Process Modeling And Operation Optimization Methods For Coal Water Slurry Gasification Unit

Coal is China's basic energy and one of the main industrial raw materials. Over four hundred million tons of coal is used by chemical industry each year. Gasification is one of the most important methods and key technology of coal for clean use. Currently, coal water slurry (CWS) gasification technology is a representative technology of coal gasification.As the headstream and energy conversion core, the running status of gasification unit is very important. Nowadays, researches on process modeling and operation optimization for CWS gasification unit using advanced control methods are still at the initial stage. It is of great significance for improving the unit operation efficiency and enhancing coal resource utilization ratio to speed up the researches on coal gasification automation technology, such as process modeling, process simulation, and optimization. In this dissertation, for CWS gasification process, complex chemical process intelligent modeling approaches and optimization algorithms are studied. Based on these methods, several improved intelligent optimization algorithms are proposed and used in coal gasification process modeling and operation optimization problem. Meanwhile, the CWS gasification operation optimization system software is designed and developed. The main results in this dissertation can be summarized as follows:(1) It appears that the coal-blending optimization is a vital part of the optimal operation of coal gasification process. For this problem, a series of mixed coal quality target prediction models are constructed. One of them is mixed coal ash fusion temperature prediction model, which is established based on least squares support vector machine (LS-SVM) to describe the complex nonlinear relationship between ash fusion temperature and ash components. Meanwhile, a multi-population competitive co-evolutionary cultural differential evolution (MCCDE) algorithm is proposed. In MCCDE, a competitive co-evolutionary strategy based on differential evolution and a fitness value evaluation method are designed. And some ideas from cultural algorithm are also introduced into MCCDE. Five differential evolution algorithms with different mutation strategy and eight typical benchmark functions are adopted to verify the performance of MCCDE algorithm. Finally, MCCDE algorithm is used to optimize the hyper-parameters of LS-SVM. The simulation results indicate that the model based on MCCDE-LS-SVM has stronger generalization.(2) Combined with the mixed coal quality target prediction models established in (1), a process model from a management and decision-making perspective is constructed to solve the problem of CWS gasification coal-blending optimization. The model takes into account mixed-coal indicators, inventory costs, market prices, operating costs and consumptions of stockpiling and transit. According to the process model, a co-evolutionary mechanism between two cultural algorithms is established, and a hybrid co-evolutionary cultural algorithm based on particle swarm optimization (CECBPSO) is proposed in order to fully use the advantages of co-evolutionary algorithm (CEA), cultural algorithm (CA) and particle swarm optimization (PSO). Factorial Design (FD) approach is used in this paper in order to get a guideline on how to tune the designed parameters in CECBPSO. Meanwhile, extensive computational studies are also carried out to evaluate the performance of CECBPSO on eight benchmark functions, compared with other four intelligent optimization algorithms. Finally, CECBPSO algorithm is employed to solve the problem of coal-blending optimization process model of a fertilizer. The calculation results validate the feasibility of the coal-blending optimization model and CECBPSO algorithm.(3) The Group Search Optimization (GSO) algorithm has good performance in high-dimension multi-modal optimization problems. In this paper, differential evolution and chaotic local optimizer are introduced into basic GSO algorithm, and an improved Differential Evolution Based Group Search Optimization (DEGSO) algorithm is proposed. Compared with other four evolutionary algorithms on function optimization problems, the performances of DEGSO are satisfactory. In addition, DEGSO algorithm is applied to optimize the weights and thresholds of neural network, and three DEGSO-NN based soft sensor models are established for estimating the percentage compositions of CO, H2 and CO2 of Texaco gasifier syngas. The simulation results indicate that soft sensor modeling method based on DEGSO-NN has higher training efficiency and stronger generalization than the other two compared methods.(4) Membrane computing is a new branch of natural computing with the features of distribution and great parallelism. Considering the features of membrane computing and PSO, a hybrid algorithm MCBPSO is proposed. In MCBPSO, PSO is introduced into the computing model of membrane system. Multi-populations iterate in different membrane structures in parallel. Meanwhile, cooperation and mutation strategy are also established in the hybrid algorithm to improve the performance. Influencing rules of parameter selection of MCBPSO are studied through orthogonal design experiments. Also, the performance of MCBPSO is evaluated by five test functions. For Texaco coal gasification operation optimization problem, the concept of regional optimization is introduced, running status evaluating standard is designed, and operation optimization model is constructed. MCBPSO algorithm is used to solve the problem of operation optimization model. Finally, simulations with a Texaco gasification unit for example testified that optimized operation variables can be found and the effective gas rate can be increased by the optimization model and algorithm.(5) For a real-world Texaco CWS gasification process of a methanol synthesis system, the CWS gasification operation optimization system software is designed and developed. The software provides functions of mixed-coal qualities prediction, coal-blending optimization, soft sensing of furnace box temperature and syngas percentage compositions, gasifier running status optimization. Process models can be updated automatically. Record reports also can be created and stored periodically. Modeling, control and optimization technologies can be used in practical production to gain more economic benefits through the use of the CWS gasification operation optimization system software.