A Comparative Study of Data-Driven Modeling Methods for Soft-Sensing in Underground Coal Gasification

Kačur, Ján; Durdán, Milan; Laciak, Marek; Flegner, Patrik

doi:10.14311/ap.2019.59.0322

Cited by 7 publications

(10 citation statements)

References 37 publications

(55 reference statements)

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“…This model-free controller solely relies on the output measurements. The authors extended their research work to include the effect of uncertainties in the measurements in [8]. Later, in [9], an experimental study is investigated to solve a real-time optimization problem for the UCG process.…”

Section: Related Workmentioning

confidence: 99%

Chattering Free Sliding Mode Control and State Dependent Kalman Filter Design for Underground Gasification Energy Conversion Process

et al. 2023

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The fluctuations in the heating value of an underground coal gasification (UCG) process limit its application in electricity generation, where a desired composition of the combustible gases is required to operate gas turbines efficiently. This shortcoming can be addressed by designing a robust control scheme for the process. In the current research work, a model-based, chattering-free sliding mode control (CFSMC) algorithm is developed to maintain a desired heating value trajectory of the syngas mixture. Besides robustness, CFSMC yields reduced chattering due to continuous control law, and the tracking error also converges in finite time. To estimate the unmeasurable states required for the controller synthesis, a state-dependent Kalman filter (SDKF) based on the quasi-linear decomposition of the nonlinear model is employed. The simulation results demonstrate that despite the external disturbance and measurement noise, the control methodology yields good tracking performance. A comparative analysis is also made between CFSMC, a conventional SMC, and an already designed dynamic integral SMC (DISMC), which shows that CFSMC yields 71.2% and 69.9% improvement in the root mean squared tracking error with respect to SMC and DISMC, respectively. Moreover, CFSMC consumes 97% and 23.2% less control energy as compared to SMC and DISMC, respectively.

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Section: Related Workmentioning

confidence: 99%

Chattering Free Sliding Mode Control and State Dependent Kalman Filter Design for Underground Gasification Energy Conversion Process

et al. 2023

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“…It is expounded that the measurements of the process variables are considered under the conditions of uncertainties, cf. [16]. Therefore, designed control system should be robust enough to handle these uncertainties and continuously adapt the changes taking place in the process, cf.…”

Section: A Related Workmentioning

confidence: 99%

“…The effect of future control inputs on the syngas calorific value to support a predictive control is also analyzed using data-driven design approaches, such as multivariate adaptive regression splines (MARS), cf. [16]. In [20], a model-free optimal control of UCG has been established for continuous optimization of operating variables which results in increased production of syngas.…”

Section: A Related Workmentioning

confidence: 99%

“…Moreover, ∆U in the given cost function ( 19) also has the process design constraints pertaining to the UCG process, which are of the form (15) as discussed earlier. Thus, the optimization problem of the MPC can be transformed as to find the optimal control parameter vector (∆U ) that satisfies the constraints of the form (15) while minimizing the cost function given in (16).…”

Section: B Mpc Problem Formulation For Ucgmentioning

confidence: 99%

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Model Predictive Control and Adaptive Kalman Filter Design for an Underground Coal Gasification Process

Chaudhry¹,

Uppal

Bram

2021

IEEE Access

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The control of a nonlinear system such as an underground coal gasification (UCG) process is a challenging task. Several nonlinear design approaches are implemented to improve the tracking performance of the UCG process, however, the nonlinear techniques make implementation complex and computationally inefficient. In this work, a constrained linear model predictive control (MPC) is designed for the UCG process to track the desired trajectory of the heating value, while satisfying actuator constraints pertaining to UCG. The unknown states required for MPC design are reconstructed by using linear adaptive Kalman filter (AKF) and unscented Kalman filter (UKF). The design of MPC and AKF is based on the quasi-linear model of UCG process. A fair comparison is conducted between different control strategies, which include MPC-AKF, MPC-UKF, MPC-gain scheduled modified Utkin observer (GSMUO) and dynamic integral sliding mode control (DISMC)-GSMUO. The quantitative analysis and simulation results show that MPC-AKF outperforms its counterparts by yielding the least tracking error and average control energy. This conclusion holds, even in the presence of an external disturbance, parametric variations, and measurement and process noises. Moreover, MPC-AKF yields 51%, 44% and 46% improvement in absolute relative rootmean-squared error with reference to MPC-UKF, MPC-GSMUO and DISMC-GSMUO, respectively. A quantitative analysis has also been carried for AKF and UKF, which shows that the performance of AKF is more robust against the changes in the initial values of measurement and process covariances.INDEX TERMS Model predictive control (MPC); underground coal gasification (UCG); adaptive Kalman filter (AKF); unscented Kalman filter (UKF); energy conversion systems.

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“…The product gas composition depends on the coal geology and gasification parameters. The diversity of the conditions, factors, and structure of individual coal seams hampers the transfer of the UCG process knowledge [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Material Balance From the Experimental Ucg

et al. 2020

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The underground coal gasification is a continually evolving technology, which converts coal to calorific gas. There are many important parameters in this technology, which are difficult to measure. These parameters include the underground cavity growth, amount gasified coal, and the leakage of input and output gaseous components into the surrounding layers during the coal gasification process. Mathematical modeling of this process is one of the possible alternatives for determining these unknown parameters. In this paper, the structure of the mathematical model of laboratory underground coal gasification process from the material balance aspect is presented. The material balance consists of mass components entering and leaving from the UCG process. The paper shows a material balance in the form of a general mass balance and atomic species balance. The material balance was testing by six UCG laboratory experiments, which were realized in two ex-situ reactors.

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A Comparative Study of Data-Driven Modeling Methods for Soft-Sensing in Underground Coal Gasification

Cited by 7 publications

References 37 publications

Chattering Free Sliding Mode Control and State Dependent Kalman Filter Design for Underground Gasification Energy Conversion Process

Chattering Free Sliding Mode Control and State Dependent Kalman Filter Design for Underground Gasification Energy Conversion Process

Model Predictive Control and Adaptive Kalman Filter Design for an Underground Coal Gasification Process

Modeling of Material Balance From the Experimental Ucg

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