Information transfer methods in causality analysis of process variables with an industrial application

Naghoosi, Elham; Huang, Biao; Domlan, Elom Ayih; Kadali, Ramesh

doi:10.1016/j.jprocont.2013.02.003

Cited by 34 publications

(14 citation statements)

References 13 publications

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“…As discussed in the previous sections, one of the most prominent methods in this group is transfer entropy. Naghoosi et al (2013) [84] based their approach for causality analysis on transfer entropy with the additional application of mutual information to reduce the computational complexity of the naïve approach. Other approaches found in the literature focused on data stationarity and exogenous variables.…”

Section: Domain and Conceptual Workmentioning

confidence: 99%

Causal Discovery in Manufacturing: A Structured Literature Review

Vuković

Thalmann

2022

JMMP

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Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality and sustainability. ML outperforms traditional approaches in many cases, but its complexity leads to unclear bases for decisions. Thus, acceptance of ML and, concomitantly, Industry 4.0, is hindered due to increasing requirements of fairness, accountability, and transparency, especially in sensitive-use cases. ML does not augment organizational knowledge, which is highly desired by manufacturing experts. Causal discovery promises a solution by providing insights on causal relationships that go beyond traditional ML’s statistical dependency. Causal discovery has a theoretical background and been successfully applied in medicine, genetics, and ecology. However, in manufacturing, only experimental and scattered applications are known; no comprehensive overview about how causal discovery can be applied in manufacturing is available. This paper investigates the state and development of research on causal discovery in manufacturing by focusing on motivations for application, common application scenarios and approaches, impacts, and implementation challenges. Based on the structured literature review, four core areas are identified, and a research agenda is proposed.

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Section: Domain and Conceptual Workmentioning

confidence: 99%

Causal Discovery in Manufacturing: A Structured Literature Review

Vuković

Thalmann

2022

JMMP

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“…From the cross‐validation results presented in Figure , it can be observed that the prediction performance of the developed soft sensor is satisfactory. Interested readers are referred to Naghoosi et al for further details about mutual information analysis methods. The soft sensor predictions are expected to be ±5% of laboratory measurements based on off‐line model validation results.…”

Section: Soft Sensing In Oil Sands Extraction Processesmentioning

confidence: 99%

Soft sensor solutions for control of oil sands processes

et al. 2013

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Oil sands development is both a costly and technically complex business with potential concerns in land use, water consumption and greenhouse gas emissions. Therefore, it is of practical interest to further investigate novel techniques to improve profitability while diligently maintaining environmental compliance. Our approach for finding solutions to achieve this objective is to develop innovative strategies for advanced monitoring, optimisation and control of plant operations. Development of reliable process models is a key requirement for investigating the behaviour of complex systems. Such descriptive models can help to improve analysis, simulation, optimisation, design, control and operation of process systems at both micro and macro levels. This paper presents a summary of some of the successful applications focussed on development and implementation of inferential process models, also known as soft sensors for oil sands processes.Keywords: soft sensor, oil sands, bitumen extraction INTRODUCTIONT he very nature of oil sands extraction processes is to separate highly viscous bitumen from other components, which are mainly water and solids (e.g. clays and sand). This task is performed in multiple steps that involve: breaking the feed received from the mine into smaller chunks; screening out oversized materials; preparing slurry through addition of hot process water and separating bitumen, solids and water through a series of gravity and/or centrifugal separation units and usage of process aids. Despite the apparent simplicity of the overall process, different factors render the operation of the process quite complex. Some of these factors are: variation of the feed quality and rate; mechanical and electrical constraints related to equipment size; existence of multiple operating conditions; restrictions of downstream processes; and availability of process utilities such as hot process water or steam.Oil sands development is both a costly and technically complex business with potential concerns raising due to land use, water consumption and greenhouse gas emissions. Therefore, real-time process monitoring and control techniques are needed in many areas in order to ensure a smooth operation, achieve high throughput, stay close to the optimal boundaries of the process operating envelope, and minimise environmental footprints. An essential prerequisite for a successful process monitoring and control scheme is continuous collection of reliable and accurate data reflecting the equipment and plant operating status. However, extraction operations are naturally subject to harsh operating conditions with climate (ambient temperature and rain) playing a secondary role as an enhancement agent. Such conditions would affect reliability, accuracy and efficiency (i.e. service factor) of the instrumentation system. Moreover, measurements of quality variables (e.g. stream composition) are normally obtained through on-line quality analysers or off-line laboratory analysis with time delays of a few hours. Discontinuity and signif...

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“…Examples of such features include time delays, attenuation, transfer of information, and conditional probability relations. Examples of methods include the quantification of the nonlinearity of time series (Thornhill, 2005), the transfer entropy between two time series (Bauer et al, 2007a;Naghoosi et al, 2013), and the non-linear mutual prediction between two time series (Bauer et al, 2007b;Stockmann et al, 2012). However, the current methods are applicable only to uni-rate systems, that is, systems whose measurements are all available with the same sampling rate.…”

Section: Introductionmentioning

confidence: 99%

Determining the propagation path of a disturbance in multi-rate process and electromechanical systems

Cecı́lio

Ottewill

Thornhill

2016

Control Engineering Practice

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This paper proposes a multi-rate method to identify the propagation path of a persistent disturbance in an enlarged system envelope which includes the process plant and its electromechanical equipment. The need to integrate process and equipment diagnosis has been highlighted by industrial commentators.However, process and electromechanical measurements often have different sampling rates. The multi-rate method proposed extends a state-of-the-art propagation path method so that it combines fast-sampled electromechanical measurements and slow-sampled process measurements. The method is based on non-linear mutual prediction, which yields the directionality in the relationship between two time series. The method was demonstrated and validated, giving the expected outcome in an experimental case study, in which the root cause and propagation path of the disturbance were known.

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Information transfer methods in causality analysis of process variables with an industrial application

Cited by 34 publications

References 13 publications

Causal Discovery in Manufacturing: A Structured Literature Review

Causal Discovery in Manufacturing: A Structured Literature Review

Soft sensor solutions for control of oil sands processes

Determining the propagation path of a disturbance in multi-rate process and electromechanical systems

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