Abstract:This paper presents a planned heating control strategy applied for a natural gas thermal desorption system for polluted soil to achieve the dynamic adjustment of the heating time and energy consumption. A lumped-parameter model for the proposed system is established to examine effects of the natural gas mass flow rate and the excess air coefficient on the heating performance of the target soil. The control strategy is explored to accomplish the heating process as expected with constant temperature change rate … Show more
“… Fig. 8 Schematic diagram of the thermal desorption system [Reprinted with permission from the reference number [ 95 ] Xu H-J, Li Y-Z, Gao L-J, Zhang X. Planned Heating Control Strategy and Thermodynamic Modeling of a Natural Gas Thermal Desorption System for Contaminated Soil.…”
Section: Different Techniques Employed For the Removal Of Hmis Fromhm...mentioning
“… Fig. 8 Schematic diagram of the thermal desorption system [Reprinted with permission from the reference number [ 95 ] Xu H-J, Li Y-Z, Gao L-J, Zhang X. Planned Heating Control Strategy and Thermodynamic Modeling of a Natural Gas Thermal Desorption System for Contaminated Soil.…”
Section: Different Techniques Employed For the Removal Of Hmis Fromhm...mentioning
“…Since most of the control problems in industrial applications are established on the plant having a linear or nonlinear dynamic model for certain regimes, it is important to monitor whether the plant in question operates in the stated regime where the control algorithm is designed for. The scheduled control algorithms for example, require the regime to be monitored in certain intervals or in real time due to the fact that depending on the regime or the region in state-space, different control algorithms may be used [4]. Generally, large portion of the industrial applications are given in Partial Differential Equation (PDE) form and by discretizing the models, a more tractable Ordinary Differential Equation (ODE) representation is obtained.…”
In this study, a GLSDC (Gaussian Least Squares Differential Correction) based parameter estimation algorithm is used to identify a PMSM (Permanent Magnet Synchronous Motor) model. In this method, a nonlinear model is assumed to be the correct representation of the underlying state dynamics and the output signals are assumed to be measured in a noisy environment. Using noisy input and output signals, parameters that constitute the coefficients of the nonlinear state and input signal terms are to be estimated using the state transition matrix which is computed by the numerical means that are detailed. Since a GLSDC algorithm requires correct initial state value, this term is also estimated in addition to the unknown coefficients whose bounds are assumed to be known, which is mostly the case in the industrial applications. The batch input and output signals are used to iteratively estimate the parameter set before and after the convergence, and to recover the filtered state trajectories. A couple of different scenarios are tested by means of numerical simulations and the results are addressed. Different methods are discussed to compute better initial estimate values, to shorten the convergence time.
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