System Identification of Industrial Debutanizer Column

Fatima, Syeda Anmol; Zabiri, Haslinda; Taqvi, Syed Ali Ammar; Ramli, Nasser Mohamed

doi:10.1109/iccsce47578.2019.9068541

Cited by 18 publications

(10 citation statements)

References 20 publications

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“…Outrossim, o sistema de controle altera a função de comportamento de acordo com o intemperismo, que é realizado por meio de ensaios (Fatima et al, 2019). Isto significa que a tomada de decisão do sistema de controle ocorre em tempo-real, porém é necessário aguardar o ensaio de intemperismo para validação das ações de controle.…”

Section: Parâmetros Do Processounclassified

Monitoramento Da Produção Enxuta Com Inteligência Artificial / Lean Production Monitoring With Artificial Intelligence

Mazzoni

Barcelos

2021

BJD

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The liquefied petroleum gas (LPG) production industry, commercially known as residential gas, presents challenges such as continuous production, the need to control the production process in real time and the variability of product quality due to the behavior of parameters thermodynamics of production. In this scenario, this work presents two distinct algorithms and proposes methods to monitor the quality of LPG in real time. At the end, an analysis tool and the control measures that must be implemented will be presented to guarantee the reduction of variability according to the principles of lean production.

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Section: Parâmetros Do Processounclassified

Monitoramento Da Produção Enxuta Com Inteligência Artificial / Lean Production Monitoring With Artificial Intelligence

Mazzoni

Barcelos

2021

BJD

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“…Outrossim, o sistema de controle altera a função de comportamento de acordo com o intemperismo, queé realizado por meio de ensaios (Fatima et al, 2019). Isto significa que a tomada de decisão do sistema de controle ocorre em tempo-real, porémé necessário aguardar o ensaio de intemperismo para validação das ações de controle.…”

Section: O Controle Do Processounclassified

Monitoramento da Produção Enxuta com Inteligência Artificial

Mazzoni

Barcelos

2020

Anais Do Congresso Brasileiro De Automática 2020

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A indústria de produção de gás liquefeito de petróleo (GLP), comercialmente denominado como gás de cozinha, apresenta desafios como a produção contínua, a necessidade do controle de processo de produção em tempo real e a variabilidade da qualidade do produto devido ao comportamento dos parâmetros termodinâmicos da produção. Neste cenário, este trabalho apresenta dois algoritmos distintos e propõe métodos para monitorar a qualidade do GLP em tempo real. Ao final será apresentado uma ferramenta de análise e as medidas de controle que devem ser implementadas para garantir a redução da variabilidade de acordo com os princípios de produção enxuta.

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“…In process industries, the demand for quality products with low rejection rate, minimal downtime, and safer operations are ever present. To meet these demands, advanced process control (APC) is considered to be one of the most renowned technologies in improving the efficiency of large‐scale process plants 1–3. Advancements in APC have added enormous benefits to various process industries including petrochemical, steel, power, cement, and desalination.…”

Section: Introductionmentioning

confidence: 99%

A Review on Data‐Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes

et al. 2021

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Fault detection and diagnosis for process plants has been an active area of research for many years. This review presents a concise overview on supervised and unsupervised data‐driven approaches for fault detection and diagnosis in chemical processes. Methods based on supervised and unsupervised data‐driven techniques are reviewed, and the challenges in the field of fault detection and diagnosis have also been highlighted. It is observed that most of the data‐driven approaches are application specific, in that no single method can be used to obtain a generalized solution for nearly all purposes. The methods reviewed differ significantly from one to the other, and hence it is difficult to generalize any key similarity. The majority of the works proposed in the literature focused mainly on single fault detection, and do not cover the root‐cause diagnosis of the detected faults. In cases where both detection and diagnosis are performed, the focus is mainly for a single fault. In addition, majority of the articles do not extend to the diagnosis of the root cause for multiple and simultaneous faults.

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System Identification of Industrial Debutanizer Column

Cited by 18 publications

References 20 publications

Monitoramento Da Produção Enxuta Com Inteligência Artificial / Lean Production Monitoring With Artificial Intelligence

Monitoramento Da Produção Enxuta Com Inteligência Artificial / Lean Production Monitoring With Artificial Intelligence

Monitoramento da Produção Enxuta com Inteligência Artificial

A Review on Data‐Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes

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