Estimation of vinyl acetate monomer concentration using neural network & partial least square - Mohamad Zafarudin, Mohamad (2008)
This thesis is about the application of Artificial Neural Network (ANN) and Partial Least Square (PLS) on the chemical process plant. At the present time, the process and development in chemical plants are getting more complex and hard to measure. Therefore, the needs for a system that can help to supervise and control the process in the plant have to be accomplished in order to achieve higher performance and quality. As the emergence of Artificial Neural Network and Partial Least Square application nowadays to solve problem in various fields had given a great significant effect such as soft-sensor, lack of on-line measurement, and incorporate the safety issues while maintaining practicality and economic feasibility, both of the system are reliable to be adapted in the chemical plant. Furthermore, this thesis will be focusing more on the application of Artificial Neural Network and Partial Least Square as a estimation scheme in the chemical plant. Estimation by using Artificial Neural Network (ANN) and Partial Least Square (PLS) is popular in the present time as a mechanism to estimate the variables in the chemical plant. By implementing such system, the performance and quality of the plant will increased. For this thesis, the vinyl acetate monomer plant had been chosen as the case study to provide the necessary data and information to run the research. Vinyl acetate monomer process will provides a dependable source of data and an appropriate test for alternative control and optimization strategies for continuous chemical processes.