A mild gasification method has been developed to provide an innovative clean coal technology. The objectives of this study are to (a) incorporate a fixed rate devolatilization model into the existing 2D multiphase reaction model, (b) expand the 2D model to 3D and (c) utilize the improved model to investigate the mild-gasification process and guide modification of the mild-gasifier design. The Eulerain-Eulerian method is employed to calculate both the primary phase (air) and secondary phase (coal particles). The improved 3D simulation model, incorporated with a devolatilization model, has been successfully developed and employed to determine the appropriate draft tube dimensions, entrained flow residence time, The simulations also help determine the appropriate operating fluidization velocity range to sustain the fluidized bed depth without depleting the chars or blowing the char away. The results are informative, but require future experimental data for verification.

Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE. / Vita. / Bibliography: leaves 594-611. / Sc.D.

Mathematical models were developed to understand cavity growth mechanisms, heat and mass transfer in combination with chemical reaction, and the factors which affect gas production from an underground coal gasifier. A model for coal gasification in a one-dimensional spatial domain was developed and validated through comparison with experimental measurements of the pyrolysis of large coal particles and cylindrical coal blocks. The effects of changes in operating conditions and coal properties on cavity growth were quantified. It was found that the operating conditions which have the greatest impact on cavity growth are: temperature, water influx, pressure and gas composition, while the coal properties which have the greatest impact are: the thermo-mechanical behaviour of the coal, the coal composition and the thickness of the ash layer. Comparison of the model results with estimates from field scale trials, indicate that the model predicts growth rates with magnitudes comparable to those observed. Model results with respect to the effect of ash content, water influx and pressure are in agreement with trends observed in field trials. A computational fluid dynamics model for simulating the combined transport phenomena and chemical reaction in an underground coal gasification cavity has been developed. Simulations of a two-dimensional axi-symmetric cavity partially filled with an inert ash bed have shown that when the oxidant is injected from the bottom of the cavity, the fluid flow in the void space is dominated by a single buoyancy force due to temperature gradients established by the combustion of volatiles produced from the gasification of carbon at the cavity walls. Simulations in which the oxidant was injected from the top of the cavity reveal a weak fluid circulation due to the absence of strong buoyancy forces, leading to poor gasification performance. A channel model of gas production from underground coal gasification was developed, which incorporates a zero-dimensional cavity growth model and mass transfer due to natural convection. A model sensitivity study is presented and model simulations elucidate the effects of operating conditions and coal properties on gas production.

Includes an addendum. / Bibliography : leaves 217-233. / iii, 284 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Chemical Engineering, 1994

Thesis (Ph. D.)--Brigham Young University. Dept. of Chemical Engineering, 2006. / Includes bibliographical references (p. 141-147).

Although nonlinear model predictive control (NMPC) might be the best choice for a nonlinear plant, it is still not widely used. This is mainly due to the computational burden associated with solving online a set of nonlinear differential equations and a nonlinear dynamic optimization problem in real time. This thesis is concerned with strategies aimed at reducing the computational burden involved in different stages of the NMPC such as optimization problem, state estimation, and nonlinear model identification. A major part of the computational burden comes from function and derivative evaluations required in different parts of the NMPC algorithm. In this work, the problem is tackled using a recently introduced efficient tool, the automatic differentiation (AD). Using the AD tool, a function is evaluated together with all its partial derivative from the code defining the function with machine accuracy. A new NMPC algorithm based on nonlinear least square optimization is proposed. In a first–order method, the sensitivity equations are integrated using a linear formula while the AD tool is applied to get their values accurately. For higher order approximations, more terms of the Taylor expansion are used in the integration for which the AD is effectively used. As a result, the gradient of the cost function against control moves is accurately obtained so that the online nonlinear optimization can be efficiently solved. In many real control cases, the states are not measured and have to be estimated for each instance when a solution of the model equations is needed. A nonlinear extended version of the Kalman filter (EKF) is added to the NMPC algorithm for this purpose. The AD tool is used to calculate the required derivatives in the local linearization step of the filter automatically and accurately. Offset is another problem faced in NMPC. A new nonlinear integration is devised for this case to eliminate the offset from the output response. In this method, an integrated disturbance model is added to the process model input or output to correct the plant/model mismatch. The time response of the controller is also improved as a by–product. The proposed NMPC algorithm has been applied to an evaporation process and a two continuous stirred tank reactor (two–CSTR) process with satisfactory results to cope with large setpoint changes, unmeasured severe disturbances, and process/model mismatches. When the process equations are not known (black–box) or when these are too complicated to be used in the controller, modelling is needed to create an internal model for the controller. In this thesis, a continuous time recurrent neural network (CTRNN) in a state–space form is developed to be used in NMPC context. An efficient training algorithm for the proposed network is developed using AD tool. By automatically generating Taylor coefficients, the algorithm not only solves the differentiation equations of the network but also produces the sensitivity for the training problem. The same approach is also used to solve online the optimization problem of the NMPC. The proposed CTRNN and the predictive controller were tested on an evaporator and two–CSTR case studies. A comparison with other approaches shows that the new algorithm can considerably reduce network training time and improve solution accuracy. For a third case study, the ALSTOM gasifier, a NMPC via linearization algorithm is implemented to control the system. In this work a nonlinear state–space class Wiener model is used to identify the black–box model of the gasifier. A linear model of the plant at zero–load is adopted as a base model for prediction. Then, a feedforward neural network is created as the static gain for a particular output channel, fuel gas pressure, to compensate its strong nonlinear behavior observed in open–loop simulations. By linearizing the neural network at each sampling time, the static nonlinear gain provides certain adaptation to the linear base model. The AD tool is used here to linearize the neural network efficiently. Noticeable performance improvement is observed when compared with pure linear MPC. The controller was able to pass all tests specified in the benchmark problem at all load conditions.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Trademark symbol follows Chemchar in title. Typescript. Vita. Includes bibliographical references (leaf 130). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Trademark symbol follows Chemchar in title. Typescript. Vita. Includes bibliographical references (leaf 130). Also available on the Internet.