Hybrid grey box modelling of a pickling process

Sohlberg, Björn

doi:10.1016/j.conengprac.2004.11.005

Cited by 26 publications

(21 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is to be noted that the inclusion of metallic Fe as a reactant is indispensable for the calculation of the weight loss, while the acid consumption could also be obtained from the simple dissolution of the sale components with Fe 3+ as a final product . The above equations indicate that the weight loss must be partly attributed to the metal itself, and only a 74.1–80.6% accounts for the scale.…”

Section: Resultsmentioning

confidence: 99%

“…The scientific literature related to the acid pickling procedure refers to various aspects of the descaling process. The simulation of the production line with aim of the regulation of the acid effluent streams and stabilization of the acid concentrations in the individual tanks was performed with various methods . These works focus on the system operation and neglect many properties of the steel to be pickled since the only important parameter is the acid consumption referred to the coil surface area.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Study of the Acid Pickling of Low‐Alloyed Steels by Using a Descaling Workstation Simulating the Production Line

Péter

Sánta

Koós³

et al. 2015

steel research int.

View full text Add to dashboard Cite

A laboratory‐scale device was constructed for treating small‐size steel sheet specimens in an analogous manner to the acid pickling production line. This descaling simulator comprises three tanks for hydrochloric acid and another one for rinsing the samples, providing an automated sample transfer with a free selection of all processing parameters such as motion rate, residence time, temperature, etc. A detailed description of the simulator device will be given. Besides the pickling loss which was the primary parameter measured, surface roughness of the samples, Raman spectrum of the surface, and scale thickness were also determined in some cases. Scale‐related pickling loss and overpickling rate were determined for a number of steel qualities. The role of the pickling liquors of each tank was studied, and the descaling efficiency at various acid temperatures was assessed. A statistical analysis was used for estimating the fluctuation in the scale thickness on the hot‐rolled specimens. Results obtained with the descaling simulator were also checked with an optical imaging of the scale and a composition depth profile analysis. The scale thickness obtained from weight loss measurement was in good agreement with the direct scale thickness measurement.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study of the Acid Pickling of Low‐Alloyed Steels by Using a Descaling Workstation Simulating the Production Line

Péter

Sánta

Koós³

et al. 2015

steel research int.

View full text Add to dashboard Cite

show abstract

“…Deterministic ARX models calculated in [17] via blackbox LTI system identification were used to first estimate the measurement noise variance R q , by computing the difference between predicted and actual outputs. Then grey-box identification was used to estimate constant matrices A q , B q , D q and process noise covariance Q q from the experimental data for each mode [33], [34] (C = 1 0 ). Each model was generated from Part 1 of the experiment, in which each task is completed separately.…”

Section: B Model Creationmentioning

confidence: 99%

Mode Detection in switched pursuit tracking tasks: Hybrid estimation to measure performance in Parkinson's disease

Oishi

Ashoori

McKeown

2010

49th IEEE Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

Parkinson's disease (PD) is a neurodegenerative disorder that impairs motor skills, speech, and other voluntary movement, and may be associated with cognitive inflexibility. Fourteen PD subjects (both on and off medication) and 10 normal subjects performed a manual pursuit tracking task, in which the dynamics of the task suddenly change without explicit enunciation. The task dynamics have three modes, in which the error (the difference between the target and the user's cursor) is attenuated, exaggerated, or unchanged -hence we model the subject performing the tracking task as a hybrid system with arbitrary switching. Second-order stochastic LTI models of tracking performance in each mode are first obtained through system identification. We then use a multiple model adaptive estimation (MMAE) algorithm to determine a) whether each subject successfully adapted to the sudden change in tracking dynamics, and if so, b) the delay in switching to the new mode. These parameters were analyzed for all subjects, and found to be statistically significant across groups. While normal subjects consistently detected the change in task dynamics, PD subjects show considerably more difficulty in detecting the switch (especially off medication), and did not switch into the new mode as quickly as normal subjects. Our results suggest that PD subjects have considerable impairment in adapting to changing motor environments.

show abstract

“…The focus in this section is on modeling applications that deal with experimental data rather than data from virtual, simulated experiments, because simulation cases are typically applied to validate a pro-posed methodology (methodologies have been discussed above), while experimental studies are much more practically oriented. It can be seen in (Bhutani et al, 2006;Feil et al, 2004;Fiedler & Schuppert, 2008;Hinchliffe, Montague, Willis, & Burke, 2003;Mogk et al, 2002;Tian et al, 2001;Tsen et al, 1996;Vega, Lima, & Pinto, 2000), crystallization (Georgieva & de Azevedo, 2009;Georgieva, Meireles, & Feyo de Azevedo, 2003;Lauret, Boyer, & Gatina, 2000), metallurgic processes (Hu et al, 2011;Jia, Mao, Chang, & Zhao, 2011;Reuter et al, 1993;Sohlberg, 2005), distilla-tion columns (Chen et al, 2004;Mahalec & Sanchez, 2012;Safavi, Nooraii, & Romagnoli, 1999), drying processes (Cubillos & Acuna, 2007), thermal devices (Arahal, Cirre, & Berenguel, 2008), mechan-ical reactors (Nascimento et al, 1999) or milling (Aguiar & Filho, 2001; Kumar Akkisetty, Lee, Reklaitis, & Venkatasubramanian, 2010), for more details and references see Table2 -supplementary material. Since the number of applications is relatively large and most of which either use the standard serial approach consisting of material and/or energy balances in which the kinetics are represented by a nonparametric model or a parallel approach, only some approaches are discussed here, namely those that present solutions of more complex problems.…”

Section: Modelingmentioning

confidence: 99%

“…In industrial settings the attitude tellingly described by Sohlberg (2005), i.e. "you have to take what you can get" is dominant, but to yield high quality data the design of experiments should correspond to the objectives (Simutis, Oliveira, Manikowski, de Azevedo, & Luebbert, 1997).…”

Section: Design Of Experimentsmentioning

confidence: 99%

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

Stosch¹,

Oliveira²,

Peres³

et al. 2014

Computers & Chemical Engineering

360

201

View full text Add to dashboard Cite

Hybrid semi-parametric models consist of model structures that combine parametric and nonparametric submodels based on different knowledge sources. The development of a hybrid semi-parametric model can offer several advantages over traditional mechanistic or data-driven modeling, as reviewed in this paper. These advantages, such as broader knowledge base, transparency of the modeling approach and cost-effective model development, have been widely recognized, not only in academia but also in the industry.In this paper, the most common hybrid semi-parametric modeling and parameter identification techniques are revisited. Applications in the areas of (bio)chemical engineering for process monitoring, control, optimization, scaleup and model-reduction are reviewed. It is outlined that the application of hybrid semi-parametric techniques does not automatically lead into better results but that rational knowledge integration has potential to significantly improve model-based process operation and design.

show abstract

Hybrid grey box modelling of a pickling process

Cited by 26 publications

References 15 publications

Study of the Acid Pickling of Low‐Alloyed Steels by Using a Descaling Workstation Simulating the Production Line

Study of the Acid Pickling of Low‐Alloyed Steels by Using a Descaling Workstation Simulating the Production Line

Mode Detection in switched pursuit tracking tasks: Hybrid estimation to measure performance in Parkinson's disease

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

Contact Info

Product

Resources

About