sophisticated processes under risks of operational upsets, and thus have an ever increasing demand of superior control strategies. As the deployment of AI in process control pushes the limits in this regard, the research advances, which are multitudinous and varied, need to be assimilated and organized to help promote further utilization and progress in the field. To that end, we examine more than 280 relevant research publications, and systematically collate the information. The AI‐based technologies are classified, and their over‐arching control paradigm is presented. Common AI‐based control technologies are then presented, which are based on expert systems, fuzzy logic, artificial neural networks, nature‐inspired algorithms, and hybrid approaches. Their working principles, types, and implementations are summarized along with advantages, limitations, and comparisons, if available. Important applications in the above disciplines are included with the help of tables that capture important details. A discussion is also provided on advanced and newly emerging AI‐based control technologies with pertinent applications. Overall trends are analyzed, and future prospects are identified based on the survey.

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Hierarchical optimal control of a binary distillation column

Ramezani

Sadati

2018

Optim Control Appl Methods

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Summary This paper presents a two‐level optimal control for the binary distillation column. From the control point of view, the binary distillation column is a high‐order system consisting of several interconnected subsystems (trays) that aim to maximize output purity. Controlling the system in a hierarchical manner not only decreases complexity and solution time but also can improve the control structure's reliability. In this study, the distillation column is decomposed into NT second‐order subsystems, each representing one stage (one tray). Accordingly, the cost function is decomposed, and thus, the overall problem is converted into NT lower‐order subproblems where each tray has its own controller. A two‐level interaction prediction approach provides optimal control for the overall system. The task of the first level is to solve the subproblems using the predicted values of the coordination parameters, whereas the second level acts as a coordinator to update the coordination parameters. Simulation results show the capability and efficacy of the proposed two‐level control method in finding the optimal solution with less complexity and lower solution time than those of the centralized method.

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Startup of a distillation column using intelligent control techniques

Cited by 29 publications

References 9 publications

A neuro-coevolutionary genetic fuzzy system to design soft sensors

A neuro-coevolutionary genetic fuzzy system to design soft sensors

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Hierarchical optimal control of a binary distillation column

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