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Research on Modeling and Energyr Conservation of Four-Tower Rectification under Varying Load Condition

Author: SunDaJun
Tutor: ZhaoXia
School: Shanghai Jiaotong University
Course: Control Theory and Control Engineering
Keywords: four-tower rectification variable load energy conservation neural network adaptive genetic algorithm
CLC: TQ223.121
Type: Master's thesis
Year: 2012
Downloads: 171
Quote: 0
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Abstract


Methanol is widely used in organic synthesis, coatings, medical, fuel industry, etc. As one of the most important products of coal chemical industry and alternative energy sources,it is very important in today’s global chemical market. Methanol is one of the main productions in the coal chemical industry methanol in China. Methanol distillation process has a significant impact on whole the methanol production process such as the production capacity, product quality, energy consumption, material consumption and environmental protection. Distillation process is highly energy intensive. Methanol distillation energy consumption accounts for about 20% of total methanol production energy consumption. With the issue of energy conservation in recent years taking more and more attention, it has become a hot topic of global concern. In methanol distillation process, due to fluctuations in upstream production conditions, the components in the feed changes a lot. And in recent years, with the fierce market competition, manufacturers often need to adjust the production load according to market, so the feed load changes. Therefore, for the operational four- towers methanol distillation system, when the feed conditions changes, especially at lower loads, it becomes an inevitable problem that how to determine the optimal operating parameters to ensure product quality and to reduce energy consumption at the same time. This paper researched on a solution which aims to find these optimizing manipulation parameters.This paper is limited in the research of the operational four- towers methanol distillation system with a solution of determining the optimal operating parameters to ensure product quality and to reduce energy consumption at the same time when the feed conditions changes. Energy optimization solution was discussed, and the system dimension was reduced by the dynamic programming principle. Then Aspen Plus and artificial neural network were used to simulate and analyze the system respectively. A modeling method applied to optimization was proposed which combines Aspen Plus and artificial neural network. Then adaptive genetic algorithm was used to find out the possible optimal operating parameters. The main contents of this paper are as follows:1. It proposes a solution to calculate the optimizing manipulation parameters of four-tower rectification system under varying load condition, which ensures product quality and reduces energy consumption at the same time. This quantitative and real-time calculation method is used in open-loop control of four-tower rectification system.2. It fully analyses the methanol distillation process, based on an actual a chemical plant. It proposes a method that reduces the modeling and optimization system dimensions based on dynamic programming principle, which greatly reduced the system’s analysis and computational complexity. Meanwhile, the analysis of the subsystems was done and the relevant control variables and controlled variable were determined. Then the practical problem was transformed into a mathematical optimization problem.3. It analyzed neural network modeling methods and feasibility. It studied on the chemical simulation software Aspen Plus modeling method, and used Aspen simulation results to analyze; on this basis of the analysis of the advantages and disadvantages in variable load optimization of the two modeling approaches, it proposes an approach which uses Aspen Plus to adopt a suitable experimental data and then train the BP neural network to get a rapid response model. To analyse its feasibility, the method was used in the chemical methanol distillation system.4. It introduces the adaptive genetic algorithm. Then the algorithm was used to find the optimal value of the operation after getting the rapid response model, and the result was analyzed.5. It gives the brief conclusion of the form work. And it brings up the ideas of further optimization.

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CLC: > Industrial Technology > Chemical Industry > Basic Organic Chemistry Industry > The production of aliphatic compounds ( acyclic compounds) > Aliphatic alcohols (alcohols, hydroxy compounds) and its derivatives > Aliphatic alcohol > Fatty Alcohols > Methanol ( wood alcohol )
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