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Analysis and Improvement of Computational Efficiency Based on Alumina Material Balance Calculation Software

Author: WangSen
Tutor: ZhangPu
School: Huazhong University of Science and Technology
Course: Detection Technology and Automation
Keywords: Material balance Computational efficiency Swarm intelligence optimization algorithm Particle Swarm
CLC: TP319
Type: Master's thesis
Year: 2009
Downloads: 26
Quote: 0
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Abstract


Material balance calculation is the core of the alumina process design. With the rapid development of modern science and technology, there is software to simulate it. Computational efficiency is one of the most important indicators of the alumina material balance calculation software. The Efficient calculation not only presents the high quality of the software, but also can save a lot of time in software development and maintenance.First of all, this thesis gives a brief introduction to the software design and implementation. Using hybrid programming skills to integrate the MATLAB computing model and VC application platform together, including Bayer process and Sinter process.Secondly, test two processes respectively based on the software. Compare the results of the MATLAB code and the integrated software. Analyze the computational efficiency and to search for the causes of inefficiency. Give and demonstrate the improvement schemes from two angles, which are software implements and algorithms, to increase the efficiency. Point out that the improvements to the algorithm are the main methods. Then abstract the material balance calculation model into a nonlinear programming problem. According to the characteristics of the model, choose the latter ones for research in the traditional nonlinear optimization algorithms and swarm intelligence optimization algorithms. Introduce fish-swarm algorithm and genetic algorithm as the two new algorithms. Make further improvement for the original particle swarm optimization to increase the speed, which is the convergence factor.Finally, finish the programming of each of these three improvement schemes. Choose the appropriate parameters and test the material balance calculation models individually. The results show that fish-swarm algorithm and particle swarm optimization with convergence factor can improve the efficiency, and the latter even can do nearly 70%. Therefore, using particle swarm optimization with convergence factor is a major discovery.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Specific applications
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