Application of Kalman filter to determination of coal liquefaction mechanisms using discrete time models

Şimşek, Emir H.; Güleç, Fatih; Kavuştu, Hakan

doi:10.1016/j.fuel.2017.06.004

Cited by 12 publications

(12 citation statements)

References 36 publications

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“…There is random white noise in the mine wind speed monitoring, which is consistent with the use conditions of the Kalman filter [8,9]. Kalman filters are used widely in various fields [10][11][12][13]. Kalman filter can treat the disordered and fluctuating data collected by the roadway wind speed sensors to get valid mine wind speed data for analysis and therefore improve the accuracy of wind speed monitor by effectively shielding invalid measurement values.…”

Section: Introductionmentioning

confidence: 87%

Application of Adaptive Kalman Filter in Online Monitoring of Mine Wind Speed

Huang¹,

Liu²,

Deng³

et al. 2019

Preprint

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The underground complicated testing environment and the fan operation instability cause large random errors and outliers of the wind speed signals. The outliers and large random errors result in distortion of mine wind speed monitoring, which possesses safety hazards in mine ventilation system. Application of Kalman filter in velocity monitoring can improve the accuracy of velocity measurement and eliminate the outliers. Adaptive Kalman Filter was built by automatically adjusting process noise covariance and measurement noise covariance depending on the differences between measured and expected speed signals. We analyzed the fluctuation of airflow flow using data of wind speed flow and distribution characteristics of the tunnel obtained by the Laser Doppler Velocimetry system (LDV) studies. A state-space model was built based on the tunnel airflow fluctuations and wind speed signal distribution. The adaptive Kalman Filter was calculated according to the actual measurement data and the Expectation Maximization (EM) algorithm. The adaptive Kalman filter was used to shield fluid pulsation while preserving system-induced fluctuations. Using the Kalman filter to treat offline wind speed signal acquired by LDV, the reliability of Kalman filter wind speed state model and the characteristics of adaptive Kalman Filter were investigated. Results showed that the adaptive Kalman filter effectively eliminated the outliers and reduced the root-mean-squares error (RMSE), and the adaptive Kalman filter had better performance than the traditional Kalman filter in eliminating outliers and reducing RMSE. Field experiments in online wind speed monitoring were conducted using the optimized adaptive Kalman Filter. Results showed that adaptive Kalman filter treatment could monitor the wind speed with smaller RMSE compared with LVD monitor. The study data demonstrated that the adaptive Kalman filter is reliable and suitable for online signal processing of mine wind speed monitor.

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Section: Introductionmentioning

confidence: 87%

Application of Adaptive Kalman Filter in Online Monitoring of Mine Wind Speed

Huang¹,

Liu²,

Deng³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The Kalman filter operates in two steps; first, the current state of the variables is estimated, along with their uncertainties in the prediction step. Second, once the output of the next measurement is obtained, these estimates are updated using a weighted average, with more weight being given to estimates with higher certainty [28]. Therefore, the reaction rate constants of the complex biomass pyrolysis models can be predicted using the Kalman filter method.…”

Section: Kalman Filter Methodsmentioning

confidence: 99%

“…Therefore, the reaction rate constants of the complex biomass pyrolysis models can be predicted using the Kalman filter method. Further information about the Kalman filtering theory and its application in the prediction of kinetic parameters of different chemical processes using MATLAB has been presented in previous studies [26][27][28][29].…”

Section: Kalman Filter Methodsmentioning

confidence: 99%

“…The char also consists of ash and trace amounts of metals such as Na, K, Hg, Cd, Be, Se, Sb, As, Pb, Zn, Cr, Co, Ni [49][50][51], which may catalyze the pyrolysis reaction with an additional cracking reaction [52][53][54], supporting a serial reaction from bio-oil to gases as suggested in Models 1, 2, 4, and 5. On the other hand, free radicals may polymerize and produce high-molecular-weight products [28,29,55], as suggested in the reversible steps in Models 2 and 5. It is known that biomass-based waxes consist of a group of chemicals including n-alkanes, branched-chain alkanes, alkenes, esters, free fatty acids, primary and secondary fatty alcohols, aldehydes, ketones, and many others [53,56].…”

