Model based DRPE large scale system

• Main Authors: Ying-Yu Chuang, 莊英譽
• Other Authors: Junghui (Jason) Chen
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/55635223743354815984

碩士 === 中原大學 === 化學工程研究所 === 103 === Data reconciliation and parameter estimation (DRPE) are important tasks for improving the performance of the process design and analysis. They provide reliable, reconciled values of measurements and estimated values of parameters to make sure that the identified process models can accurately describe the behavior of the industrial process. Since the difficulty of solving nonlinear DRPE optimization problems increases significantly with the growing number of variables and the multi-operating conditions, particularly in a large-scale process system, solving many smaller DRPE sub-problems iteratively can be more efficient than solving the whole large scale DRPE problem. With the features of parallel processing technology, a novel approach is proposed to find the optimal distributed DRPE sub-problems in this research work. To solve the optimal decomposition optimization sub-problems, the clustering based logical equation set decomposition (CLESD) is developed to reduce the sizes of sub-DRPE problems and to minimize loss information of the original large DRPE problem. The decomposition decomposes a large DRPE problem in two stages, including the variable clustering decomposition as well as the equation clustering decomposition. Also, a proper definition of a decomposition index and an efficient analysis algorithm are required. For the optimization problems with multi-operating conditions, a methodology with the combination of similar operating conditions, including the steady state detection and the clustering of multi-operating conditions, is proposed to construct a smaller DRPE problem. Further, the initial guesses influence the result of optimization problems. An efficient method, just-in-time learning-based DRPE (JITL-DRPE), is proposed to improve the efficiency of computing. The Bayesian approach is used to construct the similarity factor for finding appropriate initial guesses. Finally, we compare the traditional DRPE method with the proposed DRPE method through three industrial applications. The results show that the proposed method outperforms the traditional DRPE method in terms of the solution time and the number of iterations.