Modeling and Control of Underground Coal Gasification

Abstract

Pakistan is going through an acute energy crisis despite being blessed by huge energy potential. Pakistan has approximately 185 billion tonnes of coal, of which 175 billion tonnes of Lignite B is located in Thar. The most suitable technology to harness the potential of the Thar coal reservoirs is the underground coal gasification (UCG), which involves the underground conversion of coal in to synthetic gas that can be used in numerous industrial applications. Therefore, the planning commission of Pakistan allocated the Block V of Thar coal field to UCG project Thar, in order to setup a pilot project. This research work deals with the modeling and control of Thar coal gasifier. In this research work a computer model is developed for the underground gasification of Block V of the Thar coal field. The numerical solution of the model is carried out by incorporating a pseudo steady state approximation, which replaces gas phase PDEs with ODEs with respect to the length of the reactor. This approximation assumes that the concentration of the gases attain steady steady before any significant change occurs in the densities of coal and char. The PDEs for the densities of coal and char and solid temperature are solved by finite difference method, while the gas phase ODEs are simultaneously solved as a boundary value problem, marching from inlet to outlet. The simulation results show that the solution of the model is capable of providing space and time profiles for different physical quantities, such as, coal and char densities, concentration and molar fractions of different gases, rate of different chemical reactions and solid and gas temperatures. A detailed parametric study is also carried out for the model solution, which shows that the composition of the product gas is sensitive to various coal properties and operating conditions. The parametrization of a complex process like UCG is a formidable job, which includes a large number of physical and chemical properties of coal, different operating conditions and various in situ phenomena. In order to determine the composition of coal and char, the ultimate analysis of their samples is carried out. The results of the ultimate analysis are prone to uncertainty, because the measurements are obtained from different coal samples, which go through different handling procedures before they are analyzed. Therefore, in order to cater for the uncertainty in the results of the ultimate analysis two different nonlinear programing problems are formulated, which aim to minimize the square of the relative L2 norm error between experimental and simulated heating values. The field trial of UCG is carried out by UCG project Thar, which involves the gasification of a single coal seam. The heating value is calculated by the measurements of the molar fraction of different gases provided by the gas analyzer. After optimization, the results of the solved model are compared with the experimental data, which show a good match between experimental and simulated heating values. In order to increase the efficiency of the UCG process, a SMC is designed which maintains a desired constant heating value over a longer period of time. In order to synthesize the controller analytically, a control oriented model of the process is developed which bears certain assumptions. The SMC is considered for the process as it offers robustness against parametric variations and external disturbances. As the relative degree of the sliding variable is zero, so the trivial solution is to derive an expression for the control input algebraically, but this strategy is not feasible as the right hand side of the control input equation depends upon the unmeasured states. Therefore, the conventional SMC is implemented by adding an exogenous input, which is the derivative of the actual control signal. By doing so the relative degree of the sliding variable becomes one with respect to the exogenous input and then SMC is enforced by selecting a suitable value of the discontinuous gain. The synthesized controller is then implemented on the actual model of the UCG process. The simulation results show that despite the modeling uncertainties and external disturbance the controller keeps the heating value at the desired level.