Section: Proposed Pyrolysis Mechanismsmentioning

confidence: 99%

“…It is also relatively difficult to experimentally investigate the reversibility of the pyrolysis reactions, while it may be one of the crucial steps for a better definition of the biomass pyrolysis reactions. However, recent findings in computational modeling stand out as a great solution for the identification of these kinds of reaction mechanisms and for validation of the modeling results with the experimental findings [26][27][28][29]. In previous studies, a group of different coal liquefaction mechanisms, which consists of reversible and irreversible steps, were suggested for a variety of different coals, and the most promising liquefaction mechanisms were clarified and determined using the Kalman filter method, which has recently become an attractive technique used in chemical reactions, with the validation of experimental results [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Biomass Pyrolysis Mechanisms and Kinetics: Application of the Kalman Filter

2021

Self Cite

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In order to predict the pyrolysis mechanisms of four different biomasses (Asbos (Psilocaulon utile), Kraalbos (Galenia africane), Scholtzbos (Pteronia pallens), and palm shell), a novel method called Kalman filter was investigated and the results were compared by regression analysis. Both analyses were applied to five different generalized biomass pyrolysis models consisting of parallel and serial irreversible-reversible reaction steps. The models consisting of reversible reactions in addition to parallel pyrolysis steps demonstrated a better fit with the experimental results. The pyrolysis step from biomass to bio-oil has the highest reaction rates compared with the other pyrolysis steps defined in the models. The Kalman filter is thus defined as a promising filtering and prediction method for the estimation of detailed pyrolysis mechanisms and model parameters, using minimum experimental data.

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Utilisation of coal tar naphthalene oil fractions for the synthesis of value-added chemicals: alternative paths to mono- and di-methylnaphthalenes

Güleç,

Koçkan,

Karaduman

2023

Res Chem Intermed

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This study investigates the utilisation of coal tar naphthalene oil fraction (CTNOF), an economical by-product derived from the iron-steel industry, for the production of valuable chemicals, with a particular focus on methylnaphthalenes (MNs) and dimethylnaphthalenes (DMNs). Of specific interest is 2,6-dimethylnaphthalene (2,6-DMN), a pivotal component in the manufacture of polyethylene naphthalate (PEN). The intricate and costly nature of 2,6-DMN production currently poses challenges to the commercial viability of PEN. This study provides the potential heterogeneous reaction pathways for the synthesis of MNs and DMNs via methylation, disproportionation, and isomerisation of CTNOF. The utilisation of CTNOF was investigated in a laboratory-scale fixed bed reactor operating at atmospheric pressure using a mixture of CTNOF: methanol having 1:5 mass ratio over HBeta zeolite catalyst at a temperature of 400 °C and weight hourly space velocity of 2 h−1. The results reveal the successful methylation of CTNOF over the HBeta zeolite catalyst, initially achieving high naphthalene conversion, particularly into 2-MN. This highlights the potential of CTNOF as an alternative feedstock for the production of value-added chemicals. While naphthalene conversion initially reaches 99 wt% within 0.5 h of operation, it gradually decreases to approximately 10 wt% over extended run times. Notably, coke deposition significantly deactivates the HBeta zeolite catalyst during CTNOF methylation, impacting naphthalene conversion. A substantial proportion of naphthalene compounds convert to methylnaphthalenes early in the reaction, predominantly 2-MN, increasing from 14 wt% (in CTNOF feedstock) to 87 wt%. Among DMNs, selectivity for 2,6-DMN, 2,7-DMN, 1,3-DMN, and 1,7-DMN increases, while other DMN isomers exhibit a sharp decrease in selectivity. The distribution of 2,6-triad DMNs rises from 38 wt% in feedstocks to 52–55 wt% with extended reaction times, attributed to MN conversion to DMNs and potential isomerisation from other DMNs. This study underscores the feasibility of using CTNOF for the direct synthesis of valuable chemicals, specifically 2,6-DMN and 2-MN, through methylation over HBeta zeolite catalysts. However, it emphasises the critical role of residence time in coke deposition and the need for optimisation, particularly regarding this parameter, to ensure the efficiency of this catalytic process.

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Application of Kalman filter to determination of coal liquefaction mechanisms using discrete time models

Cited by 12 publications

References 36 publications

Application of Adaptive Kalman Filter in Online Monitoring of Mine Wind Speed

Application of Adaptive Kalman Filter in Online Monitoring of Mine Wind Speed

Prediction of Biomass Pyrolysis Mechanisms and Kinetics: Application of the Kalman Filter

Utilisation of coal tar naphthalene oil fractions for the synthesis of value-added chemicals: alternative paths to mono- and di-methylnaphthalenes

